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WO2025091079A1 - Diagnostic signature - Google Patents

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Publication number
WO2025091079A1
WO2025091079A1 PCT/AU2024/051156 AU2024051156W WO2025091079A1 WO 2025091079 A1 WO2025091079 A1 WO 2025091079A1 AU 2024051156 W AU2024051156 W AU 2024051156W WO 2025091079 A1 WO2025091079 A1 WO 2025091079A1
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WO
WIPO (PCT)
Prior art keywords
lpc
cer
lipid biomarkers
lipid
variant
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
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PCT/AU2024/051156
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French (fr)
Inventor
David Peake
Amani BATARSEH
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Bcal Diagnostics Ltd
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Bcal Diagnostics Ltd
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Filing date
Publication date
Priority claimed from AU2023903491A external-priority patent/AU2023903491A0/en
Application filed by Bcal Diagnostics Ltd filed Critical Bcal Diagnostics Ltd
Publication of WO2025091079A1 publication Critical patent/WO2025091079A1/en
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57415—Specifically defined cancers of breast
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P35/00—Antineoplastic agents
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/92—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving lipids, e.g. cholesterol, lipoproteins, or their receptors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2405/00—Assays, e.g. immunoassays or enzyme assays, involving lipids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2570/00—Omics, e.g. proteomics, glycomics or lipidomics; Methods of analysis focusing on the entire complement of classes of biological molecules or subsets thereof, i.e. focusing on proteomes, glycomes or lipidomes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/70—Mechanisms involved in disease identification
    • G01N2800/7023—(Hyper)proliferation
    • G01N2800/7028—Cancer

Definitions

  • the key to surviving breast cancer is early detection and treatment.
  • the current gold standard for detection is via mammogram however, it is known to be less effective at younger ages. Accordingly, there remains a need for a more accurate screening test for breast cancer for women of all ages, such as to detect the cancer at a cellular level and before metastasis (Mistry and French, 2016).
  • Summary The present disclosure is based on the surprising discovery of a number of lipid biomarkers, which can readily be detected, such as in a liquid biopsy, in order to diagnose breast cancer in women, as well as rule out a woman having breast cancer. By extension, these lipid biomarkers (or lipidomic signatures) demonstrate promise in identifying patients that require treatment for breast cancer.
  • the present disclosure provides a method of diagnosing a subject with a breast cancer, said method including the step of measuring a level of one or more lipid biomarkers in a biological sample from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)),
  • an increased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4) isomer), PC(38:5), PI(40:5), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3) and/or SM(d42:4), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18
  • the measuring step includes determining the presence or absence of: (i) an increased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)), PC(38:5), PI(40:5), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3) and/or SM(d42:4), or a fragment, variant or derivative thereof, and/or (ii) a decreased level of AcCa(18:2), LPA(18:0), L
  • the present disclosure relates to a method of treating a breast cancer in a subject, said method including the step of performing a treatment in respect of the subject in which a level of one or more lipid biomarkers has been measured in a biological sample therefrom that is diagnostic or indicative of the subject having the breast cancer, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (
  • the treatment includes administering a therapeutically effective amount of an anti- cancer treatment to the subject.
  • an increased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) e.g., PC(16:0/20:4)
  • PC(38:5) PI(40:5)
  • the one or more lipid biomarkers comprise LPC(14:0), or a fragment, variant or derivative thereof, and one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE
  • the one or more other lipid biomarkers may be selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • AcCa(18:2) AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(16:0e
  • the one or more other lipid biomarkers may be selected from the group consisting of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and SM(d36:2), or a fragment, variant or derivative thereof.
  • Cer(d36:1) Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and
  • the one or more other lipid biomarkers may be selected from the group consisting of Cer(d36:1), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)) and SM(d36:2), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers suitably comprise PI(38:6), or a fragment, variant or derivative thereof, and one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:
  • the one or more lipid biomarkers can comprise LPC(14:0) and PI(38:6), or a fragment, variant or derivative thereof, and optionally one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34
  • the one or more lipid biomarkers are suitably selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2), TG(50:1e), TG(52:3e), TG(56:0), TG(56:1), TG(58:2) and TG(60:5), or a fragment, variant or derivative thereof
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) and TG(52:3e), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2), TG(52:3e) and TG(56:1), or a fragment, variant or derivative thereof.
  • LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2), PC(36:4) e.g., PC(18:2/18:2) and/or PC(16:0/20:4)
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(44:2), TG(52:3e) and TG(58:2), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d37:1), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), TG(51:0), TG(52:3e), TG(53:0), TG(58:1), TG(58:2) and TG(59:1)
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d39:2), SM(d41:2), TG(52:3e), TG(53:0), TG(58:1) and TG(58:2), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(18:3), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2p), PE(38:6e), PE(40:6e), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), SM(d38:4) and SM(d42:4), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(38:5), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers can comprise, consist of or consist essentially of: (a) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1); (b) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); (c) Cer(d36:1), LPA(18:0), LPC(14:0), LPC(16:0),
  • the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • AcCa(18:2) AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC
  • the one or more lipid biomarkers may be selected from the group consisting of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and SM(d36:2), or a fragment, variant or derivative thereof.
  • Cer(d36:1) Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16
  • the one or more lipid biomarkers may be selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)) and SM(d36:2), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers may be selected from the group consisting of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers may be selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1); or a fragment, variant or derivative thereof.
  • the level of the one or more lipid biomarkers suitably is or has been measured, at least in part, by mass spectrometry.
  • the predictive accuracy thereof, as determined by an ROC AUC value is at least about 0.65, at least about 0.70, at least about 0.75 or at least about 0.80.
  • the present disclosure provides a system for determining the presence or absence of a breast cancer in a subject, the system comprising: a mass spectrometry unit configured for determining a level of one or more lipid biomarkers in a biological sample obtained from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2), PC(d
  • the present disclosure provides a kit for determining the presence or absence of a breast cancer in a subject, the kit comprising one or more reagents for determining a level of one or more lipid biomarkers in a biological sample obtained from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:
  • the one or more reagents comprise one or more probes, each probe being specific or selective for one of the one or more lipid biomarkers.
  • the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36(36:2), PC(36
  • the one or more lipid biomarkers of the above aspects suitably comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2), TG(50:1e), TG(52:3e), TG(
  • the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), LPC(16:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) and TG(52:3e), or a fragment, variant or derivative thereof.
  • LPC(14:0), LPC(16:0), PC(36:2), PC(36:4) e.g., PC(18:2/18:2) and/or
  • the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2), TG(52:3e) and TG(56:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), LPC(16:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(44:2), TG(52:3e) and TG(58:2), or a fragment, variant or derivative thereof.
  • LPC(14:0), LPC(16:0), PC(36:2), PC(36:4) e
  • the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d37:1), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), TG(51:0), TG(
  • the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d39:2), SM(d41:2), TG(52:3e), TG(53:0), TG(58:1) and TG(58:2), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), LPC(18:3), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2p), PE(38:6e), PE(40:6e), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), SM(d38:4) and SM(d42:4), or a fragment, variant or derivative thereof.
  • LPC(14:0), LPC(18:3), PC(36:4) e.g., PC(18:2/18:2) and/or PC(16:0/20:4)
  • PE(36:2p) PE(38:6e)
  • the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(38:5), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of: (a) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1); (b) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d36:2) and
  • the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • AcCa(18:2) Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d
  • the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and SM(d36:2), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)) and SM(d36:2), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the biological sample of the above aspects is or comprises a blood sample, a plasma sample, a serum sample and/or an extracellular vesicle (EV) sample.
  • the system of the fourth aspect or the kit of the fifth aspect are suitable for use in the method of the first, second or third aspects.
  • the following figures form part of the present specification and are included to further demonstrate certain aspects of the present disclosure. The disclosure may be better understood by reference to one or more of these figures in combination with the detailed description of specific embodiments presented herein. It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the above-described embodiments, without departing from the broad general scope of the present disclosure. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.
  • Figure 2. Plasma discovery phase; a) Average prediction of each model for individual patients across 2000 runs.
  • EV discovery phase a) Average prediction of each model for individual patients across 2000 runs. Purple represents that the sample is predicted as being a control in all runs, while Yellow represents consistent predictions of breast cancer across runs. The spectrum of colours between purple and yellow represents the proportion of inconsistencies across runs; b) Lipids that are consistently selected as being important by the Boruta algorithm across all runs. The top 20 lipids were selected as robust biomarkers and the final signature of the breast cancer diagnosis. Lipid identities (LIDs) are as per the Tables provided herein. Figure 6. EV performance metrics. a) Boxplots representing the distribution of different performance metrics (accuracy, F1-score, Precision or positive predictive value, sensitivity and specificity.
  • Lipid identities are as per the Tables provided herein.
  • accuracy accuracy
  • sensitivity TNR
  • TNR specificity
  • the bar plot shows the proportion of times that each lipid was identified as being relevant to the prediction. Blue bars indicate lipids that were not present in the lipid biomarker panel identified during SoW1 using the P250 dataset only, while red bars indicate lipids that were previously identified as being relevant to the prediction task.
  • c-d ROCs for each of the 200 iterations 80% LGOCV for the refined 15 lipid signature (c) and the original 20 lipid signature (d). The average and standard deviation of the AUC is reported in each plot.
  • Figure 11. Summary performance of homogenous models. For each cohort (AU-FED, EU, and P250), a separate ensemble model was trained. 200 iterations of 80% Leave-Group-Out Cross- Validation (LGOCV) were performed in order to understand the internal predictive performance of each model. Additionally, the refined 15-lipid signature (see Figure 10) was compared to the original 20-lipid signature.
  • LGOCV Leave-Group-Out Cross- Validation
  • ROCs shown are produced by aggregating predictions across all of the 200 iterations of 80% LGOCV.
  • TPR sensitivity
  • TNR specificity
  • Heatmaps presenting patient-level predictions for each patient group and disease status. The score indicates the prediction of the model, with values closer to 1 (yellow) predicting cancer while values closer to 0 (purple) predicting control.
  • the leftmost subplots provide boxplots of accuracy, sensitivity (true positive rate), and specificity (true negative rate), and the rightmost subplots provide ROCs for each iteration.
  • P450 P250 + EU cancer patient
  • P250 + EU cancer + AU control patients P450 + EU cancer patient
  • Figure 14.20-lipid signature identified using AG2-4.
  • LIDs Lipid identities
  • LID111 PI(38:4)
  • LID382 SM(d36:1)
  • LID271 LPE(22:6)
  • LID103 TG(54:4)
  • LID270 PC(30:0)
  • LID135 PE(O-38:5)
  • LID361 PE(O-38:4).
  • Stacked Plots Illustrating Prediction Confidence Levels Stacked plots represent the confidence levels of predictions, which are determined based on the range of prediction probabilities. Probabilities within the range of 0 to 1 were grouped into 6 equal bins. The top/bottom bins indicate high confidence, the second top/bottom bins indicate medium confidence, and the remaining middle bins centered around 0.5 represent low confidence. Within each confidence category, predictions are further stratified into true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). The plot above showcases confidence stack bars for Mix7, Mix0, and Mix5, aggregated across AGs 1-3 during (quasi-)external validations. Figure 25.
  • Detailed description General Techniques and Definitions Unless specifically defined otherwise, all technical and scientific terms used herein shall be taken to have the same meaning as commonly understood by one of ordinary skill in the art (e.g. in genomics, immunology, molecular biology, immunohistochemistry, biochemistry, oncology, and pharmacology). The present disclosure is performed using, unless otherwise indicated, conventional techniques of molecular biology, microbiology, recombinant DNA technology and immunology.
  • the disclosure also includes all of the steps, features, compositions and compounds referred to or indicated in this specification, individually or collectively, and any and all combinations of any two or more of said steps or features.
  • the present disclosure is not to be limited in scope by the specific embodiments described herein, which are intended for the purpose of exemplification only. Functionally equivalent products, compositions and methods are clearly within the scope of the disclosure, as described herein.
  • Each feature of any particular aspect or embodiment of the present disclosure may be applied mutatis mutandis to any other aspect or embodiment of the present disclosure.
  • reference to a single step, composition of matter, group of steps or group of compositions of matter shall be taken to encompass one and a plurality (i.e.
  • the present disclosure provides a method for measuring a level of one or more lipid biomarkers in a biological sample from a subject, said method including the steps of: (a) providing the biological sample; and (b) measuring the level of the one or more lipid biomarkers in the biological sample, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.
  • the present disclosure provides a method of diagnosing a subject with a breast cancer, said method including the step of measuring a level of one or more lipid biomarkers in a biological sample from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)
  • the term “subject” includes, but is not limited to, mammals, inclusive of humans, performance animals (such as horses, camels, greyhounds), livestock (such as cows, sheep, horses) and companion animals (such as cats and dogs).
  • the subject is a human.
  • the subject is a female human.
  • the subject is a male human.
  • cancer refers to diseases or conditions, or to cells or tissues associated with the diseases or conditions, characterized by aberrant or abnormal cell proliferation, differentiation and/or migration often accompanied by an aberrant or abnormal molecular phenotype that includes one or more genetic mutations or other genetic changes associated with oncogenesis, expression of tumour markers, loss of tumour suppressor expression or activity and/or aberrant or abnormal cell surface marker expression.
  • breast cancer refers to a condition characterized by an abnormally rapid growth of abnormal cells in one or both breasts of a subject.
  • Breast cancer can include, but is not limited to, ductal carcinoma in situ (DCIS), invasive breast cancer (e.g., an invasive carcinoma), inflammatory breast cancer, angiosarcoma of the breast, Phyllodes tumours of the breast, and/or Paget’s disease of the nipple.
  • DCIS ductal carcinoma in situ
  • invasive breast cancer e.g., an invasive carcinoma
  • inflammatory breast cancer e.g., angiosarcoma of the breast
  • Phyllodes tumours of the breast Phyllodes tumours of the breast
  • Paget s disease of the nipple.
  • invasive carcinoma or “invasive breast cancer” refers to a type of cancer that can include, but is not limited to, invasive ductal carcinoma (IDC), infiltrating ductal carcinoma, invasive lobular carcinoma (ILC), adenoid cystic (or adenocystic) carcinoma, low-grade adenosquamous carcinoma, medullary carcinoma, mucinous (or colloid) carcinoma, papillary carcinoma, tubular carcinoma, metaplastic carcinoma, micropapillary carcinoma, and/or mixed carcinoma having features of both invasive ductal and lobular.
  • IDC invasive ductal carcinoma
  • ILC invasive lobular carcinoma
  • adenoid cystic (or adenocystic) carcinoma low-grade adenosquamous carcinoma
  • medullary carcinoma or colloid carcinoma
  • papillary carcinoma tubular carcinoma
  • metaplastic carcinoma or micropapillary carcinoma
  • micropapillary carcinoma and/or mixed carcinoma having features of both invasive ductal and lobular
  • the breast cancer to be diagnosed in a subject is IDC.
  • the breast cancer to be diagnosed in a subject is DCIS.
  • the breast cancer to be diagnosed in a subject is ILC.
  • the breast cancer may include any aggressive breast cancers and cancer subtypes known in the art, such as triple negative breast cancer, lymph node positive (LN+) breast cancer, HER2 positive (HER2+) breast cancer, PR negative (PR-) breast cancer, PR positive (PR + ) breast cancer, ER negative (ER-) breast cancer and ER positive (ER+) breast cancer.
  • the breast cancer also may be of any stage or grade (e.g., Stages I, II, III or IV) and as such can include metastatic breast cancer.
  • diagnosis and “diagnosing” refer to a method by which one of ordinary skill in the art can assess and/or determine whether a patient or subject is suffering from a given disease or condition, such as determining the presence or absence of a breast cancer.
  • a given disease or condition such as determining the presence or absence of a breast cancer.
  • diagnostic indicators or markers whose presence, absence, or amount (relative or absolute) indicates the presence or absence of the disease, disorder or condition.
  • the methods used herein may be not only utilised to detect breast cancer in patients, but also or alternatively rule out the presence of breast cancer in a subject, such as after a negative primary diagnostic test (e.g., mammography).
  • diagnosis and “diagnosing” refer to an increased probability that a subject will have a certain disease, disorder or condition, such as a breast cancer.
  • the methods described herein are performed in conjunction (e.g., before and/or after) with one or more further diagnostic tests as are known in the art (e.g., breast exam; breast imaging, such as mammogram, ultrasound and MRI; biopsy).
  • the present method may be utilised as a preliminary screening test to identify subjects who may benefit from further diagnostic testing.
  • the present method may be utilised to confirm the presence or absence of breast cancer as indicated by a previous diagnostic test. Accordingly, in some examples, the present method may include the initial or earlier step and/or subsequent step of performing one or more further diagnostic tests on the subject in question. In alternative examples, the methods described herein are performed without any further diagnostic testing as a primary diagnostic test for breast cancer.
  • the methods herein include the steps of: (a) performing a diagnostic test to determine the presence or absence of a breast cancer in a subject; and (b) if the diagnostic test indicates the absence of the breast cancer in the subject, measuring a level of one or more lipid biomarkers in a biological sample from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36(36:2),
  • the one or more lipid biomarkers are selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1); or a fragment, variant or derivative thereof.
  • the level of the one or more biomarkers indicate or confirm the absence of the breast cancer in the subject.
  • the level of the one or more biomarkers indicate the presence of the breast cancer in the subject.
  • the subject may be subjected to further diagnostic testing, such as described herein.
  • the diagnostic test is suitably a breast imaging test, such as a mammogram or mammography.
  • the level, such as a concentration level or an expression level, of the one or more lipid biomarkers is altered or modulated in the biological sample from a subject, this can be diagnostic of breast cancer in the subject.
  • an increased level of expression or concentration of a first subset of the one or more lipid biomarkers and/or a decreased level of expression or concentration of a second subset of the one or more lipid biomarkers is diagnostic or indicative of the subject having the breast cancer.
  • the measuring step includes determining the presence or absence of: (i) an increased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)), PC(38:5), PI(40:5), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3) and/or SM(d42:4), or a variant or derivative thereof, and/or (ii) a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:
  • the measuring step includes determining the presence or absence of: (i) an increased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2)), PC(36:5e), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PI
  • the diagnostic methods described herein may include the step of administering a treatment to the subject.
  • this can include administering to the subject a therapeutically effective amount of the treatment, such as those anti-cancer treatments described herein, when the level of the one or more lipid biomarkers (and/or a risk or diagnostic score derived therefrom) is diagnostic or indicative of the subject having the breast cancer.
  • Methods of treatment Further to the above, the methods described herein may improve patient outcomes by diagnosing subjects with breast cancer, who could potentially benefit from a treatment thereof. Accordingly, the inventors have developed methods of treating a breast cancer in a subject.
  • the present disclosure provides a method of treating a breast cancer in a subject, said method including the step of performing a treatment in respect of the subject, such as surgery and/or administering a therapeutically effective amount of an anti-cancer treatment, in which a level of one or more lipid biomarkers has been measured in a biological sample therefrom that is diagnostic or indicative of the subject having the breast cancer, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:
  • the present method includes the initial step of measuring the level of the one or more lipid biomarkers in the biological sample from the subject.
  • a risk or diagnostic score has been determined using the level, such as a concentration level or an expression level, of the one or more lipid biomarkers and the risk score or diagnostic score is diagnostic or indicative of the subject having the breast cancer.
  • the risk score or diagnostic score is generated at least in part via a logistic model.
  • the risk score or diagnostic score can be in the form of a probability of the subject having the breast cancer, such that in the absence of additional information a score of 50% or above provides that the subject has a higher probability of having breast cancer than not having breast cancer.
  • a diagnostic score of 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99% or higher was determined for the subject.
  • a specified agent e.g., an anti-cancer agent
  • treatment such as chemotherapy, radiation therapy, a molecularly targeted therapy and immunotherapy, sufficient to achieve a desired effect in a subject being treated with that agent.
  • this can be the amount of a composition comprising one or more agents that are necessary to reduce, alleviate and/or prevent a cancer (e.g., breast cancer) or cancer-associated disease, disorder or condition.
  • a “therapeutically effective amount” is sufficient to reduce or eliminate a symptom of a cancer, such as breast cancer.
  • a “therapeutically effective amount” is an amount sufficient to achieve a desired biological effect, for example, an amount that is effective to decrease or prevent cancer growth and/or metastasis.
  • a therapeutically effective amount of an agent is an amount sufficient to induce the desired result without causing a substantial cytotoxic effect in the subject.
  • the effective amount of an agent useful for reducing, alleviating and/or preventing a breast cancer will be dependent on the subject being treated, the type and severity of any associated disease, disorder and/or condition (e.g., the number and location of any associated metastases), and the manner of administration of the therapeutic composition.
  • the various agents, anti-cancer agents or cancer treatments described herein are administered to a subject as a pharmaceutical composition comprising a pharmaceutically- acceptable carrier, diluent or excipient.
  • a pharmaceutically- acceptable carrier such as those provided therein, may be employed for providing a subject with the composition of the present disclosure.
  • pharmaceutically-acceptable carrier, diluent or excipient is meant a solid or liquid filler, diluent or encapsulating substance that may be safely used in systemic administration.
  • a variety of carriers well known in the art may be used.
  • These carriers may be selected from a group including sugars, starches, cellulose and its derivatives, malt, gelatine, talc, calcium sulfate, liposomes and other lipid-based carriers, vegetable oils, synthetic oils, polyols, alginic acid, phosphate buffered solutions, emulsifiers, isotonic saline and salts such as mineral acid salts including hydrochlorides, bromides and sulfates, organic acids such as acetates, propionates and malonates and pyrogen-free water.
  • a useful reference describing pharmaceutically acceptable carriers, diluents and excipients is Remington’s Pharmaceutical Sciences (Mack Publishing Co. N.J.
  • any safe route of administration may be employed for providing a patient with the composition of the present disclosure.
  • oral, rectal, parenteral, sublingual, buccal, intravenous, intra-articular, intra-muscular, intra-dermal, subcutaneous, inhalational, intraocular, intraperitoneal, intracerebroventricular, transdermal and the like may be employed.
  • Dosage forms include tablets, dispersions, suspensions, injections, solutions, syrups, troches, capsules, suppositories, aerosols, transdermal patches and the like.
  • These dosage forms may also include injecting or implanting controlled releasing devices designed specifically for this purpose or other forms of implants modified to act additionally in this fashion.
  • Controlled release of the therapeutic agent may be effected by coating the same, for example, with hydrophobic polymers including acrylic resins, waxes, higher aliphatic alcohols, polylactic and polyglycolic acids and certain cellulose derivatives such as hydroxypropylmethyl cellulose.
  • the controlled release may be effected by using other polymer matrices, liposomes and/or microspheres.
  • compositions of the present disclosure suitable for oral or parenteral administration may be presented as discrete units such as capsules, sachets or tablets each containing a pre-determined amount of one or more therapeutic agents of the present disclosure, as a powder or granules or as a solution or a suspension in an aqueous liquid, a non-aqueous liquid, an oil-in-water emulsion or a water-in-oil liquid emulsion.
  • Such compositions may be prepared by any of the methods of pharmacy, which may include the step of bringing into association one or more agents as described above with the carrier which constitutes one or more necessary ingredients.
  • compositions are prepared by uniformly and intimately admixing the agents of the present disclosure with liquid carriers or finely divided solid carriers or both, and then, if necessary, shaping the product into the desired presentation.
  • the above compositions may be administered in a manner compatible with the dosage formulation, and in such amount as is pharmaceutically effective.
  • the dose administered to a patient should be sufficient to effect a beneficial response in a patient over an appropriate period of time.
  • the quantity of agent(s) to be administered may depend on the subject to be treated inclusive of the age, sex, weight and general health condition thereof, factors that will depend on the judgement of the practitioner.
  • kits can be formulated as discrete doses, such as in the form of a kit.
  • a kit may further comprise a package insert comprising printed instructions for simultaneous, concurrent, sequential, successive, alternate or separate use of the agents in the treatment, amelioration and/or prevention of cancer, as described herein, in a patient in need thereof.
  • the aforementioned kits are suitably for use in a method of treating, ameliorating and/or preventing breast cancer, inclusive of one or more symptoms, consequences, sequelae or complications thereof, as described herein.
  • the various therapeutic agents described herein can be formulated together in a composition that optionally includes a pharmaceutically acceptable carrier, excipient or diluent.
  • Methods of treating breast cancer may be prophylactic, preventative or therapeutic and suitable for treatment of cancer in mammals, particularly humans.
  • treating”, “treat” or “treatment” refers to a therapeutic intervention, course of action or protocol that at least ameliorates a symptom of cancer after the cancer and/or its symptoms have at least started to develop.
  • preventing”, “prevent” or “prevention” refers to therapeutic intervention, course of action or protocol initiated prior to the onset of cancer and/or a symptom of cancer so as to prevent, inhibit or delay or development or progression of the cancer or the symptom.
  • cancer treatments for use in the methods described herein may include drug therapy, chemotherapy, antibody, nucleic acid and other biomolecular therapies, radiation therapy, surgery, nutritional therapy, relaxation or meditational therapy and other natural or holistic therapies, although without limitation thereto.
  • drugs, biomolecules e.g., antibodies, inhibitory nucleic acids such as siRNA
  • chemotherapeutic agents are referred to herein as “anti-cancer therapeutic agents” or “anti-cancer agents”.
  • the treatment is or comprises one or more of surgery (e.g., lumpectomy or mastectomy), chemotherapy, radiation therapy, molecularly targeted therapy, hormone therapy and immunotherapy.
  • chemotherapy broadly refers to a treatment or agent with a cytostatic or cytotoxic agent (i.e., a compound) to reduce or eliminate the growth or proliferation of undesirable cells, such as cancer cells.
  • a cytostatic or cytotoxic agent i.e., a compound
  • the terms can refer to a cytotoxic or cytostatic agent used to treat a proliferative disorder, for example cancer.
  • the cytotoxic effect of the agent can be, but is not required to be, the result of one or more of nucleic acid intercalation or binding, DNA or RNA alkylation, inhibition of RNA or DNA synthesis, the inhibition of another nucleic acid-related activity (e.g., protein synthesis), or any other cytotoxic effect.
  • chemotherapeutic agents include, but are not limited to, alkylating agents (e.g., nitrogen mustards such as chlorambucil, cyclophosphamide, isofamide, mechlorethamine, melphalan, and uracil mustard; aziridines such as thiotepa; methanesulphonate esters such as busulfan; nitroso ureas such as carmustine, lomustine, and streptozocin; platinum complexes such as cisplatin and carboplatin, oxaliplatin, nedaplatin, triplatin tetranitrate, phenanthriplatin, picoplatin, satraplatin and lipoplatin; bioreductive alkylators such as mitomycin, procarbazine, dacarbazine and altretamine); DNA strand-breakage agents (e.g., bleomycin); topoisomerase II inhibitors (e.g., amsacrine,
  • radiation therapy refers to the medical use of ionizing radiation, generally as part of cancer treatment, to control or destroy malignant cells. It can also be used as part of adjuvant therapy to prevent tumour recurrence after surgery to remove a primary malignant tumour.
  • Radiation therapy may be delivered by a device placed outside the patient's body (external radiation therapy) or a source placed inside the patient's body (internal radiation therapy or brachytherapy), or intravenously or orally. It may also be delivered by a systemically delivered radioisotope.
  • Radiation therapy can be planned and administered in conjunction with imaging based techniques, such as computed tomography (CT) or magnetic resonance imaging (MRI) to accurately determine the dose and location of radiation to be administered.
  • CT computed tomography
  • MRI magnetic resonance imaging
  • radiation therapy includes total body radiation therapy, conventional external beam radiation therapy, stereotactic radiosurgery, stereotactic radiation therapy, three-dimensional conformal radiation therapy, intensity modulated radiation therapy (IMRT), image-guided radiation therapy, tomotherapy and/or brachytherapy.
  • the radiation therapy includes stereotactic radiation therapy or intensity modulated radiation therapy (IMRT).
  • “molecularly targeted therapy” or “molecularly targeted therapeutic agent” refers to a therapy that targets a particular class of proteins involved in cancer growth or signalling.
  • the further anti-cancer agent described herein is or comprises an inhibitor of a tyrosine kinase.
  • tyrosine kinase refers to enzymes which are capable of transferring a phosphate group from ATP to a tyrosine residue in a protein. Phosphorylation of proteins by tyrosine kinases is an important mechanism in signal transduction for regulation of enzyme activity and cellular events such as cell survival or proliferation.
  • the molecularly targeted therapy comprises one or more of a Human epidermal growth factor receptor 2 (HER2; also referred to as ErbB-2, NEU, HER-2 and CD340) inhibitor (e.g., trastuzumab, pertuzumab, neratinib, tucatinib), a PARP inhibitor (e.g., olaparib, talazoparib), a CDK4/6 inhibitor (e.g., abemaciclib), a PI3K inhibitor (e.g., alpelisib), a dual HER2/EGFR inhibitor (e.g., lapatinib), and a neurotrophic T receptor kinase (NTRK) inhibitor (e.g., entrectinib, larotrectinib).
  • HER2 Human epidermal growth factor receptor 2
  • NEU neurotrophic T receptor kinase
  • immunotherapy or immunotherapeutic agents use or modify the immune mechanisms of a subject so as to promote or facilitate treatment of a cancer.
  • immunotherapy or immunotherapeutic agents used to treat cancer include cell-based therapies, antibody therapies (e.g., anti-PD1, anti-PDL1 or anti-CTLA4 antibodies) and cytokine therapies. These therapies all exploit the phenomenon that cancer cells often have subtly different molecules termed cancer antigens on their surface that can be detected by the immune system of the cancer subject. Accordingly, immunotherapy is used to provoke the immune system of a cancer patient into attacking the cancer's cells by using these cancer antigens as targets.
  • Non-limiting examples of immunotherapy or immunotherapeutic agents include adalimumab, alemtuzumab, basiliximab, belimumab, bevacizumab, BMS-936559, brentuximab, certolizumab, cituximab, daclizumab, eculizumab, ibritumomab, infliximab, ipilimumab, lambrolkizumab, mepolizumab, MPDL3280A muromonab, natalizumab, nivolumab, ofatumumab, omalizumab, pembrolizumab, pexelizumab, pidilizumab, rituximab, tocilizumab, tositumomab, trastuzumab, ustekinumab, abatacept, alefacept and denileukin diftitox.
  • the immunotherapeutic agent is an immune checkpoint inhibitor, such as an anti- PD-1 antibody (e.g., pidilizumab, nivolumab, lambrolkizumab, pembrolizumab), an anti-PD-L1 antibody (e.g., BMS-936559, MPDL3280A) and/or an anti-CTLA4 antibody (e.g., ipilimumab).
  • an anti- PD-1 antibody e.g., pidilizumab, nivolumab, lambrolkizumab, pembrolizumab
  • an anti-PD-L1 antibody e.g., BMS-936559, MPDL3280A
  • an anti-CTLA4 antibody e.g., ipilimumab.
  • Lipid biomarkers As described herein, the inventors have found that the concentration levels of particular lipid biomarkers in blood or plasma samples from subjects can be diagnostic of breast cancer.
  • the lipid biomarkers are derived from extracellular vesicles, such as exosomes, present within the biological sample. It is also possible that other sources of lipid biomarkers that co-isolate with extracellular vesicles may contribute, such as apolipoproteins or lipid droplets.
  • lipid refers to a group of organic compounds that has lipophilic or amphiphilic properties, including, but not limited to, acyl carnitine (AcCa), bis(monoacylglycero)phosphates (BMP), cholesterol esters (CE), ceramides (Cer), diacylglycerols (DG or DAG), dihydroleukotriene B4 (DH-LTB4), fatty acids (FA), gangliosides A2 (GA2), gangliosides M3 (GM3), hexose ceramides (HexCer), dihexosylceramide (Hex2Cer), hexosyl dihydroceramide (HexDHCer), lactosylceramide (LacCer), lysophosphatidic acid (LysoPA or LPA), lysophosphatidylcholines (LysoPC or LPC), lysophosphatidy
  • biomarker refers to a lipid molecule whose levels are indicative or diagnostic of a subject having breast cancer. It will be appreciated that the term “biomarker” is intended to encompass all classes, forms (e.g., phosphorylated or oxidised forms), fragments (e.g., a lipid head group, a fatty acyl chain) and variants (e.g., isomers and isobars) of a lipid biomarker, as are known in the art, such as those provided herein.
  • forms e.g., phosphorylated or oxidised forms
  • fragments e.g., a lipid head group, a fatty acyl chain
  • variants e.g., isomers and isobars
  • the ether-linked lipids described herein encompass both alkyl-ether and alkenyl-ether forms thereof unless stated otherwise (e.g., the alkyl-ether and alkenyl-ether of PE(38:6e) include PE(O- 38:6) and PE(P-38:5) respectively).
  • the 'O-' prefix is used to indicate the presence of an alkyl ether substituent
  • the “P-” prefix or “p” suffix is used for the alkenyl ether substituent.
  • the one or more lipid biomarkers described herein can be selected from one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 etc) classes of lipids, such as AcCa, Cer, DG, Hex2Cer, LPC, LPE, LPI, PC, PE, PG, PI, PS, SM, SphP and TG.
  • the one or more lipid biomarkers comprise LPC(14:0) and/or PI(38:6). More particularly, the one or more lipid biomarkers may comprise LPC(14:0) and/or PI(18:2_20:4).
  • the one or more lipid biomarkers comprise LPC(14:0) and PI(38:6). More particularly, the one or more lipid biomarkers may comprise LPC(14:0) and PI(18:2_20:4). In some examples, the one or more lipid biomarkers comprise LPC(14:0), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5
  • the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:1),
  • the one or more lipid biomarkers comprise LPC(14:0), or a fragment, variant or derivative thereof and one or more other lipid biomarkers selected from the group consisting of: (a) LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2) (such as, TG(16:0_10:
  • the one or more lipid biomarkers comprise PI(38:6), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e),
  • the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), LPC(18:3), PC(34:0), PC(36:0), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), PS(40:6), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d42:4), TG(42:2),
  • the one or more lipid biomarkers comprise PI(38:6), or a fragment, variant or derivative thereof and one or more other lipid biomarkers selected from the group consisting of: (a) LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(42:2), TG(44:2) (such as, TG(16:0_10:
  • the one or more lipid biomarkers comprise LPC(14:0) and PI(38:6), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2
  • the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of LPC(16:0), LPC(18:0), PC(34:0), PC(36:0), PC(36:4), PC(36:5e), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PG(36:1), PI(36:3), PI(36:4), PS(36:1), SM(d37:1), SM(d39:2), SM(d40:2), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(53:0), TG(56:0), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise LPC(14:0) and PI(38:6), or a fragment, variant or derivative thereof, and one or more other lipid biomarkers selected from the group consisting of: (a) LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(42:2), TG(44:2) (such as, TG(16:0_
  • the one or more lipid biomarkers comprise Cer(d36:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE
  • the one or more lipid biomarkers comprise Cer(d36:1), or a fragment, variant or derivative thereof and one or more other lipid biomarkers selected from the group consisting of: (a) AcCa(18:2), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1); (b) Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0),
  • the one or more lipid biomarkers comprise LPC(14:0), or a fragment, variant or derivative thereof and one or more other lipid biomarkers selected from the group consisting of: (a) AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1); (b) Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2),
  • the one or more lipid biomarkers comprise LPC(16:0e), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE
  • the one or more lipid biomarkers comprise LPC(16:0e), or a fragment, variant or derivative thereof and one or more other lipid biomarkers selected from the group consisting of: (a) AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1); (b) Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2),
  • the one or more lipid biomarkers comprise PC(32:2), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE
  • the one or more lipid biomarkers comprise PC(32:2), or a fragment, variant or derivative thereof and one or more other lipid biomarkers selected from the group consisting of: (a) AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1); (b) Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2),
  • the one or more lipid biomarkers comprise PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), or a fragment, variant or derivative thereof. More particularly, the one or more lipid biomarkers suitably comprise PC(18:2/18:2), or a fragment, variant or derivative thereof. Even more particularly, the one or more lipid biomarkers suitably comprise PC(16:0/20:4), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE
  • the one or more lipid biomarkers comprise PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), or a fragment, variant or derivative thereof and one or more other lipid biomarkers selected from the group consisting of: (a) AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1); (b) Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2),
  • the one or more lipid biomarkers comprise SM(d36:2), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e
  • the one or more lipid biomarkers comprise SM(d36:2), or a fragment, variant or derivative thereof and one or more other lipid biomarkers selected from the group consisting of: (a) AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4) and SphP(d18:1); (b) Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2),
  • the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • AcCa(18:2) AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC
  • the one or more lipid biomarkers comprise or consist of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • AcCa(18:2) AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0
  • the biological sample is suitably a plasma sample.
  • the one or more lipid biomarkers are selected from the group consisting of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and SM(d36:2), or a fragment, variant or derivative thereof.
  • Cer(d36:1) Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:
  • the one or more lipid biomarkers comprise or consist of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and SM(d36:2), or a fragment, variant or derivative thereof.
  • the biological sample is suitably a plasma sample.
  • the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise or consist of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the biological sample is suitably a plasma sample.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer.
  • the one or more lipid biomarkers are selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)) and SM(d36:2), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise or consist of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)) and SM(d36:2), or a fragment, variant or derivative thereof.
  • the biological sample is suitably a plasma sample.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), LPC(16:0e), PC(32:2) and/or PC(36:4) (e.g., PC(18:2/18:2)), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), LPC(16:0e), PC(32:2) and/or PC(36:4) (e.g., PC(18:2/18:2), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject.
  • the one or more lipid biomarkers comprise Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)) and SM(d36:2), or a fragment, variant or derivative thereof and one or more other lipid biomarkers.
  • the one or more other lipid biomarkers may be selected from the group consisting of AcCa(18:2), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(16:0), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1)
  • the one or more other lipid biomarkers may be selected from the group consisting of AcCa(18:2), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(18:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • AcCa(18:2) Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(18:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4) and SphP(d18:1),
  • the one or more other lipid biomarkers may be selected from the group consisting of AcCa(18:2), LPC(18:2), PE(36:2), PS(38:4) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more other lipid biomarkers may be selected from the group consisting of Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)) and PI(40:5), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers are selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise or consist of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the biological sample is suitably a plasma sample.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(16:0), LPC(18:0), PC(34:0), PC(36:0), PC(36:4), PC(36:5e), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PG(36:1), PI(36:3), PI(36:4), PS(36:1), SM(d37:1), SM(d39:2), SM(d40:2), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(53:0), TG(56:0), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise or consist of LPC(16:0), LPC(18:0), PC(34:0), PC(36:0), PC(36:4), PC(36:5e), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PG(36:1), PI(36:3), PI(36:4), PS(36:1), SM(d37:1), SM(d39:2), SM(d40:2), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(53:0), TG(56:0), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof.
  • the biological sample is suitably a plasma sample and/or an EV sample.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2) (such as, TG(16:0_10:0_18:2) and/or TG(16:0_10:1_18:1)), TG(50:1e), TG(52:3e), TG(56:0), TG
  • the one or more lipid biomarkers comprise or consist of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2) (such as, TG(16:0_10:0_18:2) and/or TG(16:0_10:1_18:1)), TG(50:1e), TG(52:3e), TG(56:0), TG(56:1)
  • the biological sample is suitably a plasma sample.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)) and TG(52:3e), or a fragment, variant or derivative thereof.
  • LPC(14:0), LPC(16:0), PC(36:2) such as PC(18:1_18:1) and PC(18:0_18:2)
  • PC(36:4)
  • the one or more lipid biomarkers comprise or consist of LPC(14:0), LPC(16:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)) and TG(52:3e), or a fragment, variant or derivative thereof.
  • LPC(14:0), LPC(16:0), PC(36:2) such as PC(18:1_18:1) and PC(18:0_18:2)
  • PC(36:4) PE(34:
  • the biological sample is suitably a plasma sample.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(52:3e) and TG(56:1), or a fragment, variant or derivative thereof.
  • LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2) such as PC(18:1
  • the one or more lipid biomarkers comprise or consist of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(52:3e) and TG(56:1), or a fragment, variant or derivative thereof.
  • LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2) such as PC(18:1_18:1)
  • the biological sample is suitably a plasma sample.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(52:3e) and TG(58:2), or a fragment, variant or derivative thereof.
  • LPC(14:0), LPC(16:0), PC(36:2) such as PC(18:1_18:1) and PC(
  • the one or more lipid biomarkers comprise or consist of LPC(14:0), LPC(16:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(52:3e) and TG(58:2), or a fragment, variant or derivative thereof.
  • LPC(14:0), LPC(16:0), PC(36:2) such as PC(18:1_18:1) and PC(18:0_
  • the biological sample is suitably a plasma sample.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PC(36:5e), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d37:1), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), TG(51:0), TG(52:3e), TG(53:0), TG(58:1), TG(58:2) and TG(59:1), or a fragment, variant or derivative thereof
  • the one or more lipid biomarkers comprise or consist of LPC(14:0), PC(34:0), PC(36:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PC(36:5e), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d37:1), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), TG(51:0), TG(52:3e), TG(53:0), TG(58:1), TG(58:2) and TG(59:1), or a fragment, variant or derivative thereof.
  • the biological sample is suitably an EV sample or a sample enriched for EVs.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d39:2), SM(d41:2), TG(52:3e), TG(53:0), TG(58:1) and TG(58:2), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise or consist of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d39:2), SM(d41:2), TG(52:3e), TG(53:0), TG(58:1) and TG(58:2), or a fragment, variant or derivative thereof.
  • the biological sample is suitably an EV sample or a sample enriched for EVs.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PC(36:2), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(52:3e), and TG(58:2), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise or consist of LPC(14:0), PC(36:2), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(52:3e), and TG(58:2), or a fragment, variant or derivative thereof.
  • the biological sample is suitably a plasma sample and/or an EV sample or a sample enriched for EVs.
  • a decreased level of LPC(14:0), PC(36:2), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(52:3e), and/or TG(58:2), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1), PS(38:4) and TG(52:3e), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise or consist of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1), PS(38:4) and TG(52:3e), or a fragment, variant or derivative thereof.
  • the biological sample is suitably a plasma sample and/or an EV sample or a sample enriched for EVs.
  • a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1), PS(38:4) and/or TG(52:3e), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer.
  • a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1), PS(38:4) and/or TG(52:3e), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(18:3), PC(36:4), PE(36:2p), PE(38:6e), PE(40:6e), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), SM(d38:4) and SM(d42:4), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise or consist of LPC(14:0), LPC(18:3), PC(36:4), PE(36:2p), PE(38:6e), PE(40:6e), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), SM(d38:4) and SM(d42:4), or a fragment, variant or derivative thereof.
  • the biological sample is suitably a plasma sample and/or an EV sample or a sample enriched for EVs.
  • an increased level of SM(d38:4) and/or SM(d42:4), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), LPC(18:3), PC(36:4), PE(36:2p), PE(38:6e), PE(40:6e), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4) and/or PS(40:6), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer.
  • the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4), PC(38:5), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise or consist of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4), PC(38:5), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the biological sample is suitably a plasma sample and/or an EV sample or a sample enriched for EVs.
  • the one or more lipid biomarkers are selected from the group consisting of: (a) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1); (b) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); (c) Cer(d36:1), LPA(18:0), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PS(38:4), SM(d
  • the one or more lipid biomarkers are selected from the group consisting of Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise, consist of or consist essentially of Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • an increased level of Cer(d36:1), SM(d35:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer.
  • the one or more lipid biomarkers are selected from the group consisting of: Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise, consist of or consist essentially of Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject.
  • the one or more lipid biomarkers are selected from the group consisting of Cer(d36:1), LPA(18:0), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise, consist of or consist essentially of Cer(d36:1), LPA(18:0), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPA(18:0), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPA(18:0), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject.
  • the one or more lipid biomarkers are selected from the group consisting of Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise, consist of or consist essentially of Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject.
  • the one or more lipid biomarkers are selected from the group consisting of Cer(d36:1), Cer(d42:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), PC(36:0), PC(36:2) (such as PC(18:0_18:2) and/or PC(18:1_18:1)), PE(34:1), PI(34:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise, consist of or consist essentially of Cer(d36:1), Cer(d42:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), PC(36:0), PC(36:2) (such as PC(18:0_18:2) and/or PC(18:1_18:1)), PE(34:1), PI(34:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers may comprise, consist of or consist essentially of Cer(d36:1), Cer(d42:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), PC (36:0), PC(18:0_18:2), PC(18:1_18:1), PE(34:1), PI(34:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise, consist of or consist essentially of AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • an increased level of Cer(d36:1), SM(d35:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer.
  • the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise, consist of or consist essentially of AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject.
  • the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise, consist of or consist essentially of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject.
  • the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise, consist of or consist essentially of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject.
  • the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers comprise, consist of or consist essentially of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer.
  • an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2) and/or SphP(d18:1), or a fragment, variant or derivative thereof has suitably been measured in the biological sample obtained from the subject.
  • lipid “variants” such as naturally occurring variants, isobars and isomers (including stereoisomers) of the lipid biomarkers provided herein.
  • the lipid biomarkers described herein may encompass a collection of one or more isomers thereof.
  • PC(36:2) is a lipid or lipid biomarker that is the collection of one or more phosphatidylcholine isomers that have 36 carbons in the acyl chain and two double bonds across the two acyl chains.
  • Exemplary isomers for the lipid biomarkers described herein are provided in the below table.
  • each of the lipid biomarker isomers have identical molecular weights.
  • a lipid biomarker can encompass a total number of isomers thereof, a biological sample from a subject may only contain one isomer, two isomers, three isomers, four isomers, five isomers etc, or any number of isomers less than the total number of all possible isomers of said lipid biomarker. Accordingly, a lipid biomarker can refer to one or more of the isomers that make up the entire collection of possible isomers.
  • measuring a level of PC(36:2) includes measuring a level of one or both of the isomers PC(18:1_18:1) and PC(18:0_18:2).
  • measuring a level of PC(36:2) includes measuring a level of PC(18:1_18:1).
  • measuring a level of PC(36:2) includes measuring a level of PC(18:0_18:2).
  • the one or more lipid biomarkers described herein can be selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(18:1_18:1), PC(18:0_18:2), PC(36:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:1),
  • measuring a level of TG(44:2) includes measuring a level of one or both of the isomers TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1).
  • measuring a level of TG(44:2) includes measuring a level of TG(16:0_10:0_18:2).
  • measuring a level of TG(44:2) includes measuring a level of TG(16:0_10:1_18:1).
  • the one or more lipid biomarkers described herein can be selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1)
  • reference to PC(36:4) herein may include one or both of isomers PC(18:2/18:2) and PC(16:0/20:4).
  • PC(36:4) it has been observed by the present inventors that the levels of PC 36:4 can increase or decrease in cancer patients. Without being bound by any theory, this is hypothesised to be isomer dependent in that the levels of the PC 18:2_18:2 isomer have been observed to decrease in breast cancer patients, whilst the levels of the PC 16:0_20:4 isomer have been observed to decrease in breast cancer patients (see e.g., Figure 25).
  • measuring a level of PC(36:4) includes measuring a level of one or both of the isomers PC(18:2/18:2) and PC(16:0/20:4).
  • measuring a level of PC(36:4) includes measuring a level of PC(18:2/18:2).
  • measuring a level of PC(36:4) includes measuring a level of PC(16:0/20:4).
  • the one or more lipid biomarkers described herein can be selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(18:2/18:2), PC(16:0/20:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4),
  • the one or more lipid biomarkers described herein can suitably be selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(18:1_18:1), PC(18:0_18:2), PC(18:2/18:2), PC(16:0/20:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI
  • the lipid biomarkers described herein may encompass or be interchangeable with one or more isobars thereof.
  • isobar typically refers to different lipids that have nearly or substantially the same mass (e.g., m/z ratio) and may not be distinguished from each other on the analytical platform used in their detection (e.g., for mass spectrometry, the different lipids in an isobar can elute at nearly the same time and have similar or the same quant ions, and thus cannot be distinguished).
  • lipids can be defined according to the following equation: XXX(YY:ZZ), in which XXX is the abbreviation for the lipid class or group (in many instances indicating the lipid headgroup), YY is the number of carbons in the acyl chain and ZZ is the number of double bonds in the acyl chains. Similar notation (e.g., XXX(YY1:ZZ1_YY2:ZZ2) or XXX(YY1:ZZ1_YY2:ZZ2_YY3_ZZ3) may be used to define lipid isomers, wherein the numbers refer to the particular acyl chain of the lipid.
  • the lipids defined herein may be identified by different naming annotations or nomenclature as are known in the art (see, e.g., Liebisch et al., J Lipid Res, 2013 Jun;54(6):1523-1530; Lipidomics Standards Initiative Consortium, Nat Metab, 2019 Aug;1(8):745-747). It is also envisaged that the recited lipid biomarkers may additionally cover one or more further lipid biomarkers that behave similarly or equivalently (e.g., demonstrate a similar concentration profile) to said lipid biomarker. To this end, the lipid biomarker may demonstrate substantial collinearity with one or more further lipid biomarkers in terms of, for example, being diagnostic or indicative of breast cancer in a subject.
  • Collinearity refers to a strong correlation or linear relationship between a pair of predictors (e.g., a pair of lipid biomarkers), and collinearity between multiple predictors is called multi-collinearity.
  • the one or more lipid biomarkers comprises one or more further lipid biomarkers, such as those outlined in Example 1 below, that demonstrate collinearity with one or more of the one or more lipid biomarkers recited in the examples provided herein.
  • the lipid biomarker demonstrates little or no collinearity with one or more further lipid biomarkers.
  • fragments of the lipid biomarkers inclusive of a lipid headgroup and an acyl chain or fragments thereof, that comprise less than 100% of an entire lipid biomarker molecule.
  • MRM analysis of lipid biomarkers by mass spectrometry can include fragmenting lipids into their component parts (e.g., lipid headgroups and one or more acyl chains) so as to assist in identification and quantification of said lipid biomarker, as described in more detail below.
  • High-resolution accurate-mass MS (HRMS) may also be utilised to perform reliable and sensitive quantitative analyses of lipid biomarkers, similar to that of MRM (see Rochat, Trends in Analytical Chemistry, 2016 for review).
  • PRM Parallel reaction monitoring
  • MS1 full-scan
  • MS/MS tandem mass spectrometry
  • the level (e.g., concentration or expression level) of two or more of the lipid biomarkers e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62 or 63 lipid biomarkers
  • the methods described herein include the step of determining the level or concentration of three or more lipid biomarkers described herein.
  • the methods described herein include the step of determining the level or concentration of four or more lipid biomarkers described herein. In certain examples, the methods described herein include the step of determining the level or concentration of five or more lipid biomarkers described herein. In some examples, the methods described herein include the step of determining the level or concentration of six or more lipid biomarkers described herein. In various examples, the methods described herein include the step of determining the level or concentration of seven or more lipid biomarkers described herein. In particular examples, the methods described herein include the step of determining the level or concentration of eight or more lipid biomarkers described herein. In some examples, the methods described herein include the step of determining the level or concentration of nine or more lipid biomarkers described herein.
  • the methods described herein include the step of determining the level or concentration of ten or more lipid biomarkers described herein. In certain examples, the methods described herein include the step of determining the level or concentration of eleven or more lipid biomarkers described herein. In some examples, the methods described herein include the step of determining the level or concentration of twelve or more lipid biomarkers described herein. In various examples, the methods described herein include the step of determining the level or concentration of thirteen or more lipid biomarkers described herein. In particular examples, the methods described herein include the step of determining the level or concentration of fourteen or more lipid biomarkers described herein.
  • the methods described herein include the step of determining the level or concentration of fifteen or more lipid biomarkers described herein. In particular examples, the methods described herein include the step of determining the level or concentration of sixteen or more lipid biomarkers described herein. In some examples, the methods described herein include the step of determining the level or concentration of seventeen or more lipid biomarkers described herein. In certain examples, the methods described herein include the step of determining the level or concentration of eighteen or more lipid biomarkers described herein. In particular examples, the methods described herein include the step of determining the level or concentration of nineteen or more lipid biomarkers described herein.
  • the methods described herein include the step of determining the level or concentration of twenty or more lipid biomarkers described herein. In particular examples, the methods described herein include the step of determining the level or concentration of twenty-one or more lipid biomarkers described herein. In some examples, the methods described herein include the step of determining the level or concentration of twenty-two or more lipid biomarkers described herein. In certain examples, the methods described herein include the step of determining the level or concentration of twenty-three or more lipid biomarkers described herein. In various examples, the methods described herein include the step of determining the level or concentration of twenty-four or more lipid biomarkers described herein.
  • the methods described herein include the step of determining the level or concentration of twenty-five or more lipid biomarkers described herein. In some examples, the methods described herein include the step of determining the level or concentration of twenty-six or more lipid biomarkers described herein. In certain examples, the methods described herein include the step of determining the level or concentration of twenty-seven or more lipid biomarkers described herein. In various examples, the methods described herein include the step of determining the level or concentration of twenty-eight or more lipid biomarkers described herein. In particular examples, the methods described herein include the step of determining the level or concentration of twenty-nine or more lipid biomarkers described herein.
  • the methods described herein include the step of determining the level or concentration of thirty or more lipid biomarkers described herein.
  • the methods of the present disclosure include the step of determining a level of LPC(14:0) and at least one further lipid biomarker described herein (e.g., one or more of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2) (e.g., PC(18:1_18:1) and PC(18:0_
  • the methods of the present disclosure include the step of determining a level of AcCa(18:2) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of Cer(d36:1) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of Cer(d18:1/18:0) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of Cer(d38:1) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure can include the step of determining a level of Cer(d18:1/20:0) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of Cer(d39:1) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure can include the step of determining a level of Cer(d16:1/23:0) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of Cer(d40:1) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure can include the step of determining a level of Cer(d18:1/22:0) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of Cer(d41:1) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure can include the step of determining a level of Cer(d18:1/23:0) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of Cer(d41:2) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure can include the step of determining a level of Cer(d17:1/24:1) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of Cer(d42:2) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure can include the step of determining a level of Cer(d18:1/24:1) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of LPA(18:0) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of LPA(18:2) and at least one further lipid biomarker described herein. In some examples, the methods of the present disclosure include the step of determining a level of LPC(16:0) and at least one further lipid biomarker described herein. According to particular examples, the methods of the present disclosure include the step of determining a level of LPC(16:0e) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of LPC(18:0) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of LPC(18:2) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of LPC(18:3) and at least one further lipid biomarker described herein. According to particular examples, the methods of the present disclosure include the step of determining a level of LPI(18:0) and at least one further lipid biomarker described herein. According to particular examples, the methods of the present disclosure include the step of determining a level of LPI(18:1) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of PC(32:2) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PC(14:0/18:2) and at least one further lipid biomarker described herein. In particular examples, the methods of the present disclosure include the step of determining a level of PC(34:0) and at least one further lipid biomarker described herein. In certain examples, the methods of the present disclosure include the step of determining a level of PC(36:0) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure can include the step of determining a level of PC(18:0/18:0) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of PC(36:2), such as PC(18:1_18:1) and/or PC(18:0_18:2), and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PC(18:1_18:1) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure can include the step of determining a level of PC(18:0_18:2) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of PC(36:4), such as PC(18:2/18:2) and/or PC(16:0/20:4) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure can include the step of determining a level of PC(18:2/18:2) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure can include the step of determining a level of PC(16:0/20:4) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure can include the step of determining a level of PC(16:0/20:4) and PC(18:2/18:2) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of PC(36:5e) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of PC(38:5) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PC(18:1/20:4) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of PE(34:2p) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of PE(36:2) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PE(18:0/18:2) and at least one further lipid biomarker described herein. In certain examples, the methods of the present disclosure include the step of determining a level of PE(36:2p) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of PE(36:3p) and at least one further lipid biomarker described herein. In some examples, the methods of the present disclosure include the step of determining a level of PE(36:5p) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of PE(38:2p) and at least one further lipid biomarker described herein. In particular examples, the methods of the present disclosure include the step of determining a level of PE(38:6p) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of PE(38:6e) and at least one further lipid biomarker described herein. In various examples, the methods of the present disclosure include the step of determining a level of PE(40:6e) and at least one further lipid biomarker described herein. For some examples, the methods of the present disclosure include the step of determining a level of PE(40:7e) and at least one further lipid biomarker described herein. In some examples, the methods of the present disclosure include the step of determining a level of PG(36:1) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of PI(36:1) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PI(18:0/18:1) and at least one further lipid biomarker described herein. In particular examples, the methods of the present disclosure include the step of determining a level of PI(36:3) and at least one further lipid biomarker described herein. In certain examples, the methods of the present disclosure include the step of determining a level of PI(36:4) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of PI(38:6) and at least one further lipid biomarker described herein. In various examples, the methods of the present disclosure include the step of determining a level of PI(40:5) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PI(18:0/22:5) and at least one further lipid biomarker described herein. In some examples, the methods of the present disclosure include the step of determining a level of PS(36:1) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure can include the step of determining a level of PS(18:0/18:1) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of PS(38:4) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure can include the step of determining a level of PS(18:0/20:4) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of PS(40:6) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of PS(40:7) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of SM(d35:1) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of SM(d36:2) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of SM(d18:1/18:1) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of SM(d37:1) and at least one further lipid biomarker described herein. According to particular examples, the methods of the present disclosure include the step of determining a level of SM(d38:4) and at least one further lipid biomarker described herein. In some examples, the methods of the present disclosure include the step of determining a level of SM(d39:2) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of SM(d40:2) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of SM(d41:2) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of SM(d41:3) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of SM(d42:4) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of SphP(d18:1) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of TG(42:2) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of TG(44:2), such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1), and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of TG(16:0_10:0_18:2) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure can include the step of determining a level of TG(16:0_10:1_18:1) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of TG(16:0_10:1_18:1) and TG(16:0_10:0_18:2) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of TG(50:1e) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of TG(51:0) and at least one further lipid biomarker described herein. In certain examples, the methods of the present disclosure include the step of determining a level of TG(52:3e) and at least one further lipid biomarker described herein. In various examples, the methods of the present disclosure include the step of determining a level of TG(53:0) and at least one further lipid biomarker described herein. In some examples, the methods of the present disclosure include the step of determining a level of TG(56:0) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of TG(56:1) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of TG(58:1) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of TG(58:2) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of TG(59:1) and at least one further lipid biomarker described herein.
  • the methods of the present disclosure include the step of determining a level of TG(60:5) and at least one further lipid biomarker described herein. Any of the methods disclosed herein may not include measuring any other biomarker. Thus, the methods disclosed herein may comprise excluding from analysis any other biomarker.
  • the one or more lipid biomarkers may not include one or more of Cer(d36:1), Cer(d38:1), LPC(16:0), LPC(18:0), PC(36:2), PC(36:4), PC(36:5e), PC(38:5), PE(36:5p), PE(38:2p), SM(d40:2), TG(50:1e) and TG(52:3e), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2) (such as, TG(16:0_10:0_18:2) and/or TG(16:0_10:1_18:1)), TG(56:0), TG(56:1), TG(58:2) and TG(60:5), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4) and TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)) and TG(56:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)) and TG(58:2), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d37:1), SM(d39:2), SM(d41:2), SM(d41:3), TG(51:0), TG(53:0), TG(58:1), TG(58:2) and TG(59:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d39:2), SM(d41:2), TG(53:0), TG(58:1) and TG(58:2), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4) and TG(58:2), or a fragment, variant or derivative thereof.
  • a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4) and/or TG(58:2), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer.
  • a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4) and/or TG(58:2), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject.
  • the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PE(34:2p), PE(36:2p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1) and PS(38:4), or a fragment, variant or derivative thereof.
  • a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1) and/or PS(38:4), or a fragment, variant or derivative thereof is suitably diagnostic or indicative of the subject having the breast cancer.
  • a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1) and/or PS(38:4), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject.
  • the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • an increased level of SM(d35:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer.
  • the one or more lipid biomarkers may be selected from the group consisting of AcCa(18:2), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • AcCa(18:2) Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(
  • the one or more lipid biomarkers may be selected from the group consisting of Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and SM(d36:2), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers may be selected from the group consisting of LPC(14:0), LPC(16:0e), PC(32:2), and SM(d36:2), or a fragment, variant or derivative thereof. Still even more particularly, the one or more lipid biomarkers may be selected from the group consisting of AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers may be selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
  • the present methods may include the further step of determining one or more further lipid biomarkers selected from the group consisting of Cer(d36:1), Cer(d38:1), LPC(16:0), LPC(18:0), PC(36:2), PC(36:4), PC(36:5e), PC(38:5), PE(36:5p), PE(38:2p), SM(d40:2), TG(50:1e) and TG(52:3e), or a fragment, variant or derivative thereof.
  • the present methods may include the further step of determining one or more further lipid biomarkers selected from the group consisting of Cer(d42:1), PC (36:0), PC(36:2) (such as PC(18:0_18:2) and/or PC(18:1_18:1)), PE(34:1) and PI(34:1), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers may not include one or more of Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:3), PC(36:2), PE(34:2p), PE(O-40:6), PE(40:7e), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d38:4), SM(d41:2), SM(d41:3), SM(d42:4), TG(52:3e), TG(56:1), TG(58:1) and TG(58:2), or a fragment, variant or derivative thereof.
  • the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:4), PC(36:5e), PC(38:5), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PG(36:1), PI(36:3), PI(36:4), PI(40:5), PS(36:1), SM(d37:1), SM(d39:2),
  • the present method may or may not include the further step of determining one or more further lipid biomarkers selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:3), PC(36:2), PE(34:2p), PE(O-40:6), PE(40:7e), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d38:4), SM(d41:2), SM(d41:3), SM(d42:4), TG(52:3e), TG(56:1), TG(58:1) and TG(58:2), or a fragment, variant or derivative thereof.
  • further lipid biomarkers selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:3), PC(36:2)
  • determining the level of lipid biomarkers It will be understood by the person skilled in the art that the level of expression, abundance or concentration of the one or more lipid biomarkers may be determined by any means known in the art.
  • the terms “determining”, “measuring”, “evaluating”, “assessing”, “quantifying”, “calculating” and “assaying” are used interchangeably herein and may include any form of measurement known in the art, such as those described hereinafter. Such determining may include detecting the presence or absence of one or more of the lipid biomarkers and/or determining a concentration level thereof in the biological sample obtained from the subject.
  • Suitable means for determining the level of concentration or expression of the one or more lipid biomarkers include, but are not limited to, nuclear magnetic resonance (NMR) spectrometry, surface plasmon resonance (SPR), chromatographic techniques, mass spectrometry, biosensors and any combination of these techniques.
  • NMR nuclear magnetic resonance
  • SPR surface plasmon resonance
  • chromatographic techniques mass spectrometry
  • biosensors biosensors and any combination of these techniques.
  • the level of concentration or expression of the one or more lipid biomarkers is measured by mass spectrometry.
  • Mass spectrometry is an analytical technique that measures the mass-to-charge (m/z) ratio of charged particles. It is primarily used for determining the elemental composition of a sample or molecules, and for elucidating the chemical structures of molecules, such as peptides, lipids and other chemical compounds.
  • MS works by ionizing chemical compounds to generate charged molecules or molecule fragments and measuring their mass-to-charge ratios.
  • MS instruments typically consist of three modules: (1) an ion source, which can convert gas phase sample molecules into ions (or, in the case of electrospray ionization, move ions that exist in solution into the gas phase); (2) a mass analyser, which sorts the ions by their masses by applying electromagnetic fields; and (3) a detector, which measures the value of an indicator quantity and thus provides data for calculating the abundances of each ion present.
  • Suitable mass spectrometry methods to be used with the present disclosure include but are not limited to, one or more of electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), tandem liquid chromatography-mass spectrometry (LC-MS/MS) mass spectrometry, desorption/ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(MS), atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS
  • the concentration or expression level of the one or more lipid biomarkers is determined at least in part by using liquid chromatography - mass spectrometry (LC-MS). In other examples, the concentration or expression level of the one or more lipid biomarkers is determined at least in part by using high-resolution accurate-mass MS (HRMS).
  • HRMS high-resolution accurate-mass MS
  • MS ionizes lipids and sorts ions based on their mass-to-charge ratio. It has been widely used to characterize lipids, especially with the development of soft ionization techniques such as electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI).
  • Lipid extraction is usually the first step for lipid analysis, and separates the lipidic components (organic phase) from other components such as proteins and nucleic acids (aqueous phase). Some examples, however, utilise monophasic lipid extraction. Extraction methods typically include the application of a mixture of methanol, chloroform and water for phase separation. However, shotgun lipidomic methods have also been developed which omit the chromatographic separation and sample processing described above, and analyzes all lipid classes together, and instead using ionization additives to assist in distinguishing between particular lipids.
  • the present MS method may involve lipid digestion, fragmentation or denaturation followed by LC-MS or LC-MS/MS (tandem MS) to derive mass-to-charge ratios for specific lipid headgroups and/or acyl chains that make up the lipid biomarkers described herein.
  • the one or more lipid biomarkers or one or more fragments thereof are subsequently subjected to quantitative mass spectrometry including without limitation, selected reaction monitoring mass spectrometry (SRM), high resolution data independent analyses (SWATH), multiple reaction monitoring (MRM) and/or MSI based quantitation.
  • SRM selected reaction monitoring mass spectrometry
  • SWATH high resolution data independent analyses
  • MRM multiple reaction monitoring
  • MSI MSI based quantitation.
  • an MRM assay is used which employs specific lipids and their fragments (transitions) as discriminators of individual lipid biomarkers.
  • the present MS method is performed in positive and/or negative ion modes.
  • lipids can form small cation adducts when in the positive-ion mode, due to the ionization process.
  • the formation of cation adducts of lipid molecular species resulted from the affinity of the cations with the dipole that is present in the lipid species depends on the availability of the small cations.
  • such adducts can include H + , NH4 + , Li + , Na + , K + , and (- H20+H) + .
  • lipid species in the deprotonated form or with an anionic adduct are displayed depending on whether the lipid molecule species carry a net ionic bond.
  • PE, PI, PS, PA, and PG are all of acidic lipid classes (i.e., an ionic bond is present), and thus, may be detected as deprotonated ions.
  • lipids are of a polar lipid class without an ionizable bond or PC and SM are strong zwitterionic lipid classes, all of which can form as anionic adducts with small anion(s) (e.g., Cl ⁇ , CH3COO ⁇ , and HCOO ⁇ ) depending on the concentrations present and their affinities with these lipid species.
  • the one or more lipid biomarkers described herein include an adduct as set out in Tables 4 and 5.
  • the one or more lipid biomarkers described herein include an ion mode as set out in Tables 4 and 5.
  • the ion mode is specific to that method of MS described in the respective Example.
  • Non-limiting exemplary ionization techniques that can be used with the present disclosure include but are not limited to Matrix Assisted Laser Desorption Ionization (MALDI), Desorption Electrospray Ionization (DESI), Direct Assisted Real Time (DART), Surface Assisted Laser Desorption Ionization (SALDI), or Electrospray Ionization (ESI).
  • MALDI Matrix Assisted Laser Desorption Ionization
  • DESI Desorption Electrospray Ionization
  • DART Direct Assisted Real Time
  • SALDI Surface Assisted Laser Desorption Ionization
  • ESI Electrospray Ionization
  • HPLC and UHPLC can be coupled to a mass spectrometer so that a number of other lipid separation techniques can be performed prior to mass spectrometric analysis.
  • Some exemplary separation techniques which can be used for separation of the desired analyte (e.g., lipid) from the matrix background include but are not limited to Reverse Phase Liquid Chromatography (RP-LC) of lipids, offline Liquid Chromatography (LC) prior to MALDI, 1- dimensional gel separation, 2-dimensional gel separation, Strong Cation Exchange (SCX) chromatography, Strong Anion Exchange (SAX) chromatography, Weak Cation Exchange (WCX), and Weak Anion Exchange (WAX).
  • RP-LC Reverse Phase Liquid Chromatography
  • SCX Strong Cation Exchange
  • SAX Strong Anion Exchange
  • WCX Weak Cation Exchange
  • WAX Weak Anion Exchange
  • the expression or concentration of a lipid biomarker will be higher or increased in a subject compared to a reference value determined from controls.
  • an increased level of expression or concentration of a first subset of the one or more lipid biomarkers indicates or correlates with the subject having a breast cancer; and/or a decreased level of expression or concentration of a second subset the one or more lipid biomarkers (e.g., not present in the first subset of the one or more lipid biomarkers) indicates or correlates with the subject having a breast cancer.
  • a decreased level of concentration or expression of a first subset of the one or more lipid biomarkers indicates or correlates with the subject not having a breast cancer; and/or an increased level of expression or concentration of a second subset of the one or more lipid biomarkers (e.g., not present in the first subset of the one or more lipid biomarkers) indicates or correlates with the subject not having a breast cancer.
  • the level or expression level of any one of the lipid biomarkers described herein may be relatively (i) higher, increased or greater; or (ii) lower, decreased or reduced when compared to an expression level in a control or reference sample, or to a threshold expression level.
  • an expression level may be classified as higher, increased or greater if it exceeds a mean and/or median expression level of a reference population. In some examples, an expression level may be classified as lower, decreased or reduced if it is less than the mean and/or median expression level of the reference population.
  • a reference population may be a group of subjects who have breast cancer. Alternatively, a reference population may be a group of subjects who are known to be free of cancer, and more particularly free of breast cancer. Terms such as “higher”, “increased” and “greater” as used herein refer to an elevated amount or level of a lipid biomarker, such as in a biological sample, when compared to a control or reference level or amount.
  • the concentration or expression level of the lipid biomarker may be relative or absolute (i.e., relatively or absolutely higher, increased or greater).
  • the level of a lipid biomarker is higher, increased or greater if its level of concentration or expression is more than about 0.5%, 1%, 2%, 3%, 4%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, 150%, 200%, 300%, 400% or at least about 500% above the level of concentration or expression of the lipid biomarker in a control or reference level or amount.
  • lower refers to a lower amount or level of a lipid biomarker, such as in a biological sample, when compared to a control or reference level or amount.
  • concentration or expression level of the lipid biomarker may be relative or absolute (i.e., relatively or absolutely lower, reduced or decreased).
  • the concentration or expression of a lipid biomarker is lower, reduced or decreased if its level of concentration or expression is less than about 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20% or 10%, or even less than about 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.01%, 0.001% or 0.0001% of the level or amount of concentration or expression of the lipid biomarker in a control or reference level or amount.
  • control sample typically refers to a biological sample from a (healthy) non- diseased individual or population of individuals not having cancer or, more particularly, not having breast cancer. In some examples, the control sample may be from a subject known to be free of cancer, or more particularly, free of breast cancer.
  • control sample may be from a subject in remission from cancer.
  • the control sample may be a pooled, average or an individual sample.
  • An internal control is a marker from the same biological sample being tested.
  • control sample may also or alternatively be used to refer to a biological sample from a diseased individual or population of individuals having cancer or, more particularly, having breast cancer.
  • a reference level or amount is determined from measurements of the biomarkers in a corresponding panel of biomarkers from a population of healthy individuals.
  • healthy individual refers to a person or populations of persons who are known not to have breast cancer.
  • the control reference is determined from measurements of the corresponding biomarkers in a “typical population”.
  • a "typical population” will exhibit a spectrum of breast cancer at different stages of disease progression. It is particularly preferred that a “typical population” exhibits the expression characteristics of a cohort of subjects as described herein.
  • a reference level or amount may be derived from an established data set including one or more of: 1. a data set comprising measurements of the lipid biomarkers for a population of subjects known to have breast cancer; 2. a data set comprising measurements of the lipid biomarkers for the subject being tested wherein said measurements have been made previously, such as, for example, when the subject was known to be healthy; and/or 3. a data set comprising measurements of the lipid biomarkers for a healthy individual or a population of healthy individuals.
  • a data set comprising measurements of the lipid biomarkers may be obtained from a population of subjects known to have breast cancer, a healthy individual or a population of healthy individuals. Such subjects may be in a fasted state, a non-fasted state or a combination thereof.
  • a concentration or expression level may be an absolute or relative amount of an expressed lipid. Accordingly, in some examples, the concentration or expression level of any one of the one or more lipid biomarkers is compared to a control level of concentration or expression, such as the level of lipid concentration or expression of one or a plurality of “housekeeping” lipids or molecules in the biological sample of the subject.
  • the concentration or expression level of any one of the one or more lipid biomarkers is compared to a threshold level of concentration or expression, such as a level of lipid concentration or expression in a biological sample from a control subject not having breast cancer and/or an average or median level of lipid concentration or expression in biological samples derived from a population of breast cancer patients.
  • a threshold level of concentration or expression is generally a quantified level of concentration or expression of a lipid biomarker.
  • a concentration level or an expression level of a lipid biomarker in a sample that exceeds or falls below the threshold level of concentration or expression is predictive of a particular disease state or outcome, such as the presence or absence of breast cancer.
  • the nature and numerical value (if any) of the threshold level of concentration or expression will typically vary based on the method chosen to determine the concentration or expression of the one or more lipid biomarkers used in determining, for example, a breast cancer diagnosis in the subject.
  • a person of skill in the art would be capable of determining the threshold level of any one of the one or more lipid biomarkers in a sample that may be used in determining, for example, the presence or absence of breast cancer in the relevant subject, using any method of measuring lipid concentration, abundance or expression known in the art, such as those described herein.
  • the threshold level is a mean and/or median concentration or expression level (median or absolute) of the lipid biomarker in a reference population that, for example, have or do not have breast cancer.
  • a threshold level of concentration or expression should not be limited to a single value or result.
  • a threshold level of concentration or expression may encompass multiple threshold concentration or expression levels or a suitable range thereof that could signify, for example, a high, medium, or low probability of, for example, the subject having breast cancer.
  • any of the methods disclosed herein may comprise a step of establishing a reference level or threshold level of concentration or expression of the one or more lipid biomarkers.
  • the predictive accuracy of the methods described herein, as determined by an ROC AUC value is at least about 0.65 (e.g., at least about 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99 or any range therein). More particularly, the predictive accuracy of the methods described is suitably at least about 0.70. Even more particularly, the predictive accuracy of the methods described is suitably at least about 0.75.
  • the predictive accuracy of the methods described is suitably at least about 0.80. Still even more particularly, the predictive accuracy of the methods described is suitably at least about 0.85. Yet still even more particularly, the predictive accuracy of the methods described is suitably at least about 0.90.
  • the sensitivity of the methods described herein in terms of detecting or diagnosing breast cancer in a subject is at least about 0.65 (e.g., at least about 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99 or any range therein).
  • the term “sensitivity”, as used herein, relates to the percentage of subjects having breast cancer who are correctly identified as having breast cancer.
  • the sensitivity of the methods described is suitably at least about 0.70. Even more particularly, the sensitivity of the methods described is suitably at least about 0.75. Yet even more particularly, the sensitivity of the methods described is suitably at least about 0.80. Still even more particularly, the sensitivity of the methods described is suitably at least about 0.85. Yet still even more particularly, the sensitivity of the methods described is suitably at least about 0.90.
  • the specificity of the methods described herein in terms of detecting or diagnosing breast cancer in a subject is at least about 0.65 (e.g., at least about 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99 or any range therein).
  • the term “specificity”, as used herein, relates to the percentage of healthy subjects who are correctly identified as not having breast cancer.
  • the specificity of the methods described is suitably at least about 0.70. Even more particularly, the specificity of the methods described is suitably at least about 0.75. Yet even more particularly, the specificity of the methods described is suitably at least about 0.80. Still even more particularly, the specificity of the methods described is suitably at least about 0.85. Yet still even more particularly, the specificity of the methods described is suitably at least about 0.90. Additional cancer biomarkers It is envisaged that one or more further biomarkers of cancer, and more particularly breast cancer (i.e., breast cancer-associated biomarkers), may be used in conjunction or combination with the one or more lipid biomarkers in the diagnostic and treatment methods described herein.
  • Such further biomarkers may be any as are known in the art, and may comprise macromolecules, such as nucleic acid (DNA/RNA), proteins, and intact cells.
  • DNA/RNA nucleic acid
  • a level of such further biomarkers can be detected or determined in the same biological sample in which the level of the one or more lipid biomarkers is or has been assessed.
  • Exemplary further biomarkers include cancer antigen 15-3 (CA 15-3), cancer antigen 27-29 (CA 27-29), cancer antigen 19-9 (CA 19-9), cancer antigen 125 (CA 125), trefoil factor (TFF) 1, TFF2, TFF3, carcinoembryonic antigen (CEA), Alpha-fetoprotein (AFP), circulating tumour DNA (ctDNA), circulating tumour cells (CTC), serum epithelial membrane antigen/CK1 concentration ratio, pleiotrophin (PTN), miR- 127-3p with human epididymis secretory protein 4 (HE4), human anterior gradient (AGR) 2 with AGR3, vascular endothelial growth factor (VEGF) with CA 15–3, serum apolipoprotein C-I (apoC-I), autoantibodies (e.g., autoantibodies that target oncogenic and/or tumour suppressor proteins), miR-221, miR-21, miR-145, circular RNAs (circRNAs; e.g., hs
  • the methods described herein include the step of measuring a level of one or more further biomarkers in a biological sample from the subject, wherein the one or more further biomarkers are selected from the group consisting of CA 15-3, CA 125, CA 19-9, CEA and AFP. More particularly, the methods described herein can include the step of measuring a level of one or more, two or more, three or more, four or more of five further biomarkers in a biological sample from the subject, wherein the one or more further biomarkers are selected from the group consisting of CA 15-3, CA 125, CA 19-9, CEA and AFP.
  • determining the presence or absence of a breast cancer in a subject may include the step of calculating a risk score or a diagnostic score.
  • risk score or “disease risk score” refers to value calculated with one or more feature values or scores that indicates an undesirable physiological state of the patient, such as the presence of cancer.
  • risk score in certain instances refers to a numerical representation of the current degree of the risk or probability a patient is at for having a particular disease or condition.
  • a risk score may be calculated using the concentration or expression levels or expression signature of the one or more lipid biomarkers, such as in a panel (e.g., 2, 3, 4, 5 etc or more) of the diagnostic lipid biomarkers, inclusive of those hereinbefore described.
  • the methods described herein include the step of obtaining a risk score for a lipid biomarker combination hereinbefore described or set forth in the Example (e.g., Tables 4 and 5).
  • a concentration or expression signature of a lipid may be determined using the normalized level of concentration or expression of the lipid in a sample, and an independent diagnostic value of the lipid based on the correlation of the concentration or expression of the lipid with disease presence or absence.
  • a risk score may be calculated by combining the concentration or expression levels and/or the expression signatures of each lipid in a panel thereof.
  • Methods of calculating a risk score may be by any method or means known in the art.
  • the risk score is calculated at least in part by logistic regression.
  • a linear combination of the concentration or expression levels of the one or more lipid biomarkers with various coefficients determined through prior training may be generated and subsequently used to estimate the log odds of cancer.
  • the log odds can then be converted into a probability of a subject having breast cancer via logistic regression.
  • the risk score is calculated at least in part by partial least squares discriminant analysis. Accordingly, a risk score for a patient may be calculated according to the below formula: ( 1 + ⁇ ⁇ + ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ) Wherein the intercept I and coefficients ci are the specific logistic regression model parameters calculated in advance based on the training data, and the values Li represent the normalised lipid abundances measured for the respective patient sample for each lipid in the panel.
  • a calculated risk score of the disclosure may be used to determine the likelihood of the presence or absence of a breast cancer in a subject. In general, a calculated risk score may be compared to a reference risk score.
  • the subject if (i) the risk score is equal to or higher than the reference risk score, the subject has a breast cancer, and (ii) the risk score is lower than the reference risk score, the subject does not have a breast cancer. It is envisaged that a subject’s diagnosis and/or risk score can be utilised to determine whether said subject should be treated with an anti-cancer agent. Accordingly, in other examples, if (i) the risk score is equal to or higher than the reference risk score, the subject is to be administered an anti-cancer treatment, and (ii) the risk score is lower than the reference risk score, the subject is not to be administered an anti-cancer treatment.
  • the subject may have previously been subjected to a selection step based on one or more selection criteria, such as those described herein, prior to the present methods being performed.
  • the present methods may include the earlier step of selecting a subject based on one or more selection criteria described herein.
  • the one or more selection criteria may include age.
  • the subject is at least about 20 years old (e.g., at least about 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70 years old or any range therein), at least about 25 years old, at least about 30 years old, at least about 35 years old, at least about 40 years old, at least about 45 years old, at least about 50 years old, at least about 55 years old, at least about 60 years old, at least about 65 years old, or at least about 70 years old.
  • 20 years old e.g., at least about 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,
  • the subject is between about 20 to about 60 years old. In some examples, the subject is between about 30 to about 40 years old. In some examples, the subject is between about 35 to about 40 years old. In some examples, the subject is between about 40 to about 50 years old. In some examples, the subject is between about 20 to about 30 years old. In some examples, the subject is between about 25 to about 30 years old. In some examples, the subject is between about 30 to about 50 years old.
  • the one or more selection criteria includes previous or ongoing treatment with a lipid lowering therapy, such as a statin (e.g., a HMG-CoA reductase inhibitor), a cholesterol absorption inhibitor, a bile acid sequestrant, a PCSK9 inhibitor, an adenosine triphosphate-citrate lyase inhibitor and/or a fibrate.
  • a statin e.g., a HMG-CoA reductase inhibitor
  • a cholesterol absorption inhibitor e.g., a cholesterol absorption inhibitor
  • a bile acid sequestrant e.g., a bile acid sequestrant
  • PCSK9 inhibitor e.g., an adenosine triphosphate-citrate lyase inhibitor
  • fibrate e.g., a lipid lowering therapy.
  • the one or more selection criteria includes one or more preexisting conditions or comorbidities, such as diabetes, a renal disease, disorder or condition and a cardiovascular disease, disorder or condition (e.g., atherosclerosis, peripheral artery disease, heart failure, coronary artery disease).
  • a cardiovascular disease, disorder or condition e.g., atherosclerosis, peripheral artery disease, heart failure, coronary artery disease.
  • the subject described herein does not have diabetes, inclusive of Type I and Type II diabetes.
  • the subject described herein does not have a renal disease, disorder or condition.
  • the subject described herein does not have a cardiovascular disease, disorder or condition. Suitable screening tests to determine the presence or absence of such diseases, disorders or conditions are well known to the skilled person.
  • the one or more selection criteria includes breast density.
  • Breast density may be assessed by any means known in the art and may be indicated by a breast density score.
  • the American College of Radiology developed an index which ranks breast density from 1 to 4 or A to D ranging from fatty to dense (i.e., Score 1/Type A: Fatty tissue; Score 2/Type B: Scattered fibroglandular; Score 3/Type C: Heterogeneously dense; Score 4/Type D: Dense tissue).
  • the subject described herein has a breast density score of 3 or less (i.e., has a breast density score ranging from 1 to 3 or Type A, B or C).
  • kits for the detection of lipid biomarkers that may be suitable for use in the methods described herein.
  • the present disclosure provides a kit for determining the presence or absence of a breast cancer in a subject, the kit comprising one or more reagents for determining a level of one or more lipid biomarkers in a biological sample obtained from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0
  • kits will typically include labels, secondary antibodies, inhibitors, co-factors and control lipid product preparations to allow the user to quantitate concentration or expression levels and/or to assess whether the measurement has worked correctly.
  • Biosensors including optical (e.g., SPR- based sensors, interferometry-based sensors, waveguide-based sensors), electrochemical and mechanical biosensors are particularly suitable assays that can be carried out easily by the skilled person using kit components.
  • the kit may comprise a substrate, such as a microtitre plate, on which is immobilised capture probes or antibodies corresponding to the lipid biomarkers being measured.
  • the kit comprises beads on which is immobilised capture probes or antibodies corresponding to the lipid biomarkers being measured.
  • the kit further comprises means for the detection of the binding of a probe, such as an antibody, to a lipid biomarker.
  • Such means include a reporter molecule such as, for example, an enzyme (such as horseradish peroxidase or alkaline phosphatase), a dye, a radionucleotide, a luminescent group, a chemiluminescent group, a fluorescent group, biotin or a colloidal particle, such as colloidal gold or selenium.
  • a reporter molecule such as, for example, an enzyme (such as horseradish peroxidase or alkaline phosphatase), a dye, a radionucleotide, a luminescent group, a chemiluminescent group, a fluorescent group, biotin or a colloidal particle, such as colloidal gold or selenium.
  • a reporter molecule is directly linked to the antibody.
  • a kit may additionally comprise or the one or more reagents thereof may comprise a reference sample or a reference standard, such as for one or more of the lipid biomarkers described herein.
  • a reference sample comprises a lipid that is detected by an antibody and/or may be labelled or modified so as to be distinguished from native lipid.
  • the lipid in the reference is of known concentration.
  • a lipid is of particular use as a standard or reference standard of the one or more lipid biomarkers described herein. Accordingly, various known concentrations of such a lipid may be detected using a diagnostic assay described herein.
  • such reference samples or reference standards are for use by mass spectrometry-based methods of measuring a level of a lipid biomarker, such as those described herein.
  • kits of the present disclosure are typically written instructions on a label or package insert (e.g., a paper sheet included in the kit), but machine-readable instructions (e.g., instructions carried on a magnetic or optical storage disk) are also acceptable.
  • the instructions relating to the use of the reagents described herein generally include information as to determining a concentration or expression level of the one or more lipid biomarkers and guidance regarding dosage, dosing schedule, and route of administration for an indicated treatment.
  • the kit may further comprise a description of selecting an individual having breast cancer and thereby suitable for treatment.
  • the reference data is on a computer-readable medium (e.g., software embodying or utilized by any one or more of the methodologies or functions described herein).
  • the computer-readable medium can be included on a storage device, such as a computer memory (e.g., hard disk drives or solid state drives) and may comprise computer readable code components that when selectively executed by a processor implements one or more aspects of the present disclosure.
  • a storage device such as a computer memory (e.g., hard disk drives or solid state drives) and may comprise computer readable code components that when selectively executed by a processor implements one or more aspects of the present disclosure.
  • a storage device such as a computer memory (e.g., hard disk drives or solid state drives) and may comprise computer readable code components that when selectively executed by a processor implements one or more aspects of the present disclosure.
  • a storage device such as a computer memory (e.g., hard disk drives or solid state drives) and may comprise computer readable code components that when selectively executed by a processor implements one or more aspects of the present disclosure.
  • the present disclosure provides a system for determining the presence or absence of a breast cancer in a subject, the system comprising: a mass spectrometry unit configured for determining a level of one or more lipid biomarkers in a biological sample obtained from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(
  • the step of determining a concentration level or an expression level may be performed by the mass spectrometry units and/or may be performed at least in part by a pre- processing unit.
  • That pre-processing unit may be the same as or different to the processing unit performing the steps of analysing the concentration or expression level.
  • the pre- processing unit may receive data from the mass spectrometry unit indicative of a number of fragments (e.g., lipid headgroup and/or acyl chain) of the lipid biomarkers for respective mass values. This data may also be representative of the retention time of particular fragments. The pre-processing unit may then process this data to determine lipid biomarkers as combinations of fragments to thereby calculate the corresponding concentration or expression levels.
  • the mass spectrometry unit and the processing unit are that described herein.
  • Computer-implemented methods It is envisaged that one or more steps of the methods described herein may be automated or implemented by a computer in the sense that the disclosed methods are implemented as software code that is stored on a non-volatile data storage medium.
  • the computer executes the software code, which causes the computer to perform the methods disclosed herein.
  • comparing a concentration level or an expression level of the one or more lipid biomarker with, for example, a reference or threshold level or value may be carried by a computer executing software code describing the comparing step.
  • the comparison may be carried out by a computer or computing device, such as by a processing unit.
  • the value of the determined or detected amount of the one or more lipid biomarkers in the sample from the subject and the reference amount can be, for example, compared to each other and said comparison can be automatically carried out by a computer program executing an algorithm for the comparison. Additionally, the calculation of a risk or diagnostic score and/or its comparison to a reference risk or diagnostic score can be automatically carried out by a computer program executing an algorithm for the comparison. Suitably, such algorithms may be trained on one or more case and/or control samples. In some examples, a processor may utilize the concentration or expression level data and/or a risk or diagnostic score to calculate a likelihood of the subject in question having a breast cancer. The computer program carrying out the evaluation will suitably provide the desired assessment in a suitable output format.
  • the value of the determined amount may be compared to values corresponding to suitable references, which are stored in a database by a computer program.
  • the computer program may further evaluate the result of the comparison, i.e. automatically provide the desired assessment in a suitable output format.
  • the methods of the disclosure include one or more of the broad steps of: (i) optionally performing a measurement of the concentration or expression level of the one or more lipid biomarkers described herein; (ii) inputting or receiving the values from (i) into a processing system that is configured to determine the presence or absence of a breast cancer in a subject; (iii) optionally calculating a risk or diagnostic score from the level or expression level of the one or more lipid biomarkers by the processing system; (iv) comparing the concentration or expression level and/or the risk or diagnostic score obtained in step (iii) with a threshold value by the processing system; (v) determining the presence or absence of the breast cancer in the subject; and (vi) optionally providing a treatment for the breast cancer if present in the subject.
  • the present disclosure contemplates a method of allowing a user to determine the status (e.g., the presence or absence of a breast cancer) of a subject, the method including the steps of: (a) receiving data in the form of concentration or expression levels of one or more lipid biomarkers for a test sample, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(
  • the above method further includes the step of producing or generating the concentration level or expression level data by determining a concentration level or an expression level of one or more lipid biomarkers in the biological sample obtained from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0_18:2)), PC(36:4) (e.
  • the method additionally includes: (a) having a user determine the data using a remote end station; and (b) transferring the data from the end station to a base station via a communications network.
  • the base station can include first and second processing systems, in which case the method can include: (a) transferring the data to the first processing system; (b) causing the first processing system to perform a univariate or multivariate analysis function to generate the risk or diagnostic score.
  • the method may also include: (a) transferring the results of the univariate or multivariate analysis function and/or the determined concentration or expression levels of the lipid biomarkers to the second processing system; and (b) causing the second processing system to determine the status of the subject.
  • the second processing system may be coupled to a database adapted to store predetermined data and/or the univariate or multivariate analysis function, such that the computer-implemented method may include: (a) querying the database to obtain at least selected predetermined data or access to the univariate or multivariate analysis function from the database; and (b) comparing the selected predetermined data to the subject data or generating a predicted probability index.
  • the second processing system can be coupled to a database, the method including storing the data in the database, such as by way of a memory unit.
  • the reference concentration or expression level data comprises a level or a level of concentration or expression determined for the one or more lipid biomarkers within a biological sample selected from the group consisting of: (i) a biological sample from a normal or healthy subject, such as normal or healthy subject without breast cancer; (ii) a biological sample from a subject previously diagnosed or determined as having a breast cancer; (iii) an extract of any one of (i) to (ii); (iv) a data set comprising levels of concentration or expression for the lipid biomarkers within a normal or healthy individual or a population of normal or healthy individuals; (vi) a data set comprising levels of concentration or expression for the lipid biomarkers in an individual or a population of individuals having breast cancer; and (vii) a data set comprising levels of concentration or expression for the lipid biomarkers in the subject being tested wherein the levels of concentration or expression are determined for a sample having been taken at an earlier time point when the subject was known to not have breast cancer.
  • the methods disclosed herein may further include the initial or earlier step of providing or collecting a biological sample from the subject that suitably contains lipid micro-vesicles or extracellular vesicles, such as a liquid biopsy.
  • a biological sample may be obtained by freshly collecting a sample, or may be obtained from a previously collected and stored sample.
  • a sample may be obtained from a previously collected and stored (e.g., refrigerated or frozen) blood, plasma or serum.
  • a sample is obtained by freshly collecting a sample from the subject.
  • a sample can be obtained from a previously collected and stored sample from the subject.
  • the subject has fasted prior to collection or is in a fasted state at the time of collection of the biological sample for further testing by the methods provided herein.
  • fasted refers to the condition of not having consumed food or beverage during the period between from at least about 3 hours to about 12 hours (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 hours and any range therein) prior to providing a biological sample for testing.
  • the subject has not fasted prior to collection or is in a non-fasted (or “fed”) state at the time of collection of the biological sample for further testing by the methods provided herein.
  • non-fasted refers to the condition of having consumed food and/or beverage within at least about 3 hours to about 12 hours (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 hours and any range therein) of providing a biological sample for testing.
  • Suitable samples comprise a concentration of lipid micro-vesicles or extracellular vesicles.
  • a suitable sample may also comprise circulating lipid micro-vesicles or extracellular vesicles. Circulating lipid micro-vesicles or extracellular vesicles may be found in a bodily fluid (e.g., blood, plasma, serum, urine, vomit, tears, sputum etc.) or other excrement (e.g., faeces).
  • the biological sample is or comprises blood, plasma or serum.
  • the biological sample e.g., blood, plasma and/or serum
  • the biological sample has been enriched for extracellular vesicles and/or a lipid content thereof.
  • the biological sample is or comprises plasma.
  • the biological sample is or comprises serum.
  • extracellular vesicle or “EV” refers to a cell-derived vesicle comprising a membrane that encapsulates an interior space. Extracellular vesicles include all membrane-bound vesicles (e.g., exosomes, nanovesicles) whose diameter is typically smaller than the diameter of the cell from which they are derived.
  • the biological sample may be subject to any suitable pre-treatment steps before measurement of the level of the one or more lipid biomarkers is performed, in order to improve the accuracy and/or efficiency of the measurement.
  • pre-treatment steps may include extraction, centrifugation (e.g., ultracentrifugation), lyophilization, fractionation, separation (e.g., using column or gel chromatography), concentration or evaporation.
  • this treatment can include one or more extractions with solutions comprising any suitable solvent or combinations of solvents, such as, but not limited to acetonitrile, water, chloroform, methanol, butylated hydroxytoluene, trichloroacetic acid, toluene, hexane, benzene, or combinations thereof.
  • the biological sample may undergo one or more treatment steps so as to isolate, concentrate or enrich for extracellular vesicles and/or a lipid content thereof.
  • the biological sample is an extracellular vesicle (EV) sample or is a biological sample, such as a plasma sample, serum sample or blood sample, that has been enriched for extracellular vesicles.
  • EV extracellular vesicle
  • any of the methods disclosed herein may comprise a step of taking a biological sample from a subject and determining the level of expression, concentration or abundance of the one or more lipid biomarkers in the sample.
  • any of the methods disclosed herein may not comprise a step of taking a biological sample from a subject and/or determining the level of expression, concentration or abundance of the one or more lipid biomarkers in the sample.
  • the biological sample may have already been taken from the subject and/or the level of expression, concentration or abundance of the one or more lipid biomarkers in the sample may have been determined previously. So that preferred embodiments of the present disclosure may be fully understood and put into practical effect, reference is made to the following non-limiting examples. Examples Example 1.
  • Plasma- and EV-derived lipidomics biomarker discovery The aim of the present Example was to identify a multivariate plasma-derived lipid biomarker signature that has high predictive power in stratifying controls and patients with breast cancer while being highly robust with respect to sample heterogeneities.
  • the process was replicated using EV lipidome on the same set of subjects to obtain an EV-derived lipid signature and assess the predictive performance of plasma-based signature compared to the EV signature.
  • a signature of 20 or 30 lipids was identified using a robust, statistically rigorous feature selection algorithm based on the concept of random forest feature importance. These lipids were used to train an ensemble of 18 artificial intelligence (AI) or machine learning (ML) which are used to jointly predict disease status.
  • AI artificial intelligence
  • ML machine learning
  • FIG. 1 illustrates the pipeline developed for signature panel identification and predictive model development, which involves 2000 iterations of leave-group-out cross-validation, LGOCV (80% train, 20% test) to provide a high level of confidence in the generalisability of the results. Within each iteration, feature selection was performed, 18 classification models were trained, and performance was assessed on the held-out test data. Preprocessing During the data exploration phase, lipid concentrations were determined to exhibit over- dispersion.
  • a log-transformation was used for its variance-stabilising properties. Prior to the log- transformation, a small, randomly selected [1e-6, 1e-5] offset was added to 0 values (i.e. undetected lipids). Signature panel identification Within each iteration of LGOCV, a subset of lipids was selected for use in the predictive modelling module using the 80% training set. These lipids were selected by Boruta, a robust, statistically rigorous feature selection algorithm based on the concept of random forest feature importance [2]. A p-value cut-off of 0.01 (Bonferroni Adjusted) was used to identify consistently important features over 100 iterations with 500 trees per random forest.
  • Predictive modelling A diverse range of 24 predictive classification models provided by the ‘caret’ package was identified for use in training an ensemble model [1]. Within each iteration of LGOCV, the predictive models were provided with the features selected by Boruta for the 80% training set. Hyperparameter selection was performed for each model using a random search with a tuning length of 10 over 50 iterations of a nested LGOCV (splitting the training set further into 80% sub- train and 20% sub-test). Upon selecting the ideal set of hyperparameters, the model was refit using the entire training set. Model validation The optimised models were subsequently validated on the held-out 20% test set. Individual model predictions were obtained for each test sample.
  • FIG. 2b illustrates the top 30 features sorted as per the proportion of selections (/2000).
  • the top 20 lipids (proportion: 80 – 100%, the accuracy of the corresponding signature: 84.6 – 84.8%) were selected as the final signature for training the final ensemble model.
  • Final ensemble model The final ensemble model comprises 18 classifiers, each using the previously described 20 lipid features as its predictive variables. Hyperparameters for each classifier were chosen according to the model, which obtained its median accuracy across runs where the method obtained its best rank. For example, K-nearest neighbours ranked best in 44 out of 2000 runs.
  • the median run was selected to avoid biasing models towards overly difficult or simple testing samples.
  • the final model was then trained using LGOCV (20% test, 80% train) and repeated for another 2000 runs to rigorously evaluate the performance of the final model, adjusting for the selection bias.
  • the average performance of the ensemble model, as well as 18 classifiers (using the 20- lipid signature as predictive variables), across 2000 LGOC iterations is reported in Table 1.
  • the ensemble model can naturally represent the agreement between individual classifiers, which can be used as a measure of prediction ‘certainty’.
  • an absolute certainty i.e., complete agreement
  • in classifying 72.8% of correctly predicted samples was observed, while none of the misclassified samples was predicted with high certainty (Figure 3d).
  • FIG. 5b illustrates the top 30 features sorted as per the proportion of selections (/2000).
  • the top 20 lipids (proportion: 80 – 100%, the accuracy of the corresponding signature: 83.8 – 84.1%) were selected as the final signature for training the final ensemble model.
  • Final ensemble model The final ensemble model comprises 18 classifiers, each using the previously described 20 lipid features as its predictive variables. Hyperparameters for each classifier were chosen according to the model, which obtained its median accuracy across runs where the method obtained its best rank. The median run was selected to avoid biasing models towards overly difficult or simple testing samples.
  • the final model was then trained using LGOCV (20% test, 80% train) and then repeated for another 2000 runs to rigorously evaluate the performance of the final model with respect to the selection bias.
  • the average performance of the ensemble model, as well as 18 classifiers (using the 20- lipid signature as predictive variables), across 2000 LGOC iterations is reported in Table 3.
  • Figure 7 shows that the 20-lipid signature is the optimal size, but increasing or decreasing the signature size does not greatly influence the performance or accuracy of the model.
  • C. Plasma vs EV Comparisons In general, the results of the EV analyses were concordant with those of the Plasma analyses. There was strong overlap between the top 20 lipids selected by each either approach (12 out of 20, Figure 8a), model confidence was slightly higher in the Plasma models (Figure 9b) and model performance was largely similar with both approaches ( Figure 9c).
  • Example 2 Normalization and batch effect removal
  • AS Subjects or
  • WS Within Subjects or
  • CB Control-based
  • Example 3 Fine Tuning Signature and Refining Ensemble This Example was designed to investigate the role of the lipid panel in model performance by replicating the feature selection process conducted during Example 1 using both of the P250 and P550 datasets described in Example 2 and the 80 lipid species present in the P550 dataset. A strong overlap was observed with the previously selected 20-lipid biomarker panel (Figure 10a) while identifying a further 5 lipids (Table 7), which may be useful for improved model performance ( Figure 10b-d). Furthermore, it was observed that a signature containing only 15 biomarkers demonstrated robust diagnostic capabilities.
  • Model diagnostics for models trained on homogeneous cohorts when predicting each of the patient groups The model group indicates which group was used to train the model, and the patient group indicates which group was used for prediction and, therefore, estimation of model diagnostics. Accuracy, F1 Score, Sensitivity, Specificity, and Diagnostic Odds Ratio are reported.
  • Example 5 Training and internal validation of models on selected cohorts In this Example, two additional models were constructed and analysed around selected cohorts in order to complement the previous analyses. These two cohorts are comprised of the EU cancer and P250 patients ( Figure 13a, Table 11b); and EU cancer, Australian fasted control, and P250 patients ( Figure 13b, Table 11c).
  • Models were trained using the original set of 20 lipids identified during Example 1 on the refined ensemble of models in order to yield prediction probability estimates between 0 and 1. These models were tested in order to explore the potential model performance when data with sub-optimal characteristics are excluded from consideration (e.g., fed patients and benign controls). The results suggest a strong improvement over previous observations may be possible.
  • Table 11. Model performance metrics for each of the selected additional cohorts. (a) P250 only, (b) P450 (P250 + EU cancer patients), and (c) P250 + EU cancer + AU control patients.
  • Example 6 The aim of the present Example was to use QQQ-MS to identify a plasma-derived lipid biomarker signature. The samples tested, all collected from European patients, are summarized in Table 12. These samples have been divided into 4 analysis groups (AGs) according to the cohort and year in which they were collected. Demographic information for the 4 AGs is reported in Table 13. Table 12: Sample counts by analysis group, the cohort of origin, year collected, and disease classification
  • Table 13 Demographic information for study samples. Summary: • External validation of the lipid panel optimized on AG2-4 (Mix 0) resulted in an accuracy of 0.69, sensitivity of 0.46, specificity of 0.82, and AUC of 0.75. • External validation of the economy panel (Mix 5) resulted in an accuracy of 0.65, sensitivity of 0.78, specificity of 0.57, and AUC of 0.75 • Analyses performed in addition to those in the planned SAP demonstrated favourable results by reducing the size of the optimized lipid panel.
  • the panel includes acyl-carnitine (AcCa), lyso-glycerophospholipids (LPA, LPC, & LPI), glycerophospholipids (PC, PE, PI, PS), and sphingolipids (ceramides (Cer) and S1P)).
  • AcCa acyl-carnitine
  • LPA lyso-glycerophospholipids
  • PC glycerophospholipids
  • PE glycerophospholipids
  • PE phosphatidylglycerol
  • TG triglycerides
  • Table 15 External validation of ensemble model trained on the optimized panel from outcome 1a stratified by each of the analysis groups Internal validation of Economy lipid panel LOOCV was performed on all patients (AG2-4) using the Economy panel (listed in Table 18). An ensemble of models was trained on all patients except for one and used to predict the held- out patient. This procedure was repeated for each patient and summary performance metrics are provided in Table 16. Table 16. Internal validation (LOOCV) of the Economy panel on AG2-4 External validation of Economy (Mix 5) lipid panel The procedure described above was repeated using the Economy lipid panel for model training. In comparison to the optimized panel, performance of the Economy panel was good.
  • LOOCV Internal validation
  • Table 21 and Figure 19 present the results of quasi-external validation on AG’s 2 and 3.
  • the decrease in performance previously seen in smaller mixes was not observed in this set of validations.
  • Mixes 2 and 7 were strong performers (AUC 0.79 and 0.77, ACC 0.72 and 0.73 respectively).
  • Mixes 0 and 3 while exhibiting the best internal validation results, showed relatively low performance (AUC 0.75 and 0.75, ACC 0.69 and 0.66 respectively).
  • Table 20 External validation results for additional panels on AG1. Results for Mix0 and Mix5 are repeated here for completeness.
  • Pairwise McNemar’s test p-values (unadjusted) comparing quasi-external validation performance (accuracy) between two sets of lipid panels.
  • QQQ-MS vs. HR-MS lipid-wise comparison QQQ-MS and HR-MS quantification methods were compared for each patient which is represented in both datasets (Table 23). Strong correlations were observed in log concentrations for most lipids investigated ( Figures 21 and 22).
  • QQQ-MS vs. HR-MS paired-sample comparison The bootstrapped Boruta procedure detailed above was performed on the subset of patients detailed in Table 23 to obtain a panel of lipids optimized on the same cohort. This was done to allow for a fair, practical comparison of models trained on the two modalities.
  • Table 14 Lipid concentration changes between healthy and diseased individuals in QQQ data (AG1-3) and HR data (P250). P-values have been adjusted using false-discovery-rate (FDR) corrections.
  • Example 7 The aim of the present Example was to use HR-MS to identify a plasma-derived lipid biomarker signature.
  • the samples tested were all collected from Australian women. These samples have been divided into 2 analysis groups based on whether or not they had breast cancer (i.e., 49 control samples and 50 case samples).
  • the blood samples were processed to plasma and acquired using HR-MS. Analysis was performed using LipidSearch and Tracefinder software. Lipid biomarker levels were compared between control and breast cancer plasma samples, with biomarkers showing the greatest differential abundance further analysed for statistical significance.
  • the levels of the lipid biomarkers of Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (i.e., PC(16:0/20:4) isomer) and PI(40:5) were all significantly elevated in breast cancer patients versus case controls and therefore could be useful in diagnostic assays for detecting breast cancer in patients.

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Abstract

The present disclosure relates to methods of diagnosing and treating breast cancer.

Description

"Diagnostic signature" Cross-reference to related applications The present application claims priority from Australian Provisional Patent Application No. 2023903491 filed on 31 October 2023 and US Provisional Patent Application No.63/712,255 filed on 25 October 2024, the contents of which are incorporated herein by reference in their entirety. Technical Field The present disclosure relates to methods of diagnosing and treating breast cancer. Background In 2020, 2.3 million women were diagnosed with breast cancer and there were 685000 deaths globally. At the end of 2020, there were 7.8 million women alive who had been diagnosed with breast cancer in the preceding 5 years, making it the world’s most prevalent cancer (World Health Organisation). Although breast cancer is mostly a disease of females, 1 in 1100 males may also develop the disease (Society, 2016). The key to surviving breast cancer is early detection and treatment. The current gold standard for detection is via mammogram however, it is known to be less effective at younger ages. Accordingly, there remains a need for a more accurate screening test for breast cancer for women of all ages, such as to detect the cancer at a cellular level and before metastasis (Mistry and French, 2016). Summary The present disclosure is based on the surprising discovery of a number of lipid biomarkers, which can readily be detected, such as in a liquid biopsy, in order to diagnose breast cancer in women, as well as rule out a woman having breast cancer. By extension, these lipid biomarkers (or lipidomic signatures) demonstrate promise in identifying patients that require treatment for breast cancer. In a first aspect, the present disclosure provides a method of diagnosing a subject with a breast cancer, said method including the step of measuring a level of one or more lipid biomarkers in a biological sample from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof, and wherein the level of the one or more lipid biomarkers is diagnostic or indicative of the subject having the breast cancer. According to some examples, an increased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4) isomer), PC(38:5), PI(40:5), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3) and/or SM(d42:4), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) isomer), PC(36:5e), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and/or TG(60:5), or a fragment, variant or derivative thereof, is diagnostic or indicative of the subject having the breast cancer. The present method may further include the step of administering a treatment for the breast cancer to the subject. In a second aspect, the present disclosure provides a method for measuring a level of one or more lipid biomarkers in a biological sample from a subject, said method including the steps of: (a) providing the biological sample; and (b) measuring the level of the one or more lipid biomarkers in the biological sample, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. Suitably, for the present method the subject is suspected of having a breast cancer or has been previously diagnosed with a breast cancer. Referring to particular examples, the measuring step includes determining the presence or absence of: (i) an increased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)), PC(38:5), PI(40:5), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3) and/or SM(d42:4), or a fragment, variant or derivative thereof, and/or (ii) a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2)), PC(36:5e), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and/or TG(60:5), or a fragment, variant or derivative thereof, in the biological sample of the subject. In a third aspect, the present disclosure relates to a method of treating a breast cancer in a subject, said method including the step of performing a treatment in respect of the subject in which a level of one or more lipid biomarkers has been measured in a biological sample therefrom that is diagnostic or indicative of the subject having the breast cancer, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. Suitably, the treatment includes administering a therapeutically effective amount of an anti- cancer treatment to the subject. In certain examples of the present method, an increased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)), PC(38:5), PI(40:5), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3) and/or SM(d42:4), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2)), PC(36:5e), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and/or TG(60:5), or a fragment, variant or derivative thereof, was measured from the biological sample of the subject. Suitably, and referring to the aforementioned methods, the one or more lipid biomarkers comprise LPC(14:0), or a fragment, variant or derivative thereof, and one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. For such examples, the one or more other lipid biomarkers may be selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. More particularly, the one or more other lipid biomarkers may be selected from the group consisting of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and SM(d36:2), or a fragment, variant or derivative thereof. Even more particularly, the one or more other lipid biomarkers may be selected from the group consisting of Cer(d36:1), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)) and SM(d36:2), or a fragment, variant or derivative thereof. According to some examples of the above methods, the one or more lipid biomarkers suitably comprise PI(38:6), or a fragment, variant or derivative thereof, and one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. For various examples of the above methods, the one or more lipid biomarkers can comprise LPC(14:0) and PI(38:6), or a fragment, variant or derivative thereof, and optionally one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. In other examples of the above methods, the one or more lipid biomarkers are suitably selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2), TG(50:1e), TG(52:3e), TG(56:0), TG(56:1), TG(58:2) and TG(60:5), or a fragment, variant or derivative thereof. For some examples of the aforementioned aspects, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) and TG(52:3e), or a fragment, variant or derivative thereof. Referring to certain examples of the above methods, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2), TG(52:3e) and TG(56:1), or a fragment, variant or derivative thereof. In particular examples of the above methods, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(44:2), TG(52:3e) and TG(58:2), or a fragment, variant or derivative thereof. According to various examples of the aforementioned methods, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d37:1), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), TG(51:0), TG(52:3e), TG(53:0), TG(58:1), TG(58:2) and TG(59:1), or a fragment, variant or derivative thereof. For some examples of the above methods, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d39:2), SM(d41:2), TG(52:3e), TG(53:0), TG(58:1) and TG(58:2), or a fragment, variant or derivative thereof. In other examples of the aforementioned methods, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(18:3), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2p), PE(38:6e), PE(40:6e), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), SM(d38:4) and SM(d42:4), or a fragment, variant or derivative thereof. Referring to particular examples of the above methods, the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(38:5), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. More particularly, the one or more lipid biomarkers can comprise, consist of or consist essentially of: (a) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1); (b) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); (c) Cer(d36:1), LPA(18:0), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PS(38:4), SM(d36:2) and SphP(d18:1); (d) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1); (e) Cer(d36:1), Cer(d42:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), PC (36:0), PC(36:2) (such as PC(18:0_18:2) and/or PC(18:1_18:1)), PE(34:1), PI(34:1), SM(d36:2) and SphP(d18:1); (f) AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1); (g) AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); (h) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); (i) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1); (j) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1); or (k) Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1); or a fragment, variant or derivative thereof. Referring to various examples of the aforementioned methods, the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. More particularly, the one or more lipid biomarkers may be selected from the group consisting of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and SM(d36:2), or a fragment, variant or derivative thereof. Even more particularly, the one or more lipid biomarkers may be selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)) and SM(d36:2), or a fragment, variant or derivative thereof. Still even more particularly, the one or more lipid biomarkers may be selected from the group consisting of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. Yet even more particularly, the one or more lipid biomarkers may be selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1); or a fragment, variant or derivative thereof. For the methods of the above aspects, the level of the one or more lipid biomarkers suitably is or has been measured, at least in part, by mass spectrometry. In some examples of the above methods, the predictive accuracy thereof, as determined by an ROC AUC value, is at least about 0.65, at least about 0.70, at least about 0.75 or at least about 0.80. In a fourth aspect, the present disclosure provides a system for determining the presence or absence of a breast cancer in a subject, the system comprising: a mass spectrometry unit configured for determining a level of one or more lipid biomarkers in a biological sample obtained from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof; and a processing unit configured for using or analysing the level of the one or more lipid biomarkers to determine the presence or absence of the breast cancer in the subject. In a fifth aspect, the present disclosure provides a kit for determining the presence or absence of a breast cancer in a subject, the kit comprising one or more reagents for determining a level of one or more lipid biomarkers in a biological sample obtained from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. Suitably, the one or more reagents comprise one or more probes, each probe being specific or selective for one of the one or more lipid biomarkers. In particular examples of the aforementioned aspects, the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. More particularly, the one or more lipid biomarkers of the above aspects suitably comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2), TG(50:1e), TG(52:3e), TG(56:0), TG(56:1), TG(58:2) and TG(60:5), or a fragment, variant or derivative thereof. In other examples of the above aspects, the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), LPC(16:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) and TG(52:3e), or a fragment, variant or derivative thereof. Referring to certain examples of the above aspects, the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2), TG(52:3e) and TG(56:1), or a fragment, variant or derivative thereof. According to some examples of the above aspects, the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), LPC(16:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(44:2), TG(52:3e) and TG(58:2), or a fragment, variant or derivative thereof. For various examples of the above aspects, the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d37:1), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), TG(51:0), TG(52:3e), TG(53:0), TG(58:1), TG(58:2) and TG(59:1), or a fragment, variant or derivative thereof. In particular examples of the above aspects, the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d39:2), SM(d41:2), TG(52:3e), TG(53:0), TG(58:1) and TG(58:2), or a fragment, variant or derivative thereof. Referring to various examples of the above aspects, the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), LPC(18:3), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2p), PE(38:6e), PE(40:6e), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), SM(d38:4) and SM(d42:4), or a fragment, variant or derivative thereof. According to other examples of the above aspects, the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(38:5), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. For particular examples of the above aspects, the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of: (a) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1); (b) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); (c) Cer(d36:1), LPA(18:0), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PS(38:4), SM(d36:2) and SphP(d18:1); (d) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1); (e) Cer(d36:1), Cer(d42:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), PC (36:0), PC(36:2) (such as PC(18:0_18:2) and/or PC(18:1_18:1)), PE(34:1), PI(34:1), SM(d36:2) and SphP(d18:1); (f) Cer(d36:1), Cer(d42:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), PC (36:0), PC(18:0_18:2), PC(18:1_18:1)), PE(34:1), PI(34:1), SM(d36:2) and SphP(d18:1); (g) AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1); (h) AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); (i) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); (j) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1); (k) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1); or (l) Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1); or a fragment, variant or derivative thereof. In various examples of the above aspects, the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. More particularly, the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and SM(d36:2), or a fragment, variant or derivative thereof. Even more particularly, the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)) and SM(d36:2), or a fragment, variant or derivative thereof. Still even more particularly, the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. Yet even more particularly, the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. Suitably, the biological sample of the above aspects is or comprises a blood sample, a plasma sample, a serum sample and/or an extracellular vesicle (EV) sample. Suitably, the system of the fourth aspect or the kit of the fifth aspect are suitable for use in the method of the first, second or third aspects. Brief description of the drawings The following figures form part of the present specification and are included to further demonstrate certain aspects of the present disclosure. The disclosure may be better understood by reference to one or more of these figures in combination with the detailed description of specific embodiments presented herein. It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the above-described embodiments, without departing from the broad general scope of the present disclosure. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive. Figure 1. Artificial intelligence (AI) biomarker discovery pipeline. Figure 2. Plasma discovery phase; a) Average prediction of each model for individual patients across 2000 runs. Purple represents that the sample is predicted as being a control in all runs, while Yellow represents consistent predictions of breast cancer across runs. The spectrum of colours between purple and yellow represents the proportion of inconsistencies across runs; b) Lipids that are consistently selected as being important by the Boruta algorithm across all runs. The top 20 lipids were selected as robust biomarkers and the final signature of the breast cancer diagnosis. Lipid identities (LIDs) are as per the Tables provided herein. Figure 3. Plasma final ensemble model performance. a) Boxplots representing the distribution of different performance metrics (accuracy, F1-score, precision or positive predictive value, sensitivity and specificity. b) ROC curve resulting from adjusting the voting threshold between 0 and 1 (e.g., at 0.25, a patient is predicted to have cancer if at least 25% of the individual models also predicted disease status). c) Certainty level of predictions on correctly classified versus misclassified samples. High: Complete model agreement, Medium: Greater than 80% model agreement, Low: Less than 80% model agreement. Figure 4. Plasma ensemble model accuracy as the number of lipids is increased/decreased. The violin plots represent the distribution of the ensemble model accuracy such that the top 14 to top 30 lipids were selected (selection based on Figure 2b). Horizontal lines within each violin represent the 0.05, 0.5, and 0.95 quantiles for prediction accuracy. Figure 5. EV discovery phase; a) Average prediction of each model for individual patients across 2000 runs. Purple represents that the sample is predicted as being a control in all runs, while Yellow represents consistent predictions of breast cancer across runs. The spectrum of colours between purple and yellow represents the proportion of inconsistencies across runs; b) Lipids that are consistently selected as being important by the Boruta algorithm across all runs. The top 20 lipids were selected as robust biomarkers and the final signature of the breast cancer diagnosis. Lipid identities (LIDs) are as per the Tables provided herein. Figure 6. EV performance metrics. a) Boxplots representing the distribution of different performance metrics (accuracy, F1-score, Precision or positive predictive value, sensitivity and specificity. b) ROC curve resulting from adjusting the voting threshold between 0 and 1 (e.g., at 0.25, a patient is predicted to have cancer if at least 25% of the individual models also predicted disease status. c) Certainty level of predictions on correctly classified versus misclassified samples. High: Complete model agreement, Medium: Greater than 80% model agreement, Low: Less than 80% model agreement. Figure 7. EV ensemble model accuracy as the number of lipids is increased/decreased. The violin plots represent the distribution of the ensemble model accuracy such that the top 14 to top 30 lipids were selected (selection based on Figure 6b). Horizontal lines within each violin represent the 0.05, 0.5, and 0.95 quantiles for prediction accuracy. Figure 8. Comparison of Plasma and EV analyses. a) Lipids that are consistently selected as being important by the Boruta algorithm across all EV runs. Light blue indicates that this lipid was unique to the top 30 selection during the EV analysis, red indicates that this lipid was present in the top 10 selection of the Plasma analysis, and yellow indicates that this lipid was present in the top 11-20 selection of the Plasma analysis, and green indicates that this lipid was present in the top 21-30 selection of the Plasma analysis b) Boxplots representing the distribution of different performance metrics (accuracy, F1-score, Precision or positive predictive value, sensitivity and specificity. c) Certainty level of predictions on correctly classified versus misclassified samples. High: Complete model agreement, Medium: Greater than 80% model agreement, Low: Less than 80% model agreement. Lipid identities (LIDs) are as per the Tables provided herein. Figure 9. Summary of the top-performing normalization methods and their ability to remove batch effects that are present in the data. (a) Bar plots indicating model performance when training on the P250 dataset and predicting the P550 dataset; blue: batched normalization methods, red: non-batched normalization methods (either across or within a sample), purple: selected/preferred methods (control-based median and control-based trimmed z-score), green: non-normalized data (log-transformed only). Different performance metrics are presented from top to bottom: accuracy, sensitivity (TPR), and specificity (TNR). (b-c) Per-lipid boxplots (b) and UMAP projections (c) comparing differences between patient groups and disease status; red: P250 cancer patients, green: P250 control patients, blue: P550 cancer patients, purple: P550 control patients. Select normalization approaches are compared from top to bottom: log-only (no normalization applied), control-based median normalization, and control-based trimmed z-score normalization. Lipid identities (LIDs) are as per the Tables provided herein. Figure 10. Summary results of further fine-tuning of the lipid biomarker panel. (a) Boruta was repeatedly run on a random 50% split of the entire dataset (P250 + P550) using the 80 lipid species provided in the P550 dataset. The bar plot shows the proportion of times that each lipid was identified as being relevant to the prediction. Blue bars indicate lipids that were not present in the lipid biomarker panel identified during SoW1 using the P250 dataset only, while red bars indicate lipids that were previously identified as being relevant to the prediction task. (b) Boxplots comparing model performance across 200 iterations of 80% LGOCV between the refined 15 lipid signature (red) and the original 20 lipid signature (blue). From left to right: accuracy, sensitivity (TPR), and specificity (TNR). (c-d) ROCs for each of the 200 iterations 80% LGOCV for the refined 15 lipid signature (c) and the original 20 lipid signature (d). The average and standard deviation of the AUC is reported in each plot. Lipid identities (LIDs) are as per the Tables provided herein and LID270 = PC(30:0), LID293 = PC(36:3), LID382 = SM(d36:2), LID67 = SM(d40:3), LID340 = PC(O-36:6), LID14 = TG(54:5). Figure 11. Summary performance of homogenous models. For each cohort (AU-FED, EU, and P250), a separate ensemble model was trained. 200 iterations of 80% Leave-Group-Out Cross- Validation (LGOCV) were performed in order to understand the internal predictive performance of each model. Additionally, the refined 15-lipid signature (see Figure 10) was compared to the original 20-lipid signature. (a) Boxplots presenting model performance metrics across the 200 iterations of 80% LGCOV; red: models trained on the refined 15-lipid signature; blue: models trained on the original 20-lipid signature. The x-axis labels (group) indicate the patient group which was used for training and prediction. From left to right, accuracy, sensitivity (TPR), and specificity (TNR) are reported. (b-c) ROCs for each of the 200 iterations of 80% LGOCV for the original 20-lipid signature (b) and the refined 15-lipid signature (c). Patient groups used to train and test the model are presented from left to right: AU (fed), EU, and P250. The average and standard deviation of the AUC is reported in each plot. Figure 12. Summary performance of an ensemble of the homogeneous models (see Figure 11) for prediction of the entire dataset (P250 + AU (fed) + EU).200 iterations of 80% LGOCV were performed. For each iteration, homogeneous models were trained for each of the patient groups (P250, AU (fed), and EU). Each of these 3 models was then used to predict samples in the held-out 20% test set. The prediction for the ensemble of these models was obtained by averaging the 3 homogeneous models and thresholding at 0.5. (a) ROCs for the averaged probability ensemble of homogeneous models stratified by each of the patient groups (red: all patients, green: AU (fed) patients, blue: EU patients, purple: P250 patients. ROCs shown are produced by aggregating predictions across all of the 200 iterations of 80% LGOCV. (b) Boxplots comparing the model performance of each of the homogeneous models on each of the patient groups. The “model group” on the x-axis indicates which group was used to train the model, while the “patient group” indicates which group prediction metrics are reported for. Different model performance metrics are reported in each of the subplots from left to right: accuracy, sensitivity (TPR), and specificity (TNR). (c) Heatmaps presenting patient-level predictions for each patient group and disease status. The score indicates the prediction of the model, with values closer to 1 (yellow) predicting cancer while values closer to 0 (purple) predicting control. Different models are reported in each column from left to right: the averaged ensemble of homogeneous models, the AU (fed) homogenous model, the EU homogeneous model, and the P250 homogeneous model. The top row of heatmaps shows predictions for cancer patients, while the bottom row of heatmaps shows the prediction for control patients. The columns, from left to right, show different patient groups: AU (fed), EU, and P250. Figure 13. Model diagnostic plots for additional selected cohorts under study.200 iterations of 80% LGOCV were performed, and model performance metrics were calculated for each iteration. The leftmost subplots provide boxplots of accuracy, sensitivity (true positive rate), and specificity (true negative rate), and the rightmost subplots provide ROCs for each iteration. (a) P450 (P250 + EU cancer patient) and (b) P250 + EU cancer + AU control patients. Figure 14.20-lipid signature identified using AG2-4. (a) lipids that are consistently selected as important by the Boruta algorithm across all runs. The top 20 lipids were selected (frequency > 70%) as robust biomarkers and the final signature of the breast cancer diagnosis. (b) UMAP of samples based on the 20 selected lipids (c) boxplots of log concentrations of the 20-lipid panel stratified by disease state (red – cancer, blue – control) (d) correlation of the 20 lipid panel identified by Boruta. Lipid identities (LIDs) are as per the Tables provided herein and LID111 = PI(38:4), LID382 = SM(d36:1), LID271 = LPE(22:6), LID103 = TG(54:4), LID270 = PC(30:0), LID135 = PE(O-38:5), LID361 = PE(O-38:4). Figure 15. (quasi-)External validation of the optimized lipid panel (a) Receiver operator curves (ROCs) for each of the 3 validation cohorts (b) Scatterplot of a UMAP embedding of the optimized panel coloured by analysis group and disease state indicated by shape. Figure 16. (quasi-)External validation of the Economy lipid panel (a) Receiver operator curves (ROCs) for each of the 3 validation cohorts (b) Scatterplot of a UMAP embedding of the optimized panel coloured by analysis group and disease state indicated by shape. Figure 17. Internal validation ROC curves for Mixes 0 – 10 on AG2-4 with associated AUCs Figure 18. External validation ROC curves and associated AUCs for additional panels on AG1. Results for Mix0 and Mix5 are repeated here for completeness. Figure 19. Quasi-external validation. ROC curves and AUCs of mixes on AG2 (a) and AG 3 (b). Figure 20. Patient-level scores for each of the lipid panels for the predictions obtained during (quasi)-external validation on AG’s 1-3 stratified by AG, disease state, and cancer stage. Figure 21. Bar plot correlations between HR and QQQ log-concentrations Lipid identities (LIDs) are as per the Tables provided herein and LID270 = PC(30:0), LID438 = TG(58:3), LID267 = PC(32:1); LID14 = TG(54:5), LID144 = PE(36:4), LID271 = LPE(22:6), LID103 = TG(54:4), LID135 = PE(O-38:5), LID276 = LPE(18:0), LID111 = PI(38:4), LID382 = SM(d36:2), LID430 = TG(57:1), LID63 = SM(d42:1), LID361 = PE(O-38:4) Figure 22. QQQ-MS vs. HR-MS lipid-wise comparison (log concentration) of (a) three highly correlated lipids and (b) three lipids with low correlations. Points are coloured by disease state of the sample (blue – control, red – cancer). PCC – Pearson Correlation Coefficient. Correlation plots for all lipids can be found in the markdown documents. Lipid identities (LIDs) are as per the Tables provided herein and Figure 21. Figure 23. Comparison of prediction scores on QQQ and HR-MS data points are coloured by disease state (blue – control, red – cancer). PCC – Pearson Correlation Coefficient (a – P250 optimized, b –Economy). Figure 24. Stacked Plots Illustrating Prediction Confidence Levels. Stacked plots represent the confidence levels of predictions, which are determined based on the range of prediction probabilities. Probabilities within the range of 0 to 1 were grouped into 6 equal bins. The top/bottom bins indicate high confidence, the second top/bottom bins indicate medium confidence, and the remaining middle bins centered around 0.5 represent low confidence. Within each confidence category, predictions are further stratified into true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). The plot above showcases confidence stack bars for Mix7, Mix0, and Mix5, aggregated across AGs 1-3 during (quasi-)external validations. Figure 25. Plasma lipid concentrations from donors in Australia (case v control) acquired using high-resolution MS. Lipid concentrations (in micromolar) have been log10 transformed. Differences between case v control were compared by performing a t-test for each lipid. case = breast cancer. Detailed description General Techniques and Definitions Unless specifically defined otherwise, all technical and scientific terms used herein shall be taken to have the same meaning as commonly understood by one of ordinary skill in the art (e.g. in genomics, immunology, molecular biology, immunohistochemistry, biochemistry, oncology, and pharmacology). The present disclosure is performed using, unless otherwise indicated, conventional techniques of molecular biology, microbiology, recombinant DNA technology and immunology. Such procedures are described, for example in Sambrook, Fritsch & Maniatis, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratories, New York, Fourth Edition (2012), whole of Vols I, II, and III; DNA Cloning: A Practical Approach, Vols. I and II (D. N. Glover, Second Edition., 1995), IRL Press, Oxford, whole of text; Oligonucleotide Synthesis: A Practical Approach (M. J. Gait, ed, 1984) IRL Press, Oxford, whole of text, and particularly the papers therein by Gait, ppl-22; Atkinson et al, pp35-81; Sproat et al, pp 83-115; and Wu et al, pp 135- 151; 4. Nucleic Acid Hybridization: A Practical Approach (B. D. Hames & S. J. Higgins, eds., 1985) IRL Press, Oxford, whole of text; Immobilized Cells and Enzymes: A Practical Approach (1986) IRL Press, Oxford, whole of text; Perbal, B., A Practical Guide to Molecular Cloning (1984) and Methods In Enzymology (S. Colowick and N. Kaplan, eds., Academic Press, Inc.), whole of series. Those skilled in the art will appreciate that the present disclosure is susceptible to variations and modifications other than those specifically described. It is to be understood that the disclosure includes all such variations and modifications. The disclosure also includes all of the steps, features, compositions and compounds referred to or indicated in this specification, individually or collectively, and any and all combinations of any two or more of said steps or features. The present disclosure is not to be limited in scope by the specific embodiments described herein, which are intended for the purpose of exemplification only. Functionally equivalent products, compositions and methods are clearly within the scope of the disclosure, as described herein. Each feature of any particular aspect or embodiment of the present disclosure may be applied mutatis mutandis to any other aspect or embodiment of the present disclosure. Throughout this specification, unless specifically stated otherwise or the context requires otherwise, reference to a single step, composition of matter, group of steps or group of compositions of matter shall be taken to encompass one and a plurality (i.e. one or more) of those steps, compositions of matter, groups of steps or group of compositions of matter. As used herein, the singular forms of “a”, “and” and “the” include plural forms of these words, unless the context clearly dictates otherwise. The term “and/or”, e.g., “X and/or Y” shall be understood to mean either “X and Y” or “X or Y” and shall be taken to provide explicit support for both meanings or for either meaning. Throughout this specification, the word “comprise” or variations such as “comprises” or “comprising” will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. Throughout the present specification, various aspects and components of the disclosure can be presented in a range format. The range format is included for convenience and should not be interpreted as an inflexible limitation on the scope of the present disclosure. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub- ranges as well as individual numerical values within that range, unless specifically indicated. For example, description of a range such as from 1 to 5 should be considered to have specifically disclosed sub-ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 5, from 3 to 5 etc., as well as individual and partial numbers within the recited range, for example, 1, 2, 3, 4, 5, 5.5 and 6, unless where integers are required or implicit from context. This applies regardless of the breadth of the disclosed range. Where specific values are required, these will be indicated in the specification. The term “about” in relation to a numerical value x is optional and means, for example, any number within 1, 5 or 10% of the referenced number. In certain examples, the term “about” encompasses the exact number recited. Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present disclosure as it existed before the priority date of each of the appended claims. It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the above-described embodiments, without departing from the broad general scope of the present disclosure. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive. Methods of diagnosis The inventors have surprisingly shown that the concentration level of particular lipids from a lipidomic signature in biological samples, such as plasma samples, extracellular vesicle (EV) samples or samples enriched for EVs, can be used to diagnose subjects as having breast cancer. Advantageously, such a method may allow a physician to make appropriate, informed, and timely follow-up and treatment decisions based on this information. Accordingly, the inventors have developed methods of diagnosing breast cancer. As such, in one broad form, the present disclosure provides a method for measuring a level of one or more lipid biomarkers in a biological sample from a subject, said method including the steps of: (a) providing the biological sample; and (b) measuring the level of the one or more lipid biomarkers in the biological sample, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a variant or derivative thereof. In a related broad form, the present disclosure provides a method of diagnosing a subject with a breast cancer, said method including the step of measuring a level of one or more lipid biomarkers in a biological sample from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a variant or derivative thereof, and wherein the level of the one or more lipid biomarkers is diagnostic or indicative of the subject having the breast cancer. With respect to the aspects described herein, the term “subject” includes, but is not limited to, mammals, inclusive of humans, performance animals (such as horses, camels, greyhounds), livestock (such as cows, sheep, horses) and companion animals (such as cats and dogs). In one example, the subject is a human. In certain examples, the subject is a female human. In other examples, the subject is a male human. As generally used herein, the terms “cancer”, “tumour”, “malignant” and “malignancy” refer to diseases or conditions, or to cells or tissues associated with the diseases or conditions, characterized by aberrant or abnormal cell proliferation, differentiation and/or migration often accompanied by an aberrant or abnormal molecular phenotype that includes one or more genetic mutations or other genetic changes associated with oncogenesis, expression of tumour markers, loss of tumour suppressor expression or activity and/or aberrant or abnormal cell surface marker expression. The term “breast cancer” refers to a condition characterized by an abnormally rapid growth of abnormal cells in one or both breasts of a subject. Breast cancer can include, but is not limited to, ductal carcinoma in situ (DCIS), invasive breast cancer (e.g., an invasive carcinoma), inflammatory breast cancer, angiosarcoma of the breast, Phyllodes tumours of the breast, and/or Paget’s disease of the nipple. As used herein, “invasive carcinoma” or “invasive breast cancer” refers to a type of cancer that can include, but is not limited to, invasive ductal carcinoma (IDC), infiltrating ductal carcinoma, invasive lobular carcinoma (ILC), adenoid cystic (or adenocystic) carcinoma, low-grade adenosquamous carcinoma, medullary carcinoma, mucinous (or colloid) carcinoma, papillary carcinoma, tubular carcinoma, metaplastic carcinoma, micropapillary carcinoma, and/or mixed carcinoma having features of both invasive ductal and lobular. Suitably, the breast cancer to be diagnosed in a subject is selected from IDC, DCIS and ILC. In particular examples, the breast cancer to be diagnosed in a subject is IDC. In certain examples, the breast cancer to be diagnosed in a subject is DCIS. In other examples, the breast cancer to be diagnosed in a subject is ILC. The skilled person will further appreciate that the breast cancer may include any aggressive breast cancers and cancer subtypes known in the art, such as triple negative breast cancer, lymph node positive (LN+) breast cancer, HER2 positive (HER2+) breast cancer, PR negative (PR-) breast cancer, PR positive (PR+) breast cancer, ER negative (ER-) breast cancer and ER positive (ER+) breast cancer. The breast cancer also may be of any stage or grade (e.g., Stages I, II, III or IV) and as such can include metastatic breast cancer. As used herein, the terms “diagnosis” and “diagnosing” refer to a method by which one of ordinary skill in the art can assess and/or determine whether a patient or subject is suffering from a given disease or condition, such as determining the presence or absence of a breast cancer. Those skilled in the art often make a diagnosis based on one or more diagnostic indicators or markers whose presence, absence, or amount (relative or absolute) indicates the presence or absence of the disease, disorder or condition. On this point, the methods used herein may be not only utilised to detect breast cancer in patients, but also or alternatively rule out the presence of breast cancer in a subject, such as after a negative primary diagnostic test (e.g., mammography). It will further be appreciated that these terms do not indicate the ability to determine the presence or absence of a particular disease with 100% accuracy, nor do they indicate that a given course or outcome is more likely to occur. Rather, one of ordinary skill in the art will understand that the terms “diagnosis” and “diagnosing” refer to an increased probability that a subject will have a certain disease, disorder or condition, such as a breast cancer. Suitably, the methods described herein are performed in conjunction (e.g., before and/or after) with one or more further diagnostic tests as are known in the art (e.g., breast exam; breast imaging, such as mammogram, ultrasound and MRI; biopsy). In this regard, the present method may be utilised as a preliminary screening test to identify subjects who may benefit from further diagnostic testing. Additionally, or alternatively, the present method may be utilised to confirm the presence or absence of breast cancer as indicated by a previous diagnostic test. Accordingly, in some examples, the present method may include the initial or earlier step and/or subsequent step of performing one or more further diagnostic tests on the subject in question. In alternative examples, the methods described herein are performed without any further diagnostic testing as a primary diagnostic test for breast cancer. Referring to particular examples, the methods herein include the steps of: (a) performing a diagnostic test to determine the presence or absence of a breast cancer in a subject; and (b) if the diagnostic test indicates the absence of the breast cancer in the subject, measuring a level of one or more lipid biomarkers in a biological sample from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(16:0/20:4) and/or PC(18:2/18:2)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof, and wherein the level of the one or more lipid biomarkers is indicative of the presence or absence of the breast cancer in the subject. More particularly, the one or more lipid biomarkers are selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1); or a fragment, variant or derivative thereof. In some examples, the level of the one or more biomarkers indicate or confirm the absence of the breast cancer in the subject. In alternative examples, the level of the one or more biomarkers indicate the presence of the breast cancer in the subject. For such examples, the subject may be subjected to further diagnostic testing, such as described herein. For the above examples, the diagnostic test is suitably a breast imaging test, such as a mammogram or mammography. Suitably, if the level, such as a concentration level or an expression level, of the one or more lipid biomarkers is altered or modulated in the biological sample from a subject, this can be diagnostic of breast cancer in the subject. In one example, an increased level of expression or concentration of a first subset of the one or more lipid biomarkers and/or a decreased level of expression or concentration of a second subset of the one or more lipid biomarkers is diagnostic or indicative of the subject having the breast cancer. In particular examples, an increased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)), PC(38:5), PI(40:5), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3) and/or SM(d42:4), or a variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2)), PC(36:5e), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and/or TG(60:5), or a variant or derivative thereof, is diagnostic or indicative of the subject having the breast cancer. In other examples, an increased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2)), PC(36:5e), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and/or TG(60:5), or a variant or derivative thereof, and/or a decreased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)), PC(38:5), PI(40:5), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3) and/or SM(d42:4), or a variant or derivative thereof, is diagnostic or indicative of the subject not having the breast cancer. In certain examples, the measuring step includes determining the presence or absence of: (i) an increased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)), PC(38:5), PI(40:5), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3) and/or SM(d42:4), or a variant or derivative thereof, and/or (ii) a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2)), PC(36:5e), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and/or TG(60:5), or a variant or derivative thereof, in the biological sample of the subject. According to other examples, the measuring step includes determining the presence or absence of: (i) an increased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2)), PC(36:5e), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and/or TG(60:5), or a variant or derivative thereof, and/or (ii) a decreased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)), PC(38:5), PI(40:5), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3) and/or SM(d42:4), or a variant or derivative thereof, in the biological sample of the subject. Suitably, the diagnostic methods described herein may include the step of administering a treatment to the subject. By way of example, this can include administering to the subject a therapeutically effective amount of the treatment, such as those anti-cancer treatments described herein, when the level of the one or more lipid biomarkers (and/or a risk or diagnostic score derived therefrom) is diagnostic or indicative of the subject having the breast cancer. Methods of treatment Further to the above, the methods described herein may improve patient outcomes by diagnosing subjects with breast cancer, who could potentially benefit from a treatment thereof. Accordingly, the inventors have developed methods of treating a breast cancer in a subject. In one broad form, the present disclosure provides a method of treating a breast cancer in a subject, said method including the step of performing a treatment in respect of the subject, such as surgery and/or administering a therapeutically effective amount of an anti-cancer treatment, in which a level of one or more lipid biomarkers has been measured in a biological sample therefrom that is diagnostic or indicative of the subject having the breast cancer, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a variant or derivative thereof. Suitably, an increased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)), PC(38:5), PI(40:5), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3) and/or SM(d42:4), or a variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2)), PC(36:5e), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and/or TG(60:5), or a variant or derivative thereof, has been measured in the biological sample obtained from the subject. Suitably, the present method includes the initial step of measuring the level of the one or more lipid biomarkers in the biological sample from the subject. Suitably, for the present method, a risk or diagnostic score has been determined using the level, such as a concentration level or an expression level, of the one or more lipid biomarkers and the risk score or diagnostic score is diagnostic or indicative of the subject having the breast cancer. In certain examples, the risk score or diagnostic score is generated at least in part via a logistic model. To this end, the risk score or diagnostic score can be in the form of a probability of the subject having the breast cancer, such that in the absence of additional information a score of 50% or above provides that the subject has a higher probability of having breast cancer than not having breast cancer. In some examples, a diagnostic score of 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99% or higher was determined for the subject. As used herein, the term “therapeutically effective amount” describes a quantity of a specified agent (e.g., an anti-cancer agent) or treatment, such as chemotherapy, radiation therapy, a molecularly targeted therapy and immunotherapy, sufficient to achieve a desired effect in a subject being treated with that agent. For example, this can be the amount of a composition comprising one or more agents that are necessary to reduce, alleviate and/or prevent a cancer (e.g., breast cancer) or cancer-associated disease, disorder or condition. In some examples, a “therapeutically effective amount” is sufficient to reduce or eliminate a symptom of a cancer, such as breast cancer. In other examples, a “therapeutically effective amount” is an amount sufficient to achieve a desired biological effect, for example, an amount that is effective to decrease or prevent cancer growth and/or metastasis. Ideally, a therapeutically effective amount of an agent is an amount sufficient to induce the desired result without causing a substantial cytotoxic effect in the subject. The effective amount of an agent useful for reducing, alleviating and/or preventing a breast cancer will be dependent on the subject being treated, the type and severity of any associated disease, disorder and/or condition (e.g., the number and location of any associated metastases), and the manner of administration of the therapeutic composition. Suitably, the various agents, anti-cancer agents or cancer treatments described herein are administered to a subject as a pharmaceutical composition comprising a pharmaceutically- acceptable carrier, diluent or excipient. In this regard, any dosage form and route of administration, such as those provided therein, may be employed for providing a subject with the composition of the present disclosure. By “pharmaceutically-acceptable carrier, diluent or excipient” is meant a solid or liquid filler, diluent or encapsulating substance that may be safely used in systemic administration. Depending upon the particular route of administration, a variety of carriers, well known in the art may be used. These carriers may be selected from a group including sugars, starches, cellulose and its derivatives, malt, gelatine, talc, calcium sulfate, liposomes and other lipid-based carriers, vegetable oils, synthetic oils, polyols, alginic acid, phosphate buffered solutions, emulsifiers, isotonic saline and salts such as mineral acid salts including hydrochlorides, bromides and sulfates, organic acids such as acetates, propionates and malonates and pyrogen-free water. A useful reference describing pharmaceutically acceptable carriers, diluents and excipients is Remington’s Pharmaceutical Sciences (Mack Publishing Co. N.J. USA, 1991), which is incorporated herein by reference. Any safe route of administration may be employed for providing a patient with the composition of the present disclosure. For example, oral, rectal, parenteral, sublingual, buccal, intravenous, intra-articular, intra-muscular, intra-dermal, subcutaneous, inhalational, intraocular, intraperitoneal, intracerebroventricular, transdermal and the like may be employed. Dosage forms include tablets, dispersions, suspensions, injections, solutions, syrups, troches, capsules, suppositories, aerosols, transdermal patches and the like. These dosage forms may also include injecting or implanting controlled releasing devices designed specifically for this purpose or other forms of implants modified to act additionally in this fashion. Controlled release of the therapeutic agent may be effected by coating the same, for example, with hydrophobic polymers including acrylic resins, waxes, higher aliphatic alcohols, polylactic and polyglycolic acids and certain cellulose derivatives such as hydroxypropylmethyl cellulose. In addition, the controlled release may be effected by using other polymer matrices, liposomes and/or microspheres. Compositions of the present disclosure suitable for oral or parenteral administration may be presented as discrete units such as capsules, sachets or tablets each containing a pre-determined amount of one or more therapeutic agents of the present disclosure, as a powder or granules or as a solution or a suspension in an aqueous liquid, a non-aqueous liquid, an oil-in-water emulsion or a water-in-oil liquid emulsion. Such compositions may be prepared by any of the methods of pharmacy, which may include the step of bringing into association one or more agents as described above with the carrier which constitutes one or more necessary ingredients. In general, the compositions are prepared by uniformly and intimately admixing the agents of the present disclosure with liquid carriers or finely divided solid carriers or both, and then, if necessary, shaping the product into the desired presentation. The above compositions may be administered in a manner compatible with the dosage formulation, and in such amount as is pharmaceutically effective. The dose administered to a patient, in the context of the present disclosure, should be sufficient to effect a beneficial response in a patient over an appropriate period of time. The quantity of agent(s) to be administered may depend on the subject to be treated inclusive of the age, sex, weight and general health condition thereof, factors that will depend on the judgement of the practitioner. It is envisaged that the various agents, anti-cancer agents or cancer treatments described herein can be formulated as discrete doses, such as in the form of a kit. Such a kit may further comprise a package insert comprising printed instructions for simultaneous, concurrent, sequential, successive, alternate or separate use of the agents in the treatment, amelioration and/or prevention of cancer, as described herein, in a patient in need thereof. Accordingly, the aforementioned kits are suitably for use in a method of treating, ameliorating and/or preventing breast cancer, inclusive of one or more symptoms, consequences, sequelae or complications thereof, as described herein. Alternatively, the various therapeutic agents described herein can be formulated together in a composition that optionally includes a pharmaceutically acceptable carrier, excipient or diluent. Methods of treating breast cancer may be prophylactic, preventative or therapeutic and suitable for treatment of cancer in mammals, particularly humans. As used herein, “treating”, “treat” or “treatment” refers to a therapeutic intervention, course of action or protocol that at least ameliorates a symptom of cancer after the cancer and/or its symptoms have at least started to develop. As used herein, “preventing”, “prevent” or “prevention” refers to therapeutic intervention, course of action or protocol initiated prior to the onset of cancer and/or a symptom of cancer so as to prevent, inhibit or delay or development or progression of the cancer or the symptom. Anti-cancer treatments The skilled person will appreciate that cancer treatments for use in the methods described herein may include drug therapy, chemotherapy, antibody, nucleic acid and other biomolecular therapies, radiation therapy, surgery, nutritional therapy, relaxation or meditational therapy and other natural or holistic therapies, although without limitation thereto. Generally, drugs, biomolecules (e.g., antibodies, inhibitory nucleic acids such as siRNA) or chemotherapeutic agents are referred to herein as “anti-cancer therapeutic agents” or “anti-cancer agents”. Suitably, the treatment is or comprises one or more of surgery (e.g., lumpectomy or mastectomy), chemotherapy, radiation therapy, molecularly targeted therapy, hormone therapy and immunotherapy. As generally used herein, the term “chemotherapy” or “chemotherapeutic agent” broadly refers to a treatment or agent with a cytostatic or cytotoxic agent (i.e., a compound) to reduce or eliminate the growth or proliferation of undesirable cells, such as cancer cells. Accordingly, the terms can refer to a cytotoxic or cytostatic agent used to treat a proliferative disorder, for example cancer. The cytotoxic effect of the agent can be, but is not required to be, the result of one or more of nucleic acid intercalation or binding, DNA or RNA alkylation, inhibition of RNA or DNA synthesis, the inhibition of another nucleic acid-related activity (e.g., protein synthesis), or any other cytotoxic effect. Exemplary chemotherapeutic agents include, but are not limited to, alkylating agents (e.g., nitrogen mustards such as chlorambucil, cyclophosphamide, isofamide, mechlorethamine, melphalan, and uracil mustard; aziridines such as thiotepa; methanesulphonate esters such as busulfan; nitroso ureas such as carmustine, lomustine, and streptozocin; platinum complexes such as cisplatin and carboplatin, oxaliplatin, nedaplatin, triplatin tetranitrate, phenanthriplatin, picoplatin, satraplatin and lipoplatin; bioreductive alkylators such as mitomycin, procarbazine, dacarbazine and altretamine); DNA strand-breakage agents (e.g., bleomycin); topoisomerase II inhibitors (e.g., amsacrine, dactinomycin, daunorubicin, idarubicin, mitoxantrone, doxorubicin, etoposide, and teniposide); DNA minor groove binding agents (e.g., plicamydin); antimetabolites (e.g., folate antagonists such as methotrexate and trimetrexate; pyrimidine antagonists such as fluorouracil, fluorodeoxyuridine, CB3717, azacitidine, cytarabine, and floxuridine; purine antagonists such as mercaptopurine, 6-thioguanine, fludarabine, pentostatin; asparginase; and ribonucleotide reductase inhibitors such as hydroxyurea); tubulin interactive agents (e.g., vincristine, vinblastine, and paclitaxel (Taxol)); hormonal agents (e.g., estrogens; conjugated estrogens; ethinyl estradiol; diethylstilbesterol; chlortrianisen; idenestrol; progestins such as hydroxyprogesterone caproate, medroxyprogesterone, and megestrol; and androgens such as testosterone, testosterone propionate, fluoxymesterone, and methyltestosterone); adrenal corticosteroids (e.g., prednisone, dexamethasone, methylprednisolone, and prednisolone); leutinizing hormone releasing agents or gonadotropin-releasing hormone agonists (e.g., leuprolide acetate and goserelin acetate); and antihormonal agents (e.g., tamoxifen, antiandrogen agents such as flutamide; aromatase inhibitors, such as anastrozole, exemestane, and letrozole; and antiadrenal agents, such as mitotane and aminoglutethimide). The term “radiation therapy” or “radiotherapy” used herein refers to the medical use of ionizing radiation, generally as part of cancer treatment, to control or destroy malignant cells. It can also be used as part of adjuvant therapy to prevent tumour recurrence after surgery to remove a primary malignant tumour. Radiation therapy may be delivered by a device placed outside the patient's body (external radiation therapy) or a source placed inside the patient's body (internal radiation therapy or brachytherapy), or intravenously or orally. It may also be delivered by a systemically delivered radioisotope. Radiation therapy can be planned and administered in conjunction with imaging based techniques, such as computed tomography (CT) or magnetic resonance imaging (MRI) to accurately determine the dose and location of radiation to be administered. In various embodiments, radiation therapy includes total body radiation therapy, conventional external beam radiation therapy, stereotactic radiosurgery, stereotactic radiation therapy, three-dimensional conformal radiation therapy, intensity modulated radiation therapy (IMRT), image-guided radiation therapy, tomotherapy and/or brachytherapy. In some examples, the radiation therapy includes stereotactic radiation therapy or intensity modulated radiation therapy (IMRT). As used herein, “molecularly targeted therapy” or “molecularly targeted therapeutic agent” refers to a therapy that targets a particular class of proteins involved in cancer growth or signalling. In some examples, the further anti-cancer agent described herein is or comprises an inhibitor of a tyrosine kinase. The term “tyrosine kinase” refers to enzymes which are capable of transferring a phosphate group from ATP to a tyrosine residue in a protein. Phosphorylation of proteins by tyrosine kinases is an important mechanism in signal transduction for regulation of enzyme activity and cellular events such as cell survival or proliferation. In particular examples, the molecularly targeted therapy comprises one or more of a Human epidermal growth factor receptor 2 (HER2; also referred to as ErbB-2, NEU, HER-2 and CD340) inhibitor (e.g., trastuzumab, pertuzumab, neratinib, tucatinib), a PARP inhibitor (e.g., olaparib, talazoparib), a CDK4/6 inhibitor (e.g., abemaciclib), a PI3K inhibitor (e.g., alpelisib), a dual HER2/EGFR inhibitor (e.g., lapatinib), and a neurotrophic T receptor kinase (NTRK) inhibitor (e.g., entrectinib, larotrectinib). Insofar as they relate to cancer, immunotherapy or immunotherapeutic agents use or modify the immune mechanisms of a subject so as to promote or facilitate treatment of a cancer. In this regard, immunotherapy or immunotherapeutic agents used to treat cancer include cell-based therapies, antibody therapies (e.g., anti-PD1, anti-PDL1 or anti-CTLA4 antibodies) and cytokine therapies. These therapies all exploit the phenomenon that cancer cells often have subtly different molecules termed cancer antigens on their surface that can be detected by the immune system of the cancer subject. Accordingly, immunotherapy is used to provoke the immune system of a cancer patient into attacking the cancer's cells by using these cancer antigens as targets. Non-limiting examples of immunotherapy or immunotherapeutic agents include adalimumab, alemtuzumab, basiliximab, belimumab, bevacizumab, BMS-936559, brentuximab, certolizumab, cituximab, daclizumab, eculizumab, ibritumomab, infliximab, ipilimumab, lambrolkizumab, mepolizumab, MPDL3280A muromonab, natalizumab, nivolumab, ofatumumab, omalizumab, pembrolizumab, pexelizumab, pidilizumab, rituximab, tocilizumab, tositumomab, trastuzumab, ustekinumab, abatacept, alefacept and denileukin diftitox. In particular embodiments, the immunotherapeutic agent is an immune checkpoint inhibitor, such as an anti- PD-1 antibody (e.g., pidilizumab, nivolumab, lambrolkizumab, pembrolizumab), an anti-PD-L1 antibody (e.g., BMS-936559, MPDL3280A) and/or an anti-CTLA4 antibody (e.g., ipilimumab). Lipid biomarkers As described herein, the inventors have found that the concentration levels of particular lipid biomarkers in blood or plasma samples from subjects can be diagnostic of breast cancer. Without being bound by any theory, it is believed that the lipid biomarkers are derived from extracellular vesicles, such as exosomes, present within the biological sample. It is also possible that other sources of lipid biomarkers that co-isolate with extracellular vesicles may contribute, such as apolipoproteins or lipid droplets. As used herein, the term “lipid” refers to a group of organic compounds that has lipophilic or amphiphilic properties, including, but not limited to, acyl carnitine (AcCa), bis(monoacylglycero)phosphates (BMP), cholesterol esters (CE), ceramides (Cer), diacylglycerols (DG or DAG), dihydroleukotriene B4 (DH-LTB4), fatty acids (FA), gangliosides A2 (GA2), gangliosides M3 (GM3), hexose ceramides (HexCer), dihexosylceramide (Hex2Cer), hexosyl dihydroceramide (HexDHCer), lactosylceramide (LacCer), lysophosphatidic acid (LysoPA or LPA), lysophosphatidylcholines (LysoPC or LPC), lysophosphatidylcholines-plasmalogens (LysoPC-pmg), lysophosphatidylethanolamines (LysoPE or LPE), lysophosphatidylethanolamines-plasmalogens (LysoPE-pmg), lysophosphatidylserines (LysoPS or LPS), lysophosphatidylinositols (LPI), monoacylglycerols (MAG), phosphatidylcholines (PC), phosphatidylcholines-plasmalogens (PC-pmg), phosphatidylethanolamines (PE), phosphatidylethanolamines-plasmalogens (PE-pmg), phosphatidylglycerols (PG), prostaglandin A1 (PGA1), prostaglandin B1 (PGB1), phosphatidylinositols (PI), phosphatidylserines (PS), sphingomyelins (SM), sphingosine (Sph), Sphingosine phosphate (SphP), triacylglycerols (TG or TAG) and tetrahydro-12-keto-leukotriene B4 (TH-12-keto-LTB4). The term “biomarker” as used herein refers to a lipid molecule whose levels are indicative or diagnostic of a subject having breast cancer. It will be appreciated that the term “biomarker” is intended to encompass all classes, forms (e.g., phosphorylated or oxidised forms), fragments (e.g., a lipid head group, a fatty acyl chain) and variants (e.g., isomers and isobars) of a lipid biomarker, as are known in the art, such as those provided herein. It is also envisaged that the ether-linked lipids described herein (e.g., PC, PE, PS etc) encompass both alkyl-ether and alkenyl-ether forms thereof unless stated otherwise (e.g., the alkyl-ether and alkenyl-ether of PE(38:6e) include PE(O- 38:6) and PE(P-38:5) respectively). To this end, the 'O-' prefix is used to indicate the presence of an alkyl ether substituent, whereas the “P-” prefix or “p” suffix is used for the alkenyl ether substituent. For the methods, systems and kits described herein, the one or more lipid biomarkers described herein can be selected from one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 etc) classes of lipids, such as AcCa, Cer, DG, Hex2Cer, LPC, LPE, LPI, PC, PE, PG, PI, PS, SM, SphP and TG. In certain examples, the one or more lipid biomarkers comprise LPC(14:0) and/or PI(38:6). More particularly, the one or more lipid biomarkers may comprise LPC(14:0) and/or PI(18:2_20:4). In other examples, the one or more lipid biomarkers comprise LPC(14:0) and PI(38:6). More particularly, the one or more lipid biomarkers may comprise LPC(14:0) and PI(18:2_20:4). In some examples, the one or more lipid biomarkers comprise LPC(14:0), or a fragment, variant or derivative thereof. In such examples, the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. In other such examples, the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(53:0), TG(56:0), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. In some examples, the one or more lipid biomarkers comprise LPC(14:0), or a fragment, variant or derivative thereof and one or more other lipid biomarkers selected from the group consisting of: (a) LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2) (such as, TG(16:0_10:0_18:2) and/or TG(16:0_10:1_18:1)), TG(50:1e), TG(52:3e), TG(56:0), TG(56:1), TG(58:2) and TG(60:5); (b) LPC(16:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)) and TG(52:3e); (c) LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(52:3e) and TG(56:1); (d) LPC(16:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(52:3e) and TG(58:2); (e) PC(34:0), PC(36:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d37:1), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), TG(51:0), TG(52:3e), TG(53:0), TG(58:1), TG(58:2) and TG(59:1); (f) PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d39:2), SM(d41:2), TG(52:3e), TG(53:0), TG(58:1) and TG(58:2); (g) PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(52:3e), and TG(58:2); (h) PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1), PS(38:4) and TG(52:3e); (i) LPC(18:3), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2p), PE(38:6e), PE(40:6e), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), SM(d38:4) and SM(d42:4); (j) AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(38:5), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1); (k) AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1); (l) Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and SM(d36:2); (m) Cer(d36:1), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)) and SM(d36:2); (n) AcCa(18:2), Cer(d36:1), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); or (o) Cer(d36:1), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1); or a fragment, variant or derivative thereof. In other examples, the one or more lipid biomarkers comprise PI(38:6), or a fragment, variant or derivative thereof. For such examples, the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. In other such examples, the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), LPC(18:3), PC(34:0), PC(36:0), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), PS(40:6), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d42:4), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(53:0), TG(56:0), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. In some examples, the one or more lipid biomarkers comprise PI(38:6), or a fragment, variant or derivative thereof and one or more other lipid biomarkers selected from the group consisting of: (a) LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(42:2), TG(44:2) (such as, TG(16:0_10:0_18:2) and/or TG(16:0_10:1_18:1)), TG(50:1e), TG(52:3e), TG(56:0), TG(56:1), TG(58:2) and TG(60:5); (b) LPC(14:0), LPC(16:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(52:3e) and TG(58:2); (c) LPC(14:0), PC(34:0), PC(36:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d37:1), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), TG(51:0), TG(52:3e), TG(53:0), TG(58:1), TG(58:2) and TG(59:1); (d) LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d39:2), SM(d41:2), TG(52:3e), TG(53:0), TG(58:1) and TG(58:2); (e) LPC(14:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1), PS(38:4), TG(52:3e), and TG(58:2); or (f) LPC(14:0), LPC(18:3), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2p), PE(38:6e), PE(40:6e), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), PS(40:6), SM(d38:4), SM(d42:4); or a fragment, variant or derivative thereof. In certain examples, the one or more lipid biomarkers comprise LPC(14:0) and PI(38:6), or a fragment, variant or derivative thereof. In such examples, the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. In other such examples, the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of LPC(16:0), LPC(18:0), PC(34:0), PC(36:0), PC(36:4), PC(36:5e), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PG(36:1), PI(36:3), PI(36:4), PS(36:1), SM(d37:1), SM(d39:2), SM(d40:2), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(53:0), TG(56:0), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. In some examples, the one or more lipid biomarkers comprise LPC(14:0) and PI(38:6), or a fragment, variant or derivative thereof, and one or more other lipid biomarkers selected from the group consisting of: (a) LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(42:2), TG(44:2) (such as, TG(16:0_10:0_18:2) and/or TG(16:0_10:1_18:1)), TG(50:1e), TG(52:3e), TG(56:0), TG(56:1), TG(58:2) and TG(60:5); (b) LPC(16:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(52:3e) and TG(58:2); (c) PC(34:0), PC(36:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d37:1), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), TG(51:0), TG(52:3e), TG(53:0), TG(58:1), TG(58:2) and TG(59:1); (d) PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d39:2), SM(d41:2), TG(52:3e), TG(53:0), TG(58:1) and TG(58:2); (e) PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1), PS(38:4), TG(52:3e), and TG(58:2); or (f) LPC(18:3), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2p), PE(38:6e), PE(40:6e), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), PS(40:6), SM(d38:4), SM(d42:4); or a fragment, variant or derivative thereof. In some examples, the one or more lipid biomarkers comprise Cer(d36:1), or a fragment, variant or derivative thereof. In such examples, the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. For particular examples, the one or more lipid biomarkers comprise Cer(d36:1), or a fragment, variant or derivative thereof and one or more other lipid biomarkers selected from the group consisting of: (a) AcCa(18:2), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1); (b) Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and SM(d36:2); (c) LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)) and SM(d36:2); (d) AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); or (e) LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1); or a fragment, variant or derivative thereof. For particular examples, the one or more lipid biomarkers comprise LPC(14:0), or a fragment, variant or derivative thereof and one or more other lipid biomarkers selected from the group consisting of: (a) AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1); (b) Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and SM(d36:2); (c) Cer(d36:1), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)) and SM(d36:2); (d) AcCa(18:2), Cer(d36:1), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); or (e) Cer(d36:1), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1); or a fragment, variant or derivative thereof. In some examples, the one or more lipid biomarkers comprise LPC(16:0e), or a fragment, variant or derivative thereof. In such examples, the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. For particular examples, the one or more lipid biomarkers comprise LPC(16:0e), or a fragment, variant or derivative thereof and one or more other lipid biomarkers selected from the group consisting of: (a) AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1); (b) Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and SM(d36:2); (c) Cer(d36:1), LPC(14:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)) and SM(d36:2); (d) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); or (e) Cer(d36:1), LPC(14:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1); or a fragment, variant or derivative thereof. In some examples, the one or more lipid biomarkers comprise PC(32:2), or a fragment, variant or derivative thereof. In such examples, the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. For particular examples, the one or more lipid biomarkers comprise PC(32:2), or a fragment, variant or derivative thereof and one or more other lipid biomarkers selected from the group consisting of: (a) AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1); (b) Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and SM(d36:2); (c) Cer(d36:1), LPC(14:0), LPC(16:0e), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)) and SM(d36:2); (d) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); or (e) Cer(d36:1), LPC(14:0), LPC(16:0e), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1); or a fragment, variant or derivative thereof. In some examples, the one or more lipid biomarkers comprise PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), or a fragment, variant or derivative thereof. More particularly, the one or more lipid biomarkers suitably comprise PC(18:2/18:2), or a fragment, variant or derivative thereof. Even more particularly, the one or more lipid biomarkers suitably comprise PC(16:0/20:4), or a fragment, variant or derivative thereof. In such examples, the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. For particular examples, the one or more lipid biomarkers comprise PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), or a fragment, variant or derivative thereof and one or more other lipid biomarkers selected from the group consisting of: (a) AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1); (b) Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(32:2), PI(40:5) and SM(d36:2); (c) Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2) and SM(d36:2); (d) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); or (e) Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), SM(d36:2) and SphP(d18:1); or a fragment, variant or derivative thereof. In some examples, the one or more lipid biomarkers comprise SM(d36:2), or a fragment, variant or derivative thereof. In such examples, the one or more lipid biomarkers can optionally further include one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. For particular examples, the one or more lipid biomarkers comprise SM(d36:2), or a fragment, variant or derivative thereof and one or more other lipid biomarkers selected from the group consisting of: (a) AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4) and SphP(d18:1); (b) Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)) and PI(40:5); (c) Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2) and PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)); (d) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PS(38:4), and SphP(d18:1); or (e) Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)) and SphP(d18:1); or a fragment, variant or derivative thereof. Suitably, the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise or consist of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. In relation to these examples, the biological sample is suitably a plasma sample. For such examples, an increased level of one or more of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)), PI(40:5) and/or SM(d36:2) or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)), PI(40:5) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and SM(d36:2), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise or consist of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and SM(d36:2), or a fragment, variant or derivative thereof. In relation to these examples, the biological sample is suitably a plasma sample. For such examples, an increased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)), PI(40:5) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), LPC(16:0e), PC(32:2) and/or PC(36:4) (e.g., PC(18:2/18:2)), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)), PI(40:5) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), LPC(16:0e), PC(32:2) and/or PC(36:4) (e.g., PC(18:2/18:2)), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise or consist of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. In relation to these examples, the biological sample is suitably a plasma sample. For such examples, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)) and SM(d36:2), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise or consist of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)) and SM(d36:2), or a fragment, variant or derivative thereof. In relation to these examples, the biological sample is suitably a plasma sample. For such examples, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), LPC(16:0e), PC(32:2) and/or PC(36:4) (e.g., PC(18:2/18:2)), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), LPC(16:0e), PC(32:2) and/or PC(36:4) (e.g., PC(18:2/18:2), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers comprise Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)) and SM(d36:2), or a fragment, variant or derivative thereof and one or more other lipid biomarkers. For such examples, the one or more other lipid biomarkers may be selected from the group consisting of AcCa(18:2), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(16:0), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(34:0), PC(36:0), PC(36:2), PC(36:4) (e.g., PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. For certain of such examples, the one or more other lipid biomarkers may be selected from the group consisting of AcCa(18:2), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(18:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4) and SphP(d18:1), or a fragment, variant or derivative thereof. More particularly, for such examples, the one or more other lipid biomarkers may be selected from the group consisting of AcCa(18:2), LPC(18:2), PE(36:2), PS(38:4) and SphP(d18:1), or a fragment, variant or derivative thereof. In other such examples, the one or more other lipid biomarkers may be selected from the group consisting of Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (e.g., PC(16:0/20:4)) and PI(40:5), or a fragment, variant or derivative thereof. Suitably, the one or more lipid biomarkers are selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise or consist of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. In relation to these examples, the biological sample is suitably a plasma sample. For such examples, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of LPC(16:0), LPC(18:0), PC(34:0), PC(36:0), PC(36:4), PC(36:5e), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PG(36:1), PI(36:3), PI(36:4), PS(36:1), SM(d37:1), SM(d39:2), SM(d40:2), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(53:0), TG(56:0), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise or consist of LPC(16:0), LPC(18:0), PC(34:0), PC(36:0), PC(36:4), PC(36:5e), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PG(36:1), PI(36:3), PI(36:4), PS(36:1), SM(d37:1), SM(d39:2), SM(d40:2), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(53:0), TG(56:0), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. In relation to these examples, the biological sample is suitably a plasma sample and/or an EV sample. For such examples, an increased level of SM(d37:1), SM(d39:2) and/or SM(d40:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(16:0), LPC(18:0), PC(34:0), PC(36:0), PC(36:4), PC(36:5e), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PG(36:1), PI(36:3), PI(36:4), PS(36:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(53:0), TG(56:0), TG(59:1) and/or TG(60:5), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of SM(d37:1), SM(d39:2) and/or SM(d40:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(16:0), LPC(18:0), PC(34:0), PC(36:0), PC(36:4), PC(36:5e), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PG(36:1), PI(36:3), PI(36:4), PS(36:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(53:0), TG(56:0), TG(59:1) and/or TG(60:5), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2) (such as, TG(16:0_10:0_18:2) and/or TG(16:0_10:1_18:1)), TG(50:1e), TG(52:3e), TG(56:0), TG(56:1), TG(58:2) and TG(60:5), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise or consist of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2) (such as, TG(16:0_10:0_18:2) and/or TG(16:0_10:1_18:1)), TG(50:1e), TG(52:3e), TG(56:0), TG(56:1), TG(58:2) and TG(60:5), or a fragment, variant or derivative thereof. In relation to these examples, the biological sample is suitably a plasma sample. For such examples, a decreased level of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2) (such as, TG(16:0_10:0_18:2) and/or TG(16:0_10:1_18:1)), TG(50:1e), TG(52:3e), TG(56:0), TG(56:1), TG(58:2) and/or TG(60:5), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, a decreased level of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2) (such as, TG(16:0_10:0_18:2) and/or TG(16:0_10:1_18:1)), TG(50:1e), TG(52:3e), TG(56:0), TG(56:1), TG(58:2) and/or TG(60:5), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)) and TG(52:3e), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise or consist of LPC(14:0), LPC(16:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)) and TG(52:3e), or a fragment, variant or derivative thereof. In relation to these examples, the biological sample is suitably a plasma sample. For such examples, a decreased level of LPC(14:0), LPC(16:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)) and/or TG(52:3e), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, a decreased level of LPC(14:0), LPC(16:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)) and/or TG(52:3e), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(52:3e) and TG(56:1), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise or consist of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(52:3e) and TG(56:1), or a fragment, variant or derivative thereof. In relation to these examples, the biological sample is suitably a plasma sample. For such examples, a decreased level of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(52:3e) and/or TG(56:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, a decreased level of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(52:3e) and/or TG(56:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(52:3e) and TG(58:2), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise or consist of LPC(14:0), LPC(16:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(52:3e) and TG(58:2), or a fragment, variant or derivative thereof. In relation to these examples, the biological sample is suitably a plasma sample. For such examples, a decreased level of LPC(14:0), LPC(16:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(52:3e) and/or TG(58:2), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, a decreased level of LPC(14:0), LPC(16:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(52:3e) and/or TG(58:2), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PC(36:5e), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d37:1), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), TG(51:0), TG(52:3e), TG(53:0), TG(58:1), TG(58:2) and TG(59:1), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise or consist of LPC(14:0), PC(34:0), PC(36:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PC(36:5e), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d37:1), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), TG(51:0), TG(52:3e), TG(53:0), TG(58:1), TG(58:2) and TG(59:1), or a fragment, variant or derivative thereof. In relation to these examples, the biological sample is suitably an EV sample or a sample enriched for EVs. For such examples, an increased level of SM(d35:1), SM(d37:1), SM(d39:2), SM(d40:2), SM(d41:2) and/or SM(d41:3), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), PC(34:0), PC(36:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PC(36:5e), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), TG(51:0), TG(52:3e), TG(53:0), TG(58:1), TG(58:2) and/or TG(59:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of SM(d35:1), SM(d37:1), SM(d39:2), SM(d40:2), SM(d41:2) and/or SM(d41:3), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), PC(34:0), PC(36:0), PC(36:2) (such as PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PC(36:5e), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), TG(51:0), TG(52:3e), TG(53:0), TG(58:1), TG(58:2) and/or TG(59:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d39:2), SM(d41:2), TG(52:3e), TG(53:0), TG(58:1) and TG(58:2), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise or consist of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d39:2), SM(d41:2), TG(52:3e), TG(53:0), TG(58:1) and TG(58:2), or a fragment, variant or derivative thereof. In relation to these examples, the biological sample is suitably an EV sample or a sample enriched for EVs. For such examples, an increased level of SM(d35:1), SM(d39:2) and/or SM(d41:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d39:2), SM(d41:2), TG(52:3e), TG(53:0), TG(58:1) and/or TG(58:2), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of SM(d35:1), SM(d39:2) and/or SM(d41:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d39:2), SM(d41:2), TG(52:3e), TG(53:0), TG(58:1) and/or TG(58:2), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PC(36:2), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(52:3e), and TG(58:2), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise or consist of LPC(14:0), PC(36:2), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(52:3e), and TG(58:2), or a fragment, variant or derivative thereof. In relation to these examples, the biological sample is suitably a plasma sample and/or an EV sample or a sample enriched for EVs. For such examples, a decreased level of LPC(14:0), PC(36:2), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(52:3e), and/or TG(58:2), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, a decreased level of LPC(14:0), PC(36:2), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(52:3e), and/or TG(58:2), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1), PS(38:4) and TG(52:3e), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise or consist of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1), PS(38:4) and TG(52:3e), or a fragment, variant or derivative thereof. In relation to these examples, the biological sample is suitably a plasma sample and/or an EV sample or a sample enriched for EVs. For such examples, a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1), PS(38:4) and/or TG(52:3e), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1), PS(38:4) and/or TG(52:3e), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(18:3), PC(36:4), PE(36:2p), PE(38:6e), PE(40:6e), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), SM(d38:4) and SM(d42:4), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise or consist of LPC(14:0), LPC(18:3), PC(36:4), PE(36:2p), PE(38:6e), PE(40:6e), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), SM(d38:4) and SM(d42:4), or a fragment, variant or derivative thereof. In relation to these examples, the biological sample is suitably a plasma sample and/or an EV sample or a sample enriched for EVs. For such examples, an increased level of SM(d38:4) and/or SM(d42:4), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), LPC(18:3), PC(36:4), PE(36:2p), PE(38:6e), PE(40:6e), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4) and/or PS(40:6), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of SM(d38:4) and/or SM(d42:4), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), LPC(18:3), PC(36:4), PE(36:2p), PE(38:6e), PE(40:6e), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4) and/or PS(40:6), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4), PC(38:5), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise or consist of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4), PC(38:5), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. In relation to these examples, the biological sample is suitably a plasma sample and/or an EV sample or a sample enriched for EVs. For such examples, an increased level of Cer(d36:1), PC(38:5), SM(d35:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d36:1), PC(38:5), SM(d35:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of: (a) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1); (b) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); (c) Cer(d36:1), LPA(18:0), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PS(38:4), SM(d36:2) and SphP(d18:1); (d) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1); (e) Cer(d36:1), Cer(d42:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), PC (36:0), PC(36:2) (such as PC(18:0_18:2) and/or PC(18:1_18:1)), PE(34:1), PI(34:1), SM(d36:2) and SphP(d18:1); (f) AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1); (g) AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); (h) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); (i) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1); or (j) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1); or a fragment, variant or derivative thereof. Suitably, the one or more lipid biomarkers are selected from the group consisting of Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise, consist of or consist essentially of Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. For such examples, an increased level of Cer(d36:1), SM(d35:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d36:1), SM(d35:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of: Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. For other examples, the one or more lipid biomarkers comprise, consist of or consist essentially of Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. For such examples, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of Cer(d36:1), LPA(18:0), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. According to some examples, the one or more lipid biomarkers comprise, consist of or consist essentially of Cer(d36:1), LPA(18:0), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. For such examples, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPA(18:0), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPA(18:0), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. In particular examples, the one or more lipid biomarkers comprise, consist of or consist essentially of Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. For such examples, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of Cer(d36:1), Cer(d42:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), PC(36:0), PC(36:2) (such as PC(18:0_18:2) and/or PC(18:1_18:1)), PE(34:1), PI(34:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. For certain examples, the one or more lipid biomarkers comprise, consist of or consist essentially of Cer(d36:1), Cer(d42:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), PC(36:0), PC(36:2) (such as PC(18:0_18:2) and/or PC(18:1_18:1)), PE(34:1), PI(34:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. More particularly, the one or more lipid biomarkers may comprise, consist of or consist essentially of Cer(d36:1), Cer(d42:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), PC (36:0), PC(18:0_18:2), PC(18:1_18:1), PE(34:1), PI(34:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. For such examples, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of Cer(d42:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), PC(36:0), PC(36:2) (such as PC(18:0_18:2) and/or PC(18:1_18:1)), PE(34:1), PI(34:1) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of Cer(d42:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), PC (36:0), PC(36:2) (such as PC(18:0_18:2) and/or PC(18:1_18:1)), PE(34:1), PI(34:1) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. Referring to other examples, the one or more lipid biomarkers comprise, consist of or consist essentially of AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. For such examples, an increased level of Cer(d36:1), SM(d35:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d36:1), SM(d35:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. According to other examples, the one or more lipid biomarkers comprise, consist of or consist essentially of AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. For such examples, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. In various examples, the one or more lipid biomarkers comprise, consist of or consist essentially of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. For such examples, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. For some examples, the one or more lipid biomarkers comprise, consist of or consist essentially of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. For such examples, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Suitably, the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. According to particular examples, the one or more lipid biomarkers comprise, consist of or consist essentially of AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. For such examples, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d36:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Also provided are lipid “variants” such as naturally occurring variants, isobars and isomers (including stereoisomers) of the lipid biomarkers provided herein. To this end, it is further envisaged that the lipid biomarkers described herein may encompass a collection of one or more isomers thereof. For example, PC(36:2) is a lipid or lipid biomarker that is the collection of one or more phosphatidylcholine isomers that have 36 carbons in the acyl chain and two double bonds across the two acyl chains. Exemplary isomers for the lipid biomarkers described herein are provided in the below table. Suitably, each of the lipid biomarker isomers have identical molecular weights. Although a lipid biomarker can encompass a total number of isomers thereof, a biological sample from a subject may only contain one isomer, two isomers, three isomers, four isomers, five isomers etc, or any number of isomers less than the total number of all possible isomers of said lipid biomarker. Accordingly, a lipid biomarker can refer to one or more of the isomers that make up the entire collection of possible isomers. Table of isomers Lipid Biomarker Isomers and Isobars m/z* AcCa(18:2) AcCa(18:2) 423.33485 9 Cer(d36:1) Cer(d14:1/22:0), Cer(d16:1/20:0), Cer(d18:1/18:0), 565.5434 Cer(d18:0/18:1(9Z)) Cer(d38:1) Cer(d18:1/20:0); Cer(d14:1/24:0); Cer(d16:1/22:0) 594.58197 Cer(d39:1) Cer(d16:1/23:0); Cer(d15:1/24:0); Cer(d18:1/21:0) 608.59762 Cer(d40:1) Cer(d18:1/22:0); Cer(d14:1/26:0); Cer(d16:1/24:0) 622.61327 Cer(d41:1) Cer(d18:1/23:0); Cer(d19:1/22:0) 636.62892 Cer(d41:2) Cer(d17:1/24:1); Cer(d18:2/23:0); Cer(d26:2/15:0) 634.61327 Cer(d42:2) Cer(d18:1/24:1); Cer(d18:1/24:1(15Z)); Cer(d18:2/24:0) 648.62892 Lipid Biomarker Isomers and Isobars m/z* LPA(18:0) LPA(0:0/18:0), LPA(18:0/0:0) 438.27464 3 LPA(18:2) LPA(18:2(9Z,12Z)/0:0), LPA(0:0/18:2(9Z,12Z)) 434.24334 3 LPC(14:0) LPC(14:0), LPC (1-14:0), LPC (2-14:0), LPE (1-17:0) 467.3012 LPC(16:0) LPC(16:0), PC(16:0/0:0), PC(0:0/16:0), PC(O-14:0/2:0) 495.3325 LPC(16:0e) LPC(16:0e), LPC(O-16:0/0:0) 481.3532 LPC(18:0) LPC(O-16:0/2:0), LPC(18:0/0:0), LPC(0:0/18:0) 523.3638 LPC(18:2) LPC(18:2), LPC(18:2(2E,4E)/0:0), LPC(18:2(9Z,12Z)/0:0) 519.3325 LPC(18:3) LPC(18:3), LPC(18:3(6Z,9Z,12Z)/0:0), 517.3168 LPC(18:3(9Z,12Z,15Z)/0:0) LPI(18:0) LPI(18:0/0:0) 599.32019 LPI(18:1) LPI(18:1(9Z)/0:0) 598.31181 8 PC(32:2) PC(14:0_18:2), PC(12:0/20:2(11Z,14Z)), 729.53085 PC(14:0/18:2(11Z,14Z)), PC(14:0/18:2(9Z,12Z)), 7 PC(14:1(9Z)/18:1(9Z)), PC(15:0/17:2(9Z,12Z)), PC(15:1(9Z)/17:1(9Z)), PC(16:1(9E)/16:1(9E)), PC(16:1(9Z)/16:1(9Z)), PC(17:1(9Z)/15:1(9Z)), PC(17:2(9Z,12Z)/15:0), PC(18:1(9Z)/14:1(9Z)), PC(18:2(9Z,12Z)/14:0), PC(20:2(11Z,14Z)/12:0) PC(34:0) PC(10:0_24:0), PC(11:0_23:0), PC(12:0_22:0), PC(13:0_21:0), 761.5935 PC(14:0_20:0), PC(15:0_19:0), PC(16:0_18:0), PC(17:0_17:0), PC(18:0_16:0), PC(19:0_15:0), PC(20:0_14:0), PC(21:0_13:0), PC(22:0_12:0) PC(36:0) PC(18:0_18:0), PC(11:0_25:0), PC(12:0_24:0), PC(13:0_23:0), 789.6248 PC(14:0_22:0), PC(15:0_21:0), PC(16:0_20:0), PC(17:0_19:0), PC(19:0_17:0), PC(20:0_16:0), PC(21:0_15:0), PC(22:0_14:0) PC(36:2) PC(18:0_18:2), PC(18:1_18:1), PC(18:2_18:0), PC(14:0_22:2), 785.5935 PC(14:1_22:1), PC(16:0_20:2), PC(16:1_20:1), PC(17:1_19:1), PC(17:2_19:0), PC(19:0_17:2), PC(19:1_17:1), PC(20:1_16:1), PC(20:2_16:0), PC(22:1_14:1), PC(22:2_14:0), PC(18:0/18:2(9Z,12Z)), PC(18:1(9Z)/18:1(9Z)), 18:1 (Δ6-Cis) PC, 18:1(11-cis) PC PC(36:4) PC(18:2_18:2), PC(16:0/20:4(5E,8E,11E,14E)), 781.5622 PC(18:0/18:4(6Z,9Z,12Z,15Z)), PC(18:0/18:4(9E,11E,13E,15E)), PC(18:1(9Z)/18:3(9Z,12Z,15Z)), PC(18:2(2E,4E)/18:2(2E,4E)), PC(18:2(2Z,4Z)/18:2(2Z,4Z)), PC(18:2(6Z,9Z)/18:2(6Z,9Z)), PC(18:2(9Z,11Z)/18:2(9Z,11Z)), Lipid Biomarker Isomers and Isobars m/z* PC(18:2(9Z,12Z)/18:2(9Z,12Z)), PC(18:3(9Z,12Z,15Z)/18:1(9Z)), PC(20:4(5Z,8Z,11Z,14Z)/16:0), PC(20:4(8E,11E,14E,17E)/16:0), PC(14:0/22:4(7Z,10Z,13Z,16Z)), PC(16:1(9Z)/20:3(8Z,11Z,14Z)), PC(18:1(9Z)/18:3(6Z,9Z,12Z)), PC(18:3(6Z,9Z,12Z)/18:1(9Z)), PC(18:4(6Z,9Z,12Z,15Z)/18:0), PC(20:3(8Z,11Z,14Z)/16:1(9Z)), PC(22:4(7Z,10Z,13Z,16Z)/14:0), PC(16:0/20:4), PC(16:0/20:4(8Z,11Z,14Z,17Z)), PC(16:1(9Z)/20:3(5Z,8Z,11Z)), PC(18:1(11Z)/18:3(6Z,9Z,12Z)), PC(18:1(11Z)/18:3(9Z,12Z,15Z)), PC(18:3(6Z,9Z,12Z)/18:1(11Z)), PC(18:3(9Z,12Z,15Z)/18:1(11Z)), PC(20:3(5Z,8Z,11Z)/16:1(9Z)) PC(36:5e)/PC(O- PC(16:0e_20:5), PC(O-16:0/20:5(5Z,8Z,11Z,14Z,17Z)), PC(O- 765.5672 36:5)/PC(36:4p) 16:1(9Z)/20:4(8Z,11Z,14Z,17Z)), PC(P- 16:0/20:4(5Z,8Z,11Z,14Z)), PC(P-18:0/18:4(6Z,9Z,12Z,15Z)) PC(38:5) PC(18:1_20:4), PC(16:0/22:5(4Z,7Z,10Z,13Z,16Z)), 807.5778 PC(16:0/22:5(7Z,10Z,13Z,16Z,19Z)), PC(16:1(9Z)/22:4(7Z,10Z,13Z,16Z)), PC(18:0/20:5(5Z,8Z,11Z,14Z,17Z)), PC(18:0/20:5(9Z,11Z,13Z,15Z,17Z)), PC(18:1(11Z)/20:4(5Z,8Z,11Z,14Z)), PC(18:1(11Z)/20:4(8Z,11Z,14Z,17Z)), PC(18:1(9Z)/20:4(5Z,8Z,11Z,14Z)), PC(18:1(9Z)/20:4(8Z,11Z,14Z,17Z)), PC(18:2(9Z,12Z)/20:3(5Z,8Z,11Z)), PC(18:2(9Z,12Z)/20:3(8Z,11Z,14Z)), PC(18:3(6Z,9Z,12Z)/20:2(11Z,14Z)), PC(18:3(9Z,12Z,15Z)/20:2(11Z,14Z)), PC(18:4(6Z,9Z,12Z,15Z)/20:1(11Z)), PC(20:1(11Z)/18:4(6Z,9Z,12Z,15Z)), PC(20:2(11Z,14Z)/18:3(6Z,9Z,12Z)), PC(20:2(11Z,14Z)/18:3(9Z,12Z,15Z)), PC(20:3(5Z,8Z,11Z)/18:2(9Z,12Z)), PC(20:3(8Z,11Z,14Z)/18:2(9Z,12Z)), PC(20:4(5Z,8Z,11Z,14Z)/18:1(11Z)), PC(20:4(5Z,8Z,11Z,14Z)/18:1(9Z)), Lipid Biomarker Isomers and Isobars m/z* PC(20:4(8Z,11Z,14Z,17Z)/18:1(11Z)), PC(20:5(5Z,8Z,11Z,14Z,17Z)/18:0), PC(22:4(7Z,10Z,13Z,16Z)/16:1(9Z)) PE(34:2p)/PE(34:3 PE(16:0p_18:2), PE(O-16:0_18:3), PE(P-16:0/18:2(9Z,12Z)), 699.5203 e)/PE(O-34:3) PE(O-16:0/18:3(9Z,12Z,15Z)), PE(O-16:0/18:3(6Z,9Z,12Z)) PE(36:2) PE(18:0_18:2), PE(14:0/22:2(13Z,16Z)), 743.54650 PE(14:1(9Z)/22:1(11Z)), PE(16:0/20:2(11Z,14Z)), 7 PE(16:1(9Z)/20:1(11Z)), PE(17:1(9Z)/19:1(9Z)), PE(17:2(9Z,12Z)/19:0), PE(18:0(11Cp)/16:0(9Cp)), PE(18:0/18:2(9Z,12Z)), PE(18:1(6Z)/18:1(6Z)), PE(18:1(9E)/18:1(9E)), PE(18:1(9Z)/18:1(9Z)), PE(18:2(9Z,12Z)/18:0), PE(19:0/17:2(9Z,12Z)), PE(19:1(9Z)/17:1(9Z)), PE(20:1(11Z)/16:1(9Z)), PE(20:2(11Z,14Z)/16:0), PE(22:1(11Z)/14:1(9Z)), PE(22:2(13Z,16Z)/14:0) PE(36:2p)/PE(36:3 PE(18:0p_18:2), PE(O-18:0/18:3(6Z,9Z,12Z)), PE(O- 727.5516 e)/PE(O-36:3) 18:0/18:3(9Z,12Z,15Z)), PE(O-16:0/20:3(8Z,11Z,14Z)), PE(P- 16:0/20:2(11Z,14Z)), PE(P-18:0/18:2(9Z,12Z)), PE(36:3p) PE(18:1p_18:2), PE(O-16:0/20:4(5Z,8Z,11Z,14Z)), PE(O- 725.5359 /PE(36:4e)/PE(O- 18:0/18:4(6Z,9Z,12Z,15Z)), PE(P-16:0/20:3(8Z,11Z,14Z)), 36:4) PE(P-18:0/18:3(6Z,9Z,12Z)), PE(P-18:0/18:3(9Z,12Z,15Z)) PE(36:5p) PE(16:0p_20:5), PE(P-16:0/20:5(5Z,8Z,11Z,14Z,17Z)) 721.5046 /PE(36:6e)/PE(O- 36:6) PE(38:2p) PE(20:0p_18:2), PE(O-18:0/20:3(8Z,11Z,14Z)), PE(O- 755.5829 /PE(38:3e)/PE(O- 20:0/18:3(6Z,9Z,12Z)), PE(O-20:0/18:3(9Z,12Z,15Z)), PE(P- 38:3) 16:0/22:2(13Z,16Z)), PE(P-18:0/20:2(11Z,14Z)), PE(P- 20:0/18:2(9Z,12Z)) PE(38:6p) PE(16:0p_22:6), PE(P-16:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)) 747.5203 /PE(38:7e)/PE(O- 38:7) PE(38:6e) PE(18:1e_20:5), PE(O-16:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)), 749.5359 /PE(38:5p)/PE(O- PE(P-18:0/20:5(5Z,8Z,11Z,14Z,17Z)), 38:6) PE(O-40:6) PE(18:1e_22:5), PE(18:1p_22:4), PE(20:0p_20:5), 777.5672 (PE(40:6e)/ PE(40:5p)) PE(40:7e) PE(18:1e_22:6), PE(P-18:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)) 775.5516 PG(36:1) PG(18:0_18:1), PG(18:0/18:1(9Z)), PG(14:0/22:1(11Z)), 776.5567 PG(14:1(9Z)/22:0), PG(15:1(9Z)/21:0), PG(16:1(9Z)/20:0), Lipid Biomarker Isomers and Isobars m/z* PG(17:0/19:1(9Z)), PG(17:1(9Z)/19:0), PG(19:0/17:1(9Z)), PG(19:1(9Z)/17:0), PG(20:0/16:1(9Z)), PG(20:1(11Z)/16:0), PG(21:0/15:1(9Z)), PG(22:0/14:1(9Z)), PG(22:1(11Z)/14:0), PG(18:1(9Z)/18:0), PG(16:0/20:1(11Z)) PI(36:1) PI(18:0_18:1), PI(14:0_22:1), PI(14:1_22:0), PI(15:1_21:0), 864.5728 PI(16:1_20:0), PI(17:0_19:1), PI(17:1_19:0), PI(19:0_17:1), PI(19:1_17:0), PI(20:0_16:1), PI(20:1_16:0), PI(21:0_15:1), PI(22:0_14:1), PI(22:1_14:0), PI(18:1_18:0), PI(16:0_20:1), PI(18:0/18:1(9Z)), PI(18:1(9Z)/18:0) PI(36:3) PI(18:1_18:2), PI(14:1(9Z)/22:2(13Z,16Z)), 860.5415 PI(16:1(9Z)/20:2(11Z,14Z)), PI(17:2(9Z,12Z)/19:1(9Z)), PI(18:0/18:3(6Z,9Z,12Z)), PI(18:0/18:3(9Z,12Z,15Z)), PI(18:2(9Z,12Z)/18:1(9Z)), PI(18:3(6Z,9Z,12Z)/18:0), PI(18:3(9Z,12Z,15Z)/18:0), PI(19:1(9Z)/17:2(9Z,12Z)), PI(20:2(11Z,14Z)/16:1(9Z)), PI(20:3(8Z,11Z,14Z)/16:0), PI(22:2(13Z,16Z)/14:1(9Z)), PI(16:0/20:3(8Z,11Z,14Z)), PI(18:1(9Z)/18:2(9Z,12Z)) PI(36:4) PI(18:2_18:2), PI(14:0/22:4(7Z,10Z,13Z,16Z)), 858.5258 PI(16:1(9Z)/20:3(8Z,11Z,14Z)), PI(18:1(9Z)/18:3(6Z,9Z,12Z)), PI(18:3(6Z,9Z,12Z)/18:1(9Z)), PI(18:4(6Z,9Z,12Z,15Z)/18:0), PI(20:3(8Z,11Z,14Z)/16:1(9Z)), PI(22:4(7Z,10Z,13Z,16Z)/14:0), PI(20:4(5Z,8Z,11Z,14Z)/16:0), PI(18:3(9Z,12Z,15Z)/18:1(9Z)), PI(18:1(9Z)/18:3(9Z,12Z,15Z)), PI(18:0/18:4(6Z,9Z,12Z,15Z)), PI(18:2(9Z,12Z)/18:2(9Z,12Z)), PI(16:0/20:4(5Z,8Z,11Z,14Z)) PI(38:6) PI(16:0_22:6), PI(18:2_20:4), PI(18:3_20:3), PI(18:4_20:2), 882.5258 PI(20:2_18:4), PI(20:3_18:3), PI(20:4_18:2), PI(22:6_16:0), PI(20:5_18:1), PI(18:1_20:5), PI(18:2(9Z,12Z)/20:4(5Z,8Z,11Z,14Z)), PI(20:4(5Z,8Z,11Z,14Z)/18:2(9Z,12Z)), PI(16:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)), PI(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/16:0) PI(40:5) PI 18:0/22:5; PI(18:1(9Z)/22:4(7Z,10Z,13Z,16Z)); 930.60660 PI(18:3(6Z,9Z,12Z)/22:2(13Z,16Z)); PI(18:3(9Z,12Z,15Z)/22:2(13Z,16Z)); PI(18:4(6Z,9Z,12Z,15Z)/22:1(11Z)); PI(20:0/20:5(5Z,8Z,11Z,14Z,17Z)); PI(20:1(11Z)/20:4(5Z,8Z,11Z,14Z)); PI(20:2(11Z,14Z)/20:3(8Z,11Z,14Z)); Lipid Biomarker Isomers and Isobars m/z* PI(20:3(8Z,11Z,14Z)/20:2(11Z,14Z)); PI(20:4(5Z,8Z,11Z,14Z)/20:1(11Z)); PI(20:5(5Z,8Z,11Z,14Z,17Z)/20:0); PI(22:1(11Z)/18:4(6Z,9Z,12Z,15Z)); PI(22:2(13Z,16Z)/18:3(6Z,9Z,12Z)); PI(22:2(13Z,16Z)/18:3(9Z,12Z,15Z)); PI(22:4(7Z,10Z,13Z,16Z)/18:1(9Z)) PS(36:1) PS(18:0_18:1), PS(18:0/18:1(9Z)), PS(18:1(9Z)/18:0), 789.552 PS(14:0/22:1(11Z)), PS(14:1(9Z)/22:0), PS(15:1(9Z)/21:0), PS(16:1(9Z)/20:0), PS(17:0/19:1(9Z)), PS(17:1(9Z)/19:0), PS(19:0/17:1(9Z)), PS(19:1(9Z)/17:0), PS(20:0/16:1(9Z)), PS(20:1(11Z)/16:0), PS(21:0/15:1(9Z)), PS(22:0/14:1(9Z)), PS(22:1(11Z)/14:0), PS(16:0/20:1(11Z)) PS(38:4) PS(18:0_20:4), PS(18:1_20:3), PS(18:2_20:2), PS(18:3_20:1), 811.5363 PS(18:4_20:0), PS(20:1_18:3), PS(20:2_18:2), PS(20:3_18:1), PS(20:4_18:0), PS(22:4_16:0), PS(20:0_18:4), PS(16:0_22:4), PS(18:0/20:4(5Z,8Z,11Z,14Z)), PS(20:4(5Z,8Z,11Z,14Z)/18:0) PS(40:6) PS(18:0_22:6), PS(18:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)), 835.5363 PS(18:2(9Z,12Z)/22:4(7Z,10Z,13Z,16Z)), PS(18:4(6Z,9Z,12Z,15Z)/22:2(13Z,16Z)), PS(20:1(11Z)/20:5(5Z,8Z,11Z,14Z,17Z)), PS(20:2(11Z,14Z)/20:4(5Z,8Z,11Z,14Z)), PS(20:3(8Z,11Z,14Z)/20:3(8Z,11Z,14Z)), PS(20:4(5Z,8Z,11Z,14Z)/20:2(11Z,14Z)), PS(20:5(5Z,8Z,11Z,14Z,17Z)/20:1(11Z)), PS(22:2(13Z,16Z)/18:4(6Z,9Z,12Z,15Z)), PS(22:4(7Z,10Z,13Z,16Z)/18:2(9Z,12Z)), PS(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/18:0) PS(40:7) PS(20:3_20:4), PS(18:1_22:6), PS(18:3_22:4), PS(20:2_20:5), 833.5207 PS(20:4_20:3), PS(20:5_20:2), PS(22:4_18:3), PS(22:6_18:1) SM(d35:1) SM(d18:1_17:0), SM(d19:1_16:0) 716.5832 SM(d36:2) SM(d18:0_18:2), SM(d16:1/20:1), SM(d18:0/18:2), 728.5832 SM(d18:1/18:1(9Z)), SM(d18:2/18:0), SM(d19:1/17:1) SM(d37:1) SM(d18:1/19:0), SM(d19:1/18:0) 744.6145 SM(d38:4) SM(d38:4) 752.5832 SM(d39:2) SM(d18:2/21:0) 770.6302 SM(d40:2) SM(d16:1/24:1), SM(d18:1/22:1), SM(d18:2/22:0) 784.6458 SM(d41:2) SM(d18:1_23:1), SM(d17:1_24:1) 798.6615 SM(d41:3) SM(d41:3), SM(d18:2(4E,14Z)/23:1(13Z)) 796.6458 SM(d42:4) SM(d18:1_24:3) 808.6458 Lipid Biomarker Isomers and Isobars m/z* SphP(d18:1) SphP(d18:1) 379.24876 2 TG(42:2) TG(12:0_12:0_18:2), TG(12:0/12:0/18:2(9Z,12Z)), 718.6111 TG(12:0/15:1(9Z)/15:1(9Z)), TG(14:0/14:1(9Z)/14:1(9Z)), TG(12:0/13:0/17:2(9Z,12Z)), TG(12:0/14:1(9Z)/16:1(9Z)), TG(13:0/14:1(9Z)/15:1(9Z)) TG(44:2) TG(16:0_10:0_18:2), TG(16:0_10:1_18:1), 746.6424 TG(12:0/12:0/20:2(11Z,14Z)), TG(12:0/16:1(9Z)/16:1(9Z)), TG(13:0/13:0/18:2(9Z,12Z)), TG(14:0/15:1(9Z)/15:1(9Z)), TG(14:1(9Z)/14:1(9Z)/16:0), TG(12:0/14:0/18:2(9Z,12Z)), TG(12:0/14:1(9Z)/18:1(9Z)), TG(12:0/15:0/17:2(9Z,12Z)), TG(12:0/15:1(9Z)/17:1(9Z)), TG(13:0/14:0/17:2(9Z,12Z)), TG(13:0/14:1(9Z)/17:1(9Z)), TG(13:0/15:1(9Z)/16:1(9Z)), TG(14:0/14:1(9Z)/16:1(9Z)), TG(14:1(9Z)/15:0/15:1(9Z)) TG(50:1e) TG(16:0e_16:0_18:1) 818.7727 TG(51:0) TG(18:0_16:0_17:0), TG(17:0/17:0/17:0), TG(16:0/17:0/18:0), 848.7833 TG(16:0/16:0/19:0), TG(13:0/19:0/19:0), TG(15:0/15:0/21:0), TG(15:0/18:0/18:0), TG(12:0/17:0/22:0), TG(12:0/18:0/21:0), TG(12:0/19:0/20:0), TG(13:0/16:0/22:0), TG(13:0/17:0/21:0), TG(13:0/18:0/20:0), TG(14:0/15:0/22:0), TG(14:0/16:0/21:0), TG(14:0/17:0/20:0), TG(14:0/18:0/19:0), TG(15:0/16:0/20:0), TG(15:0/17:0/19:0), TG(52:3e) TG(16:0e_18:1_18:2), TG(O-16:0_18:2_18:1), TG(O- 842.7727 16:0/18:1(9Z)/18:2(9Z,12Z)), TG(O- 16:0/18:2(9Z,12Z)/18:1(9Z)) TG(53:0) TG(18:0_18:0_17:0), TG(17:0/18:0/18:0), TG(16:0/17:0/20:0), 876.8146 TG(17:0/17:0/19:0), TG(16:0/18:0/19:0), TG(16:0/16:0/21:0), TG(13:0/20:0/20:0), TG(15:0/19:0/19:0), TG(12:0/19:0/22:0), TG(12:0/20:0/21:0), TG(13:0/18:0/22:0), TG(13:0/19:0/21:0), TG(14:0/17:0/22:0), TG(14:0/18:0/21:0), TG(14:0/19:0/20:0), TG(15:0/16:0/22:0), TG(15:0/17:0/21:0), TG(15:0/18:0/20:0) TG(56:0) TG(18:0_18:0_20:0), TG(16:0/20:0/20:0), TG(18:0/18:0/20:0), 918.8615 TG(17:0/19:0/20:0), TG(17:0/17:0/22:0), TG(16:0/18:0/22:0), TG(18:0/19:0/19:0), TG(17:0/18:0/21:0), TG(16:0/19:0/21:0), TG(12:0/22:0/22:0), TG(14:0/21:0/21:0), TG(13:0/21:0/22:0), TG(14:0/20:0/22:0), TG(15:0/19:0/22:0), TG(15:0/20:0/21:0) TG(56:1) TG(16:0_18:1_22:0), TG(16:1_20:0_20:0), 916.8459 TG(18:0_18:1_20:0), TG(16:0_20:0_20:1), TG(18:0_18:0_20:1), TG(17:1_19:0_20:0), TG(17:0_19:0_20:1), TG(17:0_17:1_22:0), Lipid Biomarker Isomers and Isobars m/z* TG(17:0_17:0_22:1), TG(16:0_18:1_22:0), TG(16:1_18:0_22:0), TG(16:0_18:0_22:1), TG(18:1_19:0_19:0), TG(17:0_18:1_21:0), TG(17:1_18:0_21:0), TG(16:1_19:0_21:0), TG(14:1_21:0_21:0), TG(17:0_17:0_22:1), TG(12:0_22:0_22:1), TG(13:0_21:0_22:1), TG(14:0_20:0_22:1), TG(14:0_20:1_22:0), TG(14:1_20:0_22:0), TG(15:0_19:0_22:1), TG(15:0_19:1_22:0), TG(15:0_20:1_21:0), TG(15:1_19:0_22:0), TG(15:1_20:0_21:0), TG(16:0_18:0_22:1), TG(16:0_19:1_21:0), TG(17:0_19:1_20:0), TG(18:0_19:0_19:1), TG(16:0/18:1(9Z)/22:0), TG(18:1(9Z)/16:0/22:0) TG(58:1) TG(18:0_18:1_22:0), TG(19:0_19:0_20:1), 944.8772 TG(18:0_18:0_22:1), TG(18:0_18:0_22:1), TG(17:1_20:0_21:0), TG(17:1_19:0_22:0), TG(16:1_21:0_21:0), TG(16:1_20:0_22:0), TG(18:1_20:0_20:0), TG(18:1_19:0_21:0), TG(15:1_21:0_22:0), TG(14:1_22:0_22:0), TG(17:0_20:1_21:0), TG(17:0_19:1_22:0), TG(17:0_19:0_22:1), TG(17:0_19:0_22:1), TG(16:0_20:1_22:0), TG(16:0_20:0_22:1), TG(16:0_20:0_22:1), TG(19:0_19:1_20:0), TG(18:0_19:1_21:0), TG(18:0_18:1_22:0), TG(18:0_20:0_20:1), TG(15:0_21:0_22:1), TG(14:0_22:0_22:1), TG(18:0/18:1(9Z)/22:0), TG(18:1(9Z)/18:0/22:0), TG(18:0/18:0/22:1(13Z)) TG(58:2) TG(18:1_18:1_22:0), TG(18:2_20:0_20:0), 942.8615 TG(18:0_20:1_20:1), TG(18:1_20:0_20:1), TG(18:0_20:0_20:2), TG(16:0_20:2_22:0), TG(16:1_20:1_22:0), TG(18:0_18:2_22:0), TG(18:1_18:1_22:0), TG(16:0_20:1_22:1), TG(16:1_20:0_22:1), TG(18:0_18:1_22:1), TG(19:0_19:0_20:2), TG(17:0_20:2_21:0), TG(17:1_20:1_21:0), TG(17:2_20:0_21:0), TG(17:2_19:0_22:0), TG(17:1_19:0_22:1), TG(16:0_20:0_22:2), TG(18:0_18:0_22:2), TG(17:0_19:0_22:2), TG(18:2_19:0_21:0), TG(19:1_19:1_20:0), TG(14:0_22:0_22:2), TG(14:1_22:0_22:1), TG(15:0_21:0_22:2), TG(15:1_21:0_22:1), TG(16:0_20:1_22:1), Lipid Biomarker Isomers and Isobars m/z* TG(16:1_20:0_22:1), TG(17:0_19:1_22:1), TG(17:1_19:0_22:1), TG(17:1_19:1_22:0), TG(18:0_18:1_22:1), TG(18:1_19:1_21:0), TG(19:0_19:1_20:1), TG(18:1(9Z)/18:1(9Z)/22:0), TG(18:1(9Z)/22:0/18:1(9Z)), TG(18:0/18:1(9Z)/22:1(11Z)) TG(59:1) TG(25:0_16:0_18:1), TG(19:0/20:0/20:1(11Z)), 958.8928 TG(17:0/20:1(11Z)/22:0), TG(17:1(9Z)/20:0/22:0), TG(17:0/20:0/22:1(13Z)), TG(18:0/20:1(11Z)/21:0), TG(18:1(9Z)/20:0/21:0), TG(18:1(9Z)/19:0/22:0), TG(18:0/19:0/22:1(13Z)), TG(16:1(9Z)/21:0/22:0), TG(16:0/21:0/22:1(13Z)), TG(17:1(9Z)/21:0/21:0), TG(15:1(9Z)/22:0/22:0), TG(19:1(9Z)/20:0/20:0), TG(15:0/22:0/22:1(11Z)), TG(16:0/21:0/22:1(11Z)), TG(17:0/20:0/22:1(11Z)), TG(18:0/19:0/22:1(11Z)), TG(18:0/19:1(9Z)/22:0), TG(19:0/19:1(9Z)/21:0), TG(60:5) TG(18:1_20:4_22:0), 964.8459 TG(20:1(11Z)/20:2(11Z,14Z)/20:2(11Z,14Z)), TG(20:0/20:2(11Z,14Z)/20:3(8Z,11Z,14Z)), TG(20:1(11Z)/20:1(11Z)/20:3(8Z,11Z,14Z)), TG(20:0/20:1(11Z)/20:4(5Z,8Z,11Z,14Z)), TG(20:0/20:0/20:5(5Z,8Z,11Z,14Z,17Z)), TG(18:0/20:2(11Z,14Z)/22:3(10Z,13Z,16Z)), TG(18:1(9Z)/20:1(11Z)/22:3(10Z,13Z,16Z)), TG(18:2(9Z,12Z)/20:0/22:3(10Z,13Z,16Z)), TG(18:0/20:5(5Z,8Z,11Z,14Z,17Z)/22:0), TG(18:1(9Z)/20:4(5Z,8Z,11Z,14Z)/22:0), TG(18:2(9Z,12Z)/20:3(8Z,11Z,14Z)/22:0), TG(18:3(9Z,12Z,15Z)/20:2(11Z,14Z)/22:0), TG(18:0/20:4(5Z,8Z,11Z,14Z)/22:1(13Z)), TG(18:1(9Z)/20:3(8Z,11Z,14Z)/22:1(13Z)), TG(18:2(9Z,12Z)/20:2(11Z,14Z)/22:1(13Z)), TG(18:3(9Z,12Z,15Z)/20:1(11Z)/22:1(13Z)), TG(18:0/20:1(11Z)/22:4(7Z,10Z,13Z,16Z)), TG(18:1(9Z)/20:0/22:4(7Z,10Z,13Z,16Z)), TG(18:0/20:0/22:5(7Z,10Z,13Z,16Z,19Z)), TG(16:1(9Z)/22:1(13Z)/22:3(10Z,13Z,16Z)), TG(18:0/20:3(8Z,11Z,14Z)/22:2(13Z,16Z)), TG(18:1(9Z)/20:2(11Z,14Z)/22:2(13Z,16Z)), TG(18:2(9Z,12Z)/20:1(11Z)/22:2(13Z,16Z)), TG(18:3(9Z,12Z,15Z)/20:0/22:2(13Z,16Z)), TG(17:2(9Z,12Z)/21:0/22:3(10Z,13Z,16Z)), Lipid Biomarker Isomers and Isobars m/z* TG(16:0/22:3(10Z,13Z,16Z)/22:2(13Z,16Z)), TG(16:0/22:1(13Z)/22:4(7Z,10Z,13Z,16Z)), TG(16:1(9Z)/22:0/22:4(7Z,10Z,13Z,16Z)), TG(16:0/22:0/22:5(7Z,10Z,13Z,16Z,19Z)), TG(19:0/20:5(5Z,8Z,11Z,14Z,17Z)/21:0), TG(17:1(9Z)/21:0/22:4(7Z,10Z,13Z,16Z)), TG(17:0/21:0/22:5(7Z,10Z,13Z,16Z,19Z)), TG(19:0/19:0/22:5(7Z,10Z,13Z,16Z,19Z)), TG(16:1(9Z)/22:2(13Z,16Z)/22:2(13Z,16Z)), TG(19:1(9Z)/19:1(9Z)/22:3(10Z,13Z,16Z)), TG(16:0/22:1(11Z)/22:4(7Z,10Z,13Z,16Z)), TG(16:0/22:2(13Z,16Z)/22:3(10Z,13Z,16Z)), TG(16:1(9Z)/22:1(11Z)/22:3(10Z,13Z,16Z)), TG(18:0/20:4(5Z,8Z,11Z,14Z)/22:1(11Z)), TG(18:1(9Z)/20:3(8Z,11Z,14Z)/22:1(11Z)), TG(18:2(9Z,12Z)/20:2(11Z,14Z)/22:1(11Z)), TG(18:3(6Z,9Z,12Z)/20:0/22:2(13Z,16Z)), TG(18:3(6Z,9Z,12Z)/20:1(11Z)/22:1(11Z)), TG(18:3(6Z,9Z,12Z)/20:2(11Z,14Z)/22:0), TG(18:3(9Z,12Z,15Z)/20:1(11Z)/22:1(11Z)), TG(18:4(6Z,9Z,12Z,15Z)/20:0/22:1(11Z)), TG(18:4(6Z,9Z,12Z,15Z)/20:1(11Z)/22:0), TG(19:0/19:1(9Z)/22:4(7Z,10Z,13Z,16Z)), TG(19:1(9Z)/20:4(5Z,8Z,11Z,14Z)/21:0), *as measured by the mass spectrometry methods described herein. It is contemplated that reference to PC(36:2) herein may include one or both of isomers PC(18:1_18:1) and PC(18:0_18:2). As such, in particular examples, measuring a level of PC(36:2) includes measuring a level of one or both of the isomers PC(18:1_18:1) and PC(18:0_18:2). In other examples, measuring a level of PC(36:2) includes measuring a level of PC(18:1_18:1). For certain examples, measuring a level of PC(36:2) includes measuring a level of PC(18:0_18:2). Accordingly, the one or more lipid biomarkers described herein can be selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(18:1_18:1), PC(18:0_18:2), PC(36:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a variant or derivative thereof. Further, it is envisaged that reference to TG(44:2) herein may include one or both of isomers TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1). As such, in some examples, measuring a level of TG(44:2) includes measuring a level of one or both of the isomers TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1). In other examples, measuring a level of TG(44:2) includes measuring a level of TG(16:0_10:0_18:2). For certain examples, measuring a level of TG(44:2) includes measuring a level of TG(16:0_10:1_18:1). Accordingly, the one or more lipid biomarkers described herein can be selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(16:0_10:0_18:2), TG(16:0_10:1_18:1), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a variant or derivative thereof. Similarly, it is also contemplated that reference to PC(36:4) herein may include one or both of isomers PC(18:2/18:2) and PC(16:0/20:4). In this regard, it has been observed by the present inventors that the levels of PC 36:4 can increase or decrease in cancer patients. Without being bound by any theory, this is hypothesised to be isomer dependent in that the levels of the PC 18:2_18:2 isomer have been observed to decrease in breast cancer patients, whilst the levels of the PC 16:0_20:4 isomer have been observed to decrease in breast cancer patients (see e.g., Figure 25). As such, in some examples, measuring a level of PC(36:4) includes measuring a level of one or both of the isomers PC(18:2/18:2) and PC(16:0/20:4). In other examples, measuring a level of PC(36:4) includes measuring a level of PC(18:2/18:2). For certain examples, measuring a level of PC(36:4) includes measuring a level of PC(16:0/20:4). Accordingly, the one or more lipid biomarkers described herein can be selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(18:2/18:2), PC(16:0/20:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a variant or derivative thereof. In view of the above, the one or more lipid biomarkers described herein can suitably be selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(18:1_18:1), PC(18:0_18:2), PC(18:2/18:2), PC(16:0/20:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(16:0_10:0_18:2), TG(16:0_10:1_18:1), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a variant or derivative thereof. It is also envisaged that the lipid biomarkers described herein may encompass or be interchangeable with one or more isobars thereof. The term “isobar” typically refers to different lipids that have nearly or substantially the same mass (e.g., m/z ratio) and may not be distinguished from each other on the analytical platform used in their detection (e.g., for mass spectrometry, the different lipids in an isobar can elute at nearly the same time and have similar or the same quant ions, and thus cannot be distinguished). As the skilled person will appreciate, lipids can be defined according to the following equation: XXX(YY:ZZ), in which XXX is the abbreviation for the lipid class or group (in many instances indicating the lipid headgroup), YY is the number of carbons in the acyl chain and ZZ is the number of double bonds in the acyl chains. Similar notation (e.g., XXX(YY1:ZZ1_YY2:ZZ2) or XXX(YY1:ZZ1_YY2:ZZ2_YY3_ZZ3) may be used to define lipid isomers, wherein the numbers refer to the particular acyl chain of the lipid. It is envisaged, however, that the lipids defined herein may be identified by different naming annotations or nomenclature as are known in the art (see, e.g., Liebisch et al., J Lipid Res, 2013 Jun;54(6):1523-1530; Lipidomics Standards Initiative Consortium, Nat Metab, 2019 Aug;1(8):745-747). It is also envisaged that the recited lipid biomarkers may additionally cover one or more further lipid biomarkers that behave similarly or equivalently (e.g., demonstrate a similar concentration profile) to said lipid biomarker. To this end, the lipid biomarker may demonstrate substantial collinearity with one or more further lipid biomarkers in terms of, for example, being diagnostic or indicative of breast cancer in a subject. Collinearity refers to a strong correlation or linear relationship between a pair of predictors (e.g., a pair of lipid biomarkers), and collinearity between multiple predictors is called multi-collinearity. As such, in some examples, the one or more lipid biomarkers comprises one or more further lipid biomarkers, such as those outlined in Example 1 below, that demonstrate collinearity with one or more of the one or more lipid biomarkers recited in the examples provided herein. In other examples, the lipid biomarker demonstrates little or no collinearity with one or more further lipid biomarkers. Also provided are fragments of the lipid biomarkers, inclusive of a lipid headgroup and an acyl chain or fragments thereof, that comprise less than 100% of an entire lipid biomarker molecule. In this regard, the skilled person will appreciate that MRM analysis of lipid biomarkers by mass spectrometry can include fragmenting lipids into their component parts (e.g., lipid headgroups and one or more acyl chains) so as to assist in identification and quantification of said lipid biomarker, as described in more detail below. High-resolution accurate-mass MS (HRMS) may also be utilised to perform reliable and sensitive quantitative analyses of lipid biomarkers, similar to that of MRM (see Rochat, Trends in Analytical Chemistry, 2016 for review). Parallel reaction monitoring (PRM) is an ion monitoring technique based on high-resolution and high-precision mass spectrometry. PRM can be based on, for example, a Q Exactive Orbitrap™ (Thermo Scientific™) system or a Sciex 7500 (Sciex™) system as the representative quadrupole-high resolution mass spectrum platform. Unlike MRM, which monitors specific transitions at a time, the high resolution and mass accuracy of full-scan (MS1) and tandem mass spectrometry (MS/MS) scan of PRM can result in sufficient selectivity by monitoring all MS/MS fragment ions for each target precursor lipid. Suitably, the level (e.g., concentration or expression level) of two or more of the lipid biomarkers (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62 or 63 lipid biomarkers) provided herein are determined for the methods described herein. In some examples, the methods described herein include the step of determining the level or concentration of three or more lipid biomarkers described herein. In other examples, the methods described herein include the step of determining the level or concentration of four or more lipid biomarkers described herein. In certain examples, the methods described herein include the step of determining the level or concentration of five or more lipid biomarkers described herein. In some examples, the methods described herein include the step of determining the level or concentration of six or more lipid biomarkers described herein. In various examples, the methods described herein include the step of determining the level or concentration of seven or more lipid biomarkers described herein. In particular examples, the methods described herein include the step of determining the level or concentration of eight or more lipid biomarkers described herein. In some examples, the methods described herein include the step of determining the level or concentration of nine or more lipid biomarkers described herein. In other examples, the methods described herein include the step of determining the level or concentration of ten or more lipid biomarkers described herein. In certain examples, the methods described herein include the step of determining the level or concentration of eleven or more lipid biomarkers described herein. In some examples, the methods described herein include the step of determining the level or concentration of twelve or more lipid biomarkers described herein. In various examples, the methods described herein include the step of determining the level or concentration of thirteen or more lipid biomarkers described herein. In particular examples, the methods described herein include the step of determining the level or concentration of fourteen or more lipid biomarkers described herein. In various examples, the methods described herein include the step of determining the level or concentration of fifteen or more lipid biomarkers described herein. In particular examples, the methods described herein include the step of determining the level or concentration of sixteen or more lipid biomarkers described herein. In some examples, the methods described herein include the step of determining the level or concentration of seventeen or more lipid biomarkers described herein. In certain examples, the methods described herein include the step of determining the level or concentration of eighteen or more lipid biomarkers described herein. In particular examples, the methods described herein include the step of determining the level or concentration of nineteen or more lipid biomarkers described herein. In various examples, the methods described herein include the step of determining the level or concentration of twenty or more lipid biomarkers described herein. In particular examples, the methods described herein include the step of determining the level or concentration of twenty-one or more lipid biomarkers described herein. In some examples, the methods described herein include the step of determining the level or concentration of twenty-two or more lipid biomarkers described herein. In certain examples, the methods described herein include the step of determining the level or concentration of twenty-three or more lipid biomarkers described herein. In various examples, the methods described herein include the step of determining the level or concentration of twenty-four or more lipid biomarkers described herein. In particular examples, the methods described herein include the step of determining the level or concentration of twenty-five or more lipid biomarkers described herein. In some examples, the methods described herein include the step of determining the level or concentration of twenty-six or more lipid biomarkers described herein. In certain examples, the methods described herein include the step of determining the level or concentration of twenty-seven or more lipid biomarkers described herein. In various examples, the methods described herein include the step of determining the level or concentration of twenty-eight or more lipid biomarkers described herein. In particular examples, the methods described herein include the step of determining the level or concentration of twenty-nine or more lipid biomarkers described herein. In some examples, the methods described herein include the step of determining the level or concentration of thirty or more lipid biomarkers described herein. In various examples, the methods of the present disclosure include the step of determining a level of LPC(14:0) and at least one further lipid biomarker described herein (e.g., one or more of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2) (e.g., PC(18:1_18:1) and PC(18:0_18:2)), PC(36:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2) (e.g., TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), and fragments, variants or derivatives thereof). For some examples, the methods of the present disclosure include the step of determining a level of AcCa(18:2) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of Cer(d36:1) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of Cer(d18:1/18:0) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of Cer(d38:1) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of Cer(d18:1/20:0) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of Cer(d39:1) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of Cer(d16:1/23:0) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of Cer(d40:1) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of Cer(d18:1/22:0) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of Cer(d41:1) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of Cer(d18:1/23:0) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of Cer(d41:2) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of Cer(d17:1/24:1) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of Cer(d42:2) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of Cer(d18:1/24:1) and at least one further lipid biomarker described herein. According to particular examples, the methods of the present disclosure include the step of determining a level of LPA(18:0) and at least one further lipid biomarker described herein. In certain examples, the methods of the present disclosure include the step of determining a level of LPA(18:2) and at least one further lipid biomarker described herein. In some examples, the methods of the present disclosure include the step of determining a level of LPC(16:0) and at least one further lipid biomarker described herein. According to particular examples, the methods of the present disclosure include the step of determining a level of LPC(16:0e) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of LPC(18:0) and at least one further lipid biomarker described herein. For some examples, the methods of the present disclosure include the step of determining a level of LPC(18:2) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of LPC(18:3) and at least one further lipid biomarker described herein. According to particular examples, the methods of the present disclosure include the step of determining a level of LPI(18:0) and at least one further lipid biomarker described herein. According to particular examples, the methods of the present disclosure include the step of determining a level of LPI(18:1) and at least one further lipid biomarker described herein. In certain examples, the methods of the present disclosure include the step of determining a level of PC(32:2) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PC(14:0/18:2) and at least one further lipid biomarker described herein. In particular examples, the methods of the present disclosure include the step of determining a level of PC(34:0) and at least one further lipid biomarker described herein. In certain examples, the methods of the present disclosure include the step of determining a level of PC(36:0) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PC(18:0/18:0) and at least one further lipid biomarker described herein. In various examples, the methods of the present disclosure include the step of determining a level of PC(36:2), such as PC(18:1_18:1) and/or PC(18:0_18:2), and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PC(18:1_18:1) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PC(18:0_18:2) and at least one further lipid biomarker described herein. In some examples, the methods of the present disclosure include the step of determining a level of PC(36:4), such as PC(18:2/18:2) and/or PC(16:0/20:4) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PC(18:2/18:2) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PC(16:0/20:4) and at least one further lipid biomarker described herein. Even more particularly, the methods of the present disclosure can include the step of determining a level of PC(16:0/20:4) and PC(18:2/18:2) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of PC(36:5e) and at least one further lipid biomarker described herein. For some examples, the methods of the present disclosure include the step of determining a level of PC(38:5) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PC(18:1/20:4) and at least one further lipid biomarker described herein. In particular examples, the methods of the present disclosure include the step of determining a level of PE(34:2p) and at least one further lipid biomarker described herein. For some examples, the methods of the present disclosure include the step of determining a level of PE(36:2) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PE(18:0/18:2) and at least one further lipid biomarker described herein. In certain examples, the methods of the present disclosure include the step of determining a level of PE(36:2p) and at least one further lipid biomarker described herein. In various examples, the methods of the present disclosure include the step of determining a level of PE(36:3p) and at least one further lipid biomarker described herein. In some examples, the methods of the present disclosure include the step of determining a level of PE(36:5p) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of PE(38:2p) and at least one further lipid biomarker described herein. In particular examples, the methods of the present disclosure include the step of determining a level of PE(38:6p) and at least one further lipid biomarker described herein. In certain examples, the methods of the present disclosure include the step of determining a level of PE(38:6e) and at least one further lipid biomarker described herein. In various examples, the methods of the present disclosure include the step of determining a level of PE(40:6e) and at least one further lipid biomarker described herein. For some examples, the methods of the present disclosure include the step of determining a level of PE(40:7e) and at least one further lipid biomarker described herein. In some examples, the methods of the present disclosure include the step of determining a level of PG(36:1) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of PI(36:1) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PI(18:0/18:1) and at least one further lipid biomarker described herein. In particular examples, the methods of the present disclosure include the step of determining a level of PI(36:3) and at least one further lipid biomarker described herein. In certain examples, the methods of the present disclosure include the step of determining a level of PI(36:4) and at least one further lipid biomarker described herein. In various examples, the methods of the present disclosure include the step of determining a level of PI(38:6) and at least one further lipid biomarker described herein. In various examples, the methods of the present disclosure include the step of determining a level of PI(40:5) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PI(18:0/22:5) and at least one further lipid biomarker described herein. In some examples, the methods of the present disclosure include the step of determining a level of PS(36:1) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PS(18:0/18:1) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of PS(38:4) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of PS(18:0/20:4) and at least one further lipid biomarker described herein. For some examples, the methods of the present disclosure include the step of determining a level of PS(40:6) and at least one further lipid biomarker described herein. In particular examples, the methods of the present disclosure include the step of determining a level of PS(40:7) and at least one further lipid biomarker described herein. In certain examples, the methods of the present disclosure include the step of determining a level of SM(d35:1) and at least one further lipid biomarker described herein. According to other examples, the methods of the present disclosure include the step of determining a level of SM(d36:2) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of SM(d18:1/18:1) and at least one further lipid biomarker described herein. In various examples, the methods of the present disclosure include the step of determining a level of SM(d37:1) and at least one further lipid biomarker described herein. According to particular examples, the methods of the present disclosure include the step of determining a level of SM(d38:4) and at least one further lipid biomarker described herein. In some examples, the methods of the present disclosure include the step of determining a level of SM(d39:2) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of SM(d40:2) and at least one further lipid biomarker described herein. In particular examples, the methods of the present disclosure include the step of determining a level of SM(d41:2) and at least one further lipid biomarker described herein. In certain examples, the methods of the present disclosure include the step of determining a level of SM(d41:3) and at least one further lipid biomarker described herein. According to particular examples, the methods of the present disclosure include the step of determining a level of SM(d42:4) and at least one further lipid biomarker described herein. For some examples, the methods of the present disclosure include the step of determining a level of SphP(d18:1) and at least one further lipid biomarker described herein. In various examples, the methods of the present disclosure include the step of determining a level of TG(42:2) and at least one further lipid biomarker described herein. In some examples, the methods of the present disclosure include the step of determining a level of TG(44:2), such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1), and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of TG(16:0_10:0_18:2) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of TG(16:0_10:1_18:1) and at least one further lipid biomarker described herein. More particularly, the methods of the present disclosure can include the step of determining a level of TG(16:0_10:1_18:1) and TG(16:0_10:0_18:2) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of TG(50:1e) and at least one further lipid biomarker described herein. In particular examples, the methods of the present disclosure include the step of determining a level of TG(51:0) and at least one further lipid biomarker described herein. In certain examples, the methods of the present disclosure include the step of determining a level of TG(52:3e) and at least one further lipid biomarker described herein. In various examples, the methods of the present disclosure include the step of determining a level of TG(53:0) and at least one further lipid biomarker described herein. In some examples, the methods of the present disclosure include the step of determining a level of TG(56:0) and at least one further lipid biomarker described herein. In other examples, the methods of the present disclosure include the step of determining a level of TG(56:1) and at least one further lipid biomarker described herein. In particular examples, the methods of the present disclosure include the step of determining a level of TG(58:1) and at least one further lipid biomarker described herein. In certain examples, the methods of the present disclosure include the step of determining a level of TG(58:2) and at least one further lipid biomarker described herein. In various examples, the methods of the present disclosure include the step of determining a level of TG(59:1) and at least one further lipid biomarker described herein. In some examples, the methods of the present disclosure include the step of determining a level of TG(60:5) and at least one further lipid biomarker described herein. Any of the methods disclosed herein may not include measuring any other biomarker. Thus, the methods disclosed herein may comprise excluding from analysis any other biomarker. In some examples, the one or more lipid biomarkers may not include one or more of Cer(d36:1), Cer(d38:1), LPC(16:0), LPC(18:0), PC(36:2), PC(36:4), PC(36:5e), PC(38:5), PE(36:5p), PE(38:2p), SM(d40:2), TG(50:1e) and TG(52:3e), or a fragment, variant or derivative thereof. In some examples, the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2) (e.g., TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(51:0), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. For such examples, an increased level of Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PI(40:5), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d41:2), SM(d41:3) and/or SM(d42:4), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SphP(d18:1), TG(42:2), TG(44:2) (e.g., TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(51:0), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and/or TG(60:5), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PI(40:5), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d41:2), SM(d41:3) and/or SM(d42:4), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SphP(d18:1), TG(42:2), TG(44:2) (e.g., TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), TG(51:0), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and/or TG(60:5), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. In certain examples, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2) (such as, TG(16:0_10:0_18:2) and/or TG(16:0_10:1_18:1)), TG(56:0), TG(56:1), TG(58:2) and TG(60:5), or a fragment, variant or derivative thereof. For such examples, a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2) (such as, TG(16:0_10:0_18:2) and/or TG(16:0_10:1_18:1)), TG(56:0), TG(56:1), TG(58:2) and/or TG(60:5), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2) (such as, TG(16:0_10:0_18:2) and/or TG(16:0_10:1_18:1)), TG(56:0), TG(56:1), TG(58:2) and/or TG(60:5), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. In particular examples, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4) and TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), or a fragment, variant or derivative thereof. For such examples, a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4) and/or TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4) and/or TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. In other examples, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)) and TG(56:1), or a fragment, variant or derivative thereof. For such examples, a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)) and/or TG(56:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)) and/or TG(56:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. In various examples, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)) and TG(58:2), or a fragment, variant or derivative thereof. For such examples, a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)) and/or TG(58:2), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(44:2) (such as TG(16:0_10:0_18:2) and TG(16:0_10:1_18:1)) and/or TG(58:2), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. In some examples, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d37:1), SM(d39:2), SM(d41:2), SM(d41:3), TG(51:0), TG(53:0), TG(58:1), TG(58:2) and TG(59:1), or a fragment, variant or derivative thereof. For such examples, an increased level of SM(d35:1), SM(d37:1), SM(d39:2), SM(d41:2) and/or SM(d41:3), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), TG(51:0), TG(53:0), TG(58:1), TG(58:2) and/or TG(59:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of SM(d35:1), SM(d37:1), SM(d39:2), SM(d41:2) and/or SM(d41:3), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), TG(51:0), TG(53:0), TG(58:1), TG(58:2) and/or TG(59:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. In other examples, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d39:2), SM(d41:2), TG(53:0), TG(58:1) and TG(58:2), or a fragment, variant or derivative thereof. For such examples, an increased level of SM(d35:1), SM(d39:2) and/or SM(d41:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), TG(53:0), TG(58:1) and/or TG(58:2), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of SM(d35:1), SM(d39:2) and/or SM(d41:2), or a fragment, variant or derivative thereof, and/or a decreased level of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), TG(53:0), TG(58:1) and/or TG(58:2), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. In particular examples, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4) and TG(58:2), or a fragment, variant or derivative thereof. For such examples, a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4) and/or TG(58:2), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(36:3p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4) and/or TG(58:2), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. In certain examples, the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PE(34:2p), PE(36:2p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1) and PS(38:4), or a fragment, variant or derivative thereof. For such examples, a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1) and/or PS(38:4), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, a decreased level of LPC(14:0), PE(34:2p), PE(36:2p), PE(40:6e), PG(36:1), PI(36:4), PS(36:1) and/or PS(38:4), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Referring to other examples, the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. For such examples, an increased level of SM(d35:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of SM(d35:1) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Referring to other examples, the one or more lipid biomarkers may be selected from the group consisting of AcCa(18:2), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. More particularly, the one or more lipid biomarkers may be selected from the group consisting of Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PI(40:5) and SM(d36:2), or a fragment, variant or derivative thereof. Even more particularly, the one or more lipid biomarkers may be selected from the group consisting of LPC(14:0), LPC(16:0e), PC(32:2), and SM(d36:2), or a fragment, variant or derivative thereof. Still even more particularly, the one or more lipid biomarkers may be selected from the group consisting of AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. Yet even more particularly, the one or more lipid biomarkers may be selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof. For such examples, an increased level of Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), PI(40:5) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, is suitably diagnostic or indicative of the subject having the breast cancer. As such, in relation to the methods of treatment provided herein, an increased level of Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), PI(40:5) and/or SM(d36:2), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PE(36:2), PS(38:4) and/or SphP(d18:1), or a fragment, variant or derivative thereof, has suitably been measured in the biological sample obtained from the subject. Referring to the above examples, the present methods may include the further step of determining one or more further lipid biomarkers selected from the group consisting of Cer(d36:1), Cer(d38:1), LPC(16:0), LPC(18:0), PC(36:2), PC(36:4), PC(36:5e), PC(38:5), PE(36:5p), PE(38:2p), SM(d40:2), TG(50:1e) and TG(52:3e), or a fragment, variant or derivative thereof. According to certain examples, the present methods may include the further step of determining one or more further lipid biomarkers selected from the group consisting of Cer(d42:1), PC (36:0), PC(36:2) (such as PC(18:0_18:2) and/or PC(18:1_18:1)), PE(34:1) and PI(34:1), or a fragment, variant or derivative thereof. In some examples, the one or more lipid biomarkers may not include one or more of Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:3), PC(36:2), PE(34:2p), PE(O-40:6), PE(40:7e), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d38:4), SM(d41:2), SM(d41:3), SM(d42:4), TG(52:3e), TG(56:1), TG(58:1) and TG(58:2), or a fragment, variant or derivative thereof. In some examples, the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:4), PC(36:5e), PC(38:5), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PG(36:1), PI(36:3), PI(36:4), PI(40:5), PS(36:1), SM(d37:1), SM(d39:2), SM(d40:2), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(53:0), TG(56:0), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. For such examples, the present method may or may not include the further step of determining one or more further lipid biomarkers selected from the group consisting of Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:3), PC(36:2), PE(34:2p), PE(O-40:6), PE(40:7e), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d38:4), SM(d41:2), SM(d41:3), SM(d42:4), TG(52:3e), TG(56:1), TG(58:1) and TG(58:2), or a fragment, variant or derivative thereof. Determining the level of lipid biomarkers It will be understood by the person skilled in the art that the level of expression, abundance or concentration of the one or more lipid biomarkers may be determined by any means known in the art. The terms “determining”, “measuring”, “evaluating”, “assessing”, “quantifying”, “calculating” and “assaying” are used interchangeably herein and may include any form of measurement known in the art, such as those described hereinafter. Such determining may include detecting the presence or absence of one or more of the lipid biomarkers and/or determining a concentration level thereof in the biological sample obtained from the subject. Suitable means for determining the level of concentration or expression of the one or more lipid biomarkers include, but are not limited to, nuclear magnetic resonance (NMR) spectrometry, surface plasmon resonance (SPR), chromatographic techniques, mass spectrometry, biosensors and any combination of these techniques. In certain examples, the level of concentration or expression of the one or more lipid biomarkers is measured by mass spectrometry. Mass spectrometry (MS) is an analytical technique that measures the mass-to-charge (m/z) ratio of charged particles. It is primarily used for determining the elemental composition of a sample or molecules, and for elucidating the chemical structures of molecules, such as peptides, lipids and other chemical compounds. MS works by ionizing chemical compounds to generate charged molecules or molecule fragments and measuring their mass-to-charge ratios. MS instruments typically consist of three modules: (1) an ion source, which can convert gas phase sample molecules into ions (or, in the case of electrospray ionization, move ions that exist in solution into the gas phase); (2) a mass analyser, which sorts the ions by their masses by applying electromagnetic fields; and (3) a detector, which measures the value of an indicator quantity and thus provides data for calculating the abundances of each ion present. Suitable mass spectrometry methods to be used with the present disclosure include but are not limited to, one or more of electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), tandem liquid chromatography-mass spectrometry (LC-MS/MS) mass spectrometry, desorption/ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(MS), atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS/MS, and APPI-(MS)n, quadrupole mass spectrometry (inclusive of triple quadrupole (QQQ) mass spectrometry), Fourier transform mass spectrometry (FTMS), and ion trap mass spectrometry, where n is an integer greater than zero. In particular examples, the concentration or expression level of the one or more lipid biomarkers is determined at least in part by using liquid chromatography - mass spectrometry (LC-MS). In other examples, the concentration or expression level of the one or more lipid biomarkers is determined at least in part by using high-resolution accurate-mass MS (HRMS). As noted above, MS ionizes lipids and sorts ions based on their mass-to-charge ratio. It has been widely used to characterize lipids, especially with the development of soft ionization techniques such as electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI). Lipid extraction is usually the first step for lipid analysis, and separates the lipidic components (organic phase) from other components such as proteins and nucleic acids (aqueous phase). Some examples, however, utilise monophasic lipid extraction. Extraction methods typically include the application of a mixture of methanol, chloroform and water for phase separation. However, shotgun lipidomic methods have also been developed which omit the chromatographic separation and sample processing described above, and analyzes all lipid classes together, and instead using ionization additives to assist in distinguishing between particular lipids. The present MS method may involve lipid digestion, fragmentation or denaturation followed by LC-MS or LC-MS/MS (tandem MS) to derive mass-to-charge ratios for specific lipid headgroups and/or acyl chains that make up the lipid biomarkers described herein. Suitably, the one or more lipid biomarkers or one or more fragments thereof are subsequently subjected to quantitative mass spectrometry including without limitation, selected reaction monitoring mass spectrometry (SRM), high resolution data independent analyses (SWATH), multiple reaction monitoring (MRM) and/or MSI based quantitation. In certain examples, an MRM assay is used which employs specific lipids and their fragments (transitions) as discriminators of individual lipid biomarkers. In particular examples, the present MS method is performed in positive and/or negative ion modes. In general, lipids can form small cation adducts when in the positive-ion mode, due to the ionization process. The formation of cation adducts of lipid molecular species resulted from the affinity of the cations with the dipole that is present in the lipid species depends on the availability of the small cations. By way of example, such adducts can include H+, NH4+, Li+, Na+, K+, and (- H20+H)+. In the negative-ion mode, lipid species in the deprotonated form or with an anionic adduct are displayed depending on whether the lipid molecule species carry a net ionic bond. For example, PE, PI, PS, PA, and PG, are all of acidic lipid classes (i.e., an ionic bond is present), and thus, may be detected as deprotonated ions. Some lipids are of a polar lipid class without an ionizable bond or PC and SM are strong zwitterionic lipid classes, all of which can form as anionic adducts with small anion(s) (e.g., Cl−, CH3COO−, and HCOO−) depending on the concentrations present and their affinities with these lipid species. In certain examples, the one or more lipid biomarkers described herein include an adduct as set out in Tables 4 and 5. In some examples, the one or more lipid biomarkers described herein include an ion mode as set out in Tables 4 and 5. Suitably, the ion mode is specific to that method of MS described in the respective Example. In other examples, the one or more lipid biomarkers described herein have a retention time or elution time of that or about that as set out in Tables 4 and 5. Suitably, the retention time is determined as per that method of MS described in the respective Example. In particular examples, the one or more lipid biomarkers described herein have an accurate mass, neutral mass or mass-to-charge ratio (m/z) of that or about that as set out in the isomer table above, in Tables 4 and 5 or in a database of lipidmaps.org. Suitably, the neutral mass is determined as per that method of MS described in the respective Example. In some applications, various ionization techniques can be coupled to the mass spectrometers provided herein to generate the desired information. Non-limiting exemplary ionization techniques that can be used with the present disclosure include but are not limited to Matrix Assisted Laser Desorption Ionization (MALDI), Desorption Electrospray Ionization (DESI), Direct Assisted Real Time (DART), Surface Assisted Laser Desorption Ionization (SALDI), or Electrospray Ionization (ESI). In some applications, HPLC and UHPLC can be coupled to a mass spectrometer so that a number of other lipid separation techniques can be performed prior to mass spectrometric analysis. Some exemplary separation techniques which can be used for separation of the desired analyte (e.g., lipid) from the matrix background include but are not limited to Reverse Phase Liquid Chromatography (RP-LC) of lipids, offline Liquid Chromatography (LC) prior to MALDI, 1- dimensional gel separation, 2-dimensional gel separation, Strong Cation Exchange (SCX) chromatography, Strong Anion Exchange (SAX) chromatography, Weak Cation Exchange (WCX), and Weak Anion Exchange (WAX). One or more of the above techniques can be used prior to mass spectrometric analysis. In some examples, the expression or concentration of a lipid biomarker will be higher or increased in a subject compared to a reference value determined from controls. However, for certain lipid biomarkers, expression or concentration of that biomarker is lower or decreased relative to a reference value from controls. Suitably, an increased level of expression or concentration of a first subset of the one or more lipid biomarkers indicates or correlates with the subject having a breast cancer; and/or a decreased level of expression or concentration of a second subset the one or more lipid biomarkers (e.g., not present in the first subset of the one or more lipid biomarkers) indicates or correlates with the subject having a breast cancer. In other examples, a decreased level of concentration or expression of a first subset of the one or more lipid biomarkers indicates or correlates with the subject not having a breast cancer; and/or an increased level of expression or concentration of a second subset of the one or more lipid biomarkers (e.g., not present in the first subset of the one or more lipid biomarkers) indicates or correlates with the subject not having a breast cancer. As will be understood by the skilled person, the level or expression level of any one of the lipid biomarkers described herein may be relatively (i) higher, increased or greater; or (ii) lower, decreased or reduced when compared to an expression level in a control or reference sample, or to a threshold expression level. In various examples, an expression level may be classified as higher, increased or greater if it exceeds a mean and/or median expression level of a reference population. In some examples, an expression level may be classified as lower, decreased or reduced if it is less than the mean and/or median expression level of the reference population. In this regard, a reference population may be a group of subjects who have breast cancer. Alternatively, a reference population may be a group of subjects who are known to be free of cancer, and more particularly free of breast cancer. Terms such as “higher”, “increased” and “greater” as used herein refer to an elevated amount or level of a lipid biomarker, such as in a biological sample, when compared to a control or reference level or amount. The concentration or expression level of the lipid biomarker may be relative or absolute (i.e., relatively or absolutely higher, increased or greater). In some examples, the level of a lipid biomarker is higher, increased or greater if its level of concentration or expression is more than about 0.5%, 1%, 2%, 3%, 4%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, 150%, 200%, 300%, 400% or at least about 500% above the level of concentration or expression of the lipid biomarker in a control or reference level or amount. The terms, “lower”, “reduced” and “decreased”, as used herein refer to a lower amount or level of a lipid biomarker, such as in a biological sample, when compared to a control or reference level or amount. The concentration or expression level of the lipid biomarker may be relative or absolute (i.e., relatively or absolutely lower, reduced or decreased). In some examples, the concentration or expression of a lipid biomarker is lower, reduced or decreased if its level of concentration or expression is less than about 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20% or 10%, or even less than about 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.01%, 0.001% or 0.0001% of the level or amount of concentration or expression of the lipid biomarker in a control or reference level or amount. The term “control sample” typically refers to a biological sample from a (healthy) non- diseased individual or population of individuals not having cancer or, more particularly, not having breast cancer. In some examples, the control sample may be from a subject known to be free of cancer, or more particularly, free of breast cancer. Alternatively, the control sample may be from a subject in remission from cancer. The control sample may be a pooled, average or an individual sample. An internal control is a marker from the same biological sample being tested. The term “control sample” may also or alternatively be used to refer to a biological sample from a diseased individual or population of individuals having cancer or, more particularly, having breast cancer. In some examples, a reference level or amount is determined from measurements of the biomarkers in a corresponding panel of biomarkers from a population of healthy individuals. The term “healthy individual” as used herein refers to a person or populations of persons who are known not to have breast cancer. In some examples, the control reference is determined from measurements of the corresponding biomarkers in a “typical population”. Preferably, a "typical population" will exhibit a spectrum of breast cancer at different stages of disease progression. It is particularly preferred that a “typical population” exhibits the expression characteristics of a cohort of subjects as described herein. In another example, a reference level or amount may be derived from an established data set including one or more of: 1. a data set comprising measurements of the lipid biomarkers for a population of subjects known to have breast cancer; 2. a data set comprising measurements of the lipid biomarkers for the subject being tested wherein said measurements have been made previously, such as, for example, when the subject was known to be healthy; and/or 3. a data set comprising measurements of the lipid biomarkers for a healthy individual or a population of healthy individuals. In certain examples, a data set comprising measurements of the lipid biomarkers may be obtained from a population of subjects known to have breast cancer, a healthy individual or a population of healthy individuals. Such subjects may be in a fasted state, a non-fasted state or a combination thereof. As used herein, a concentration or expression level may be an absolute or relative amount of an expressed lipid. Accordingly, in some examples, the concentration or expression level of any one of the one or more lipid biomarkers is compared to a control level of concentration or expression, such as the level of lipid concentration or expression of one or a plurality of “housekeeping” lipids or molecules in the biological sample of the subject. In further examples, the concentration or expression level of any one of the one or more lipid biomarkers is compared to a threshold level of concentration or expression, such as a level of lipid concentration or expression in a biological sample from a control subject not having breast cancer and/or an average or median level of lipid concentration or expression in biological samples derived from a population of breast cancer patients. A threshold level of concentration or expression is generally a quantified level of concentration or expression of a lipid biomarker. Typically, a concentration level or an expression level of a lipid biomarker in a sample that exceeds or falls below the threshold level of concentration or expression is predictive of a particular disease state or outcome, such as the presence or absence of breast cancer. The nature and numerical value (if any) of the threshold level of concentration or expression will typically vary based on the method chosen to determine the concentration or expression of the one or more lipid biomarkers used in determining, for example, a breast cancer diagnosis in the subject. A person of skill in the art would be capable of determining the threshold level of any one of the one or more lipid biomarkers in a sample that may be used in determining, for example, the presence or absence of breast cancer in the relevant subject, using any method of measuring lipid concentration, abundance or expression known in the art, such as those described herein. In various examples, the threshold level is a mean and/or median concentration or expression level (median or absolute) of the lipid biomarker in a reference population that, for example, have or do not have breast cancer. Additionally, the concept of a threshold level of concentration or expression should not be limited to a single value or result. In this regard, a threshold level of concentration or expression may encompass multiple threshold concentration or expression levels or a suitable range thereof that could signify, for example, a high, medium, or low probability of, for example, the subject having breast cancer. In view of the foregoing, any of the methods disclosed herein may comprise a step of establishing a reference level or threshold level of concentration or expression of the one or more lipid biomarkers. Suitably, the predictive accuracy of the methods described herein, as determined by an ROC AUC value, is at least about 0.65 (e.g., at least about 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99 or any range therein). More particularly, the predictive accuracy of the methods described is suitably at least about 0.70. Even more particularly, the predictive accuracy of the methods described is suitably at least about 0.75. Yet even more particularly, the predictive accuracy of the methods described is suitably at least about 0.80. Still even more particularly, the predictive accuracy of the methods described is suitably at least about 0.85. Yet still even more particularly, the predictive accuracy of the methods described is suitably at least about 0.90. Suitably, the sensitivity of the methods described herein in terms of detecting or diagnosing breast cancer in a subject is at least about 0.65 (e.g., at least about 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99 or any range therein). The term “sensitivity”, as used herein, relates to the percentage of subjects having breast cancer who are correctly identified as having breast cancer. More particularly, the sensitivity of the methods described is suitably at least about 0.70. Even more particularly, the sensitivity of the methods described is suitably at least about 0.75. Yet even more particularly, the sensitivity of the methods described is suitably at least about 0.80. Still even more particularly, the sensitivity of the methods described is suitably at least about 0.85. Yet still even more particularly, the sensitivity of the methods described is suitably at least about 0.90. Suitably, the specificity of the methods described herein in terms of detecting or diagnosing breast cancer in a subject is at least about 0.65 (e.g., at least about 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99 or any range therein). The term “specificity”, as used herein, relates to the percentage of healthy subjects who are correctly identified as not having breast cancer. More particularly, the specificity of the methods described is suitably at least about 0.70. Even more particularly, the specificity of the methods described is suitably at least about 0.75. Yet even more particularly, the specificity of the methods described is suitably at least about 0.80. Still even more particularly, the specificity of the methods described is suitably at least about 0.85. Yet still even more particularly, the specificity of the methods described is suitably at least about 0.90. Additional cancer biomarkers It is envisaged that one or more further biomarkers of cancer, and more particularly breast cancer (i.e., breast cancer-associated biomarkers), may be used in conjunction or combination with the one or more lipid biomarkers in the diagnostic and treatment methods described herein. Such further biomarkers may be any as are known in the art, and may comprise macromolecules, such as nucleic acid (DNA/RNA), proteins, and intact cells. Suitably, a level of such further biomarkers can be detected or determined in the same biological sample in which the level of the one or more lipid biomarkers is or has been assessed. Exemplary further biomarkers include cancer antigen 15-3 (CA 15-3), cancer antigen 27-29 (CA 27-29), cancer antigen 19-9 (CA 19-9), cancer antigen 125 (CA 125), trefoil factor (TFF) 1, TFF2, TFF3, carcinoembryonic antigen (CEA), Alpha-fetoprotein (AFP), circulating tumour DNA (ctDNA), circulating tumour cells (CTC), serum epithelial membrane antigen/CK1 concentration ratio, pleiotrophin (PTN), miR- 127-3p with human epididymis secretory protein 4 (HE4), human anterior gradient (AGR) 2 with AGR3, vascular endothelial growth factor (VEGF) with CA 15–3, serum apolipoprotein C-I (apoC-I), autoantibodies (e.g., autoantibodies that target oncogenic and/or tumour suppressor proteins), miR-221, miR-21, miR-145, circular RNAs (circRNAs; e.g., hsa circ 103110, hsa circ 104689, hsa circ 104821, hsa circ 006054, hsa circ 100219, and hsa circ 406697) and exosomal surface proteins (e.g., developmental endothelial locus-1 protein (Del-1) and fibronectin). In particular examples, the methods described herein include the step of measuring a level of one or more further biomarkers in a biological sample from the subject, wherein the one or more further biomarkers are selected from the group consisting of CA 15-3, CA 125, CA 19-9, CEA and AFP. More particularly, the methods described herein can include the step of measuring a level of one or more, two or more, three or more, four or more of five further biomarkers in a biological sample from the subject, wherein the one or more further biomarkers are selected from the group consisting of CA 15-3, CA 125, CA 19-9, CEA and AFP. It is envisaged that the biological sample for assessing the levels of further biomarkers may be the same of different to that utilised in measuring the levels of the one or more lipid biomarkers. Calculating risk or diagnostic scores For the methods described herein, determining the presence or absence of a breast cancer in a subject may include the step of calculating a risk score or a diagnostic score. The term “risk score” or “disease risk score” refers to value calculated with one or more feature values or scores that indicates an undesirable physiological state of the patient, such as the presence of cancer. The term “risk score” in certain instances refers to a numerical representation of the current degree of the risk or probability a patient is at for having a particular disease or condition. A risk score may be calculated using the concentration or expression levels or expression signature of the one or more lipid biomarkers, such as in a panel (e.g., 2, 3, 4, 5 etc or more) of the diagnostic lipid biomarkers, inclusive of those hereinbefore described. To this end, the methods described herein include the step of obtaining a risk score for a lipid biomarker combination hereinbefore described or set forth in the Example (e.g., Tables 4 and 5). A concentration or expression signature of a lipid may be determined using the normalized level of concentration or expression of the lipid in a sample, and an independent diagnostic value of the lipid based on the correlation of the concentration or expression of the lipid with disease presence or absence. Any method of determining a concentration or expression signature for a lipid known in the art may be utilised. After determining the concentration or expression levels or expression signatures of individual lipids, such as in a panel of two or more of the lipids described herein, a risk score may be calculated by combining the concentration or expression levels and/or the expression signatures of each lipid in a panel thereof. Methods of calculating a risk score may be by any method or means known in the art. In particular examples, the risk score is calculated at least in part by logistic regression. By way of example, a linear combination of the concentration or expression levels of the one or more lipid biomarkers with various coefficients determined through prior training may be generated and subsequently used to estimate the log odds of cancer. The log odds can then be converted into a probability of a subject having breast cancer via logistic regression. In other examples, the risk score is calculated at least in part by partial least squares discriminant analysis. Accordingly, a risk score for a patient may be calculated according to the below formula:
Figure imgf000089_0001
(1 + ^^ ^^+ ∑ ^^ ^^∗ ^^ ^^ ) Wherein the intercept I and coefficients ci are the specific logistic regression model parameters calculated in advance based on the training data, and the values Li represent the normalised lipid abundances measured for the respective patient sample for each lipid in the panel. A calculated risk score of the disclosure may be used to determine the likelihood of the presence or absence of a breast cancer in a subject. In general, a calculated risk score may be compared to a reference risk score. In certain examples, if (i) the risk score is equal to or higher than the reference risk score, the subject has a breast cancer, and (ii) the risk score is lower than the reference risk score, the subject does not have a breast cancer. It is envisaged that a subject’s diagnosis and/or risk score can be utilised to determine whether said subject should be treated with an anti-cancer agent. Accordingly, in other examples, if (i) the risk score is equal to or higher than the reference risk score, the subject is to be administered an anti-cancer treatment, and (ii) the risk score is lower than the reference risk score, the subject is not to be administered an anti-cancer treatment. In some examples, the risk score is compared to a threshold risk score, such as a median or average risk score, to determine the diagnosis of breast cancer in a subject. If the risk score is equal to or higher than the threshold risk score, the subject suitably has breast cancer. Alternatively, if the risk score is lower than the threshold risk score, the subject suitably does not have breast cancer. A threshold risk score may be the respective median or average of the risk scores calculated for each subject in a population of subjects with breast cancer and/or the respective median or average of the risk scores calculated for each subject in a population of subjects without breast cancer. Subject selection The present disclosure further contemplates that the methods described herein may be performed in respect of a particular subject or patient population or group. To this end, the subject may have previously been subjected to a selection step based on one or more selection criteria, such as those described herein, prior to the present methods being performed. Moreover, the present methods may include the earlier step of selecting a subject based on one or more selection criteria described herein. Suitably, the one or more selection criteria may include age. In particular examples, the subject is at least about 20 years old (e.g., at least about 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70 years old or any range therein), at least about 25 years old, at least about 30 years old, at least about 35 years old, at least about 40 years old, at least about 45 years old, at least about 50 years old, at least about 55 years old, at least about 60 years old, at least about 65 years old, or at least about 70 years old. In other examples, the subject is between about 20 to about 60 years old. In some examples, the subject is between about 30 to about 40 years old. In some examples, the subject is between about 35 to about 40 years old. In some examples, the subject is between about 40 to about 50 years old. In some examples, the subject is between about 20 to about 30 years old. In some examples, the subject is between about 25 to about 30 years old. In some examples, the subject is between about 30 to about 50 years old. Suitably, the one or more selection criteria includes previous or ongoing treatment with a lipid lowering therapy, such as a statin (e.g., a HMG-CoA reductase inhibitor), a cholesterol absorption inhibitor, a bile acid sequestrant, a PCSK9 inhibitor, an adenosine triphosphate-citrate lyase inhibitor and/or a fibrate. In some examples, the subject described herein has not and/or is not currently being administered a lipid lowering therapy. Suitably, the one or more selection criteria includes one or more preexisting conditions or comorbidities, such as diabetes, a renal disease, disorder or condition and a cardiovascular disease, disorder or condition (e.g., atherosclerosis, peripheral artery disease, heart failure, coronary artery disease). In some examples, the subject described herein does not have diabetes, inclusive of Type I and Type II diabetes. In other examples, the subject described herein does not have a renal disease, disorder or condition. For some examples, the subject described herein does not have a cardiovascular disease, disorder or condition. Suitable screening tests to determine the presence or absence of such diseases, disorders or conditions are well known to the skilled person. Suitably, the one or more selection criteria includes breast density. Breast density may be assessed by any means known in the art and may be indicated by a breast density score. To help provide radiologists with a uniform scoring system, the American College of Radiology developed an index which ranks breast density from 1 to 4 or A to D ranging from fatty to dense (i.e., Score 1/Type A: Fatty tissue; Score 2/Type B: Scattered fibroglandular; Score 3/Type C: Heterogeneously dense; Score 4/Type D: Dense tissue). In some examples, the subject described herein has a breast density score of 3 or less (i.e., has a breast density score ranging from 1 to 3 or Type A, B or C). In other examples, the subject described herein has a breast density score of 2 or less (i.e., has a breast density score ranging from 1 to 2 or Type A or B). Kits The present disclosure also contemplates kits for the detection of lipid biomarkers that may be suitable for use in the methods described herein. In one broad form, the present disclosure provides a kit for determining the presence or absence of a breast cancer in a subject, the kit comprising one or more reagents for determining a level of one or more lipid biomarkers in a biological sample obtained from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2) (such as TG(16:0_10:0_18:2) and/or TG(16:0_10:1_18:1)), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof. Any agent or probe capable of binding specifically to a lipid biomarker of interest will be useful, such as a probe, an aptamer, an antibody and/or an antibody fragment. Other components of the kits will typically include labels, secondary antibodies, inhibitors, co-factors and control lipid product preparations to allow the user to quantitate concentration or expression levels and/or to assess whether the measurement has worked correctly. Biosensors, including optical (e.g., SPR- based sensors, interferometry-based sensors, waveguide-based sensors), electrochemical and mechanical biosensors are particularly suitable assays that can be carried out easily by the skilled person using kit components. In some examples, the kit may comprise a substrate, such as a microtitre plate, on which is immobilised capture probes or antibodies corresponding to the lipid biomarkers being measured. In some examples, the kit comprises beads on which is immobilised capture probes or antibodies corresponding to the lipid biomarkers being measured. Optionally, the kit further comprises means for the detection of the binding of a probe, such as an antibody, to a lipid biomarker. Such means include a reporter molecule such as, for example, an enzyme (such as horseradish peroxidase or alkaline phosphatase), a dye, a radionucleotide, a luminescent group, a chemiluminescent group, a fluorescent group, biotin or a colloidal particle, such as colloidal gold or selenium. Suitably, such a reporter molecule is directly linked to the antibody. In one example, a kit may additionally comprise or the one or more reagents thereof may comprise a reference sample or a reference standard, such as for one or more of the lipid biomarkers described herein. Suitably, a reference sample comprises a lipid that is detected by an antibody and/or may be labelled or modified so as to be distinguished from native lipid. Suitably, the lipid in the reference is of known concentration. Such a lipid is of particular use as a standard or reference standard of the one or more lipid biomarkers described herein. Accordingly, various known concentrations of such a lipid may be detected using a diagnostic assay described herein. Suitably, such reference samples or reference standards are for use by mass spectrometry-based methods of measuring a level of a lipid biomarker, such as those described herein. Instructions supplied in the kits of the present disclosure are typically written instructions on a label or package insert (e.g., a paper sheet included in the kit), but machine-readable instructions (e.g., instructions carried on a magnetic or optical storage disk) are also acceptable. The instructions relating to the use of the reagents described herein, generally include information as to determining a concentration or expression level of the one or more lipid biomarkers and guidance regarding dosage, dosing schedule, and route of administration for an indicated treatment. The kit may further comprise a description of selecting an individual having breast cancer and thereby suitable for treatment. In particular examples, the reference data is on a computer-readable medium (e.g., software embodying or utilized by any one or more of the methodologies or functions described herein). The computer-readable medium can be included on a storage device, such as a computer memory (e.g., hard disk drives or solid state drives) and may comprise computer readable code components that when selectively executed by a processor implements one or more aspects of the present disclosure. Systems The present disclosure also contemplates systems for the detection of lipid biomarkers that may be suitable for use in the methods described herein. In one broad form, the present disclosure provides a system for determining the presence or absence of a breast cancer in a subject, the system comprising: a mass spectrometry unit configured for determining a level of one or more lipid biomarkers in a biological sample obtained from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2) (such as TG(16:0_10:0_18:2) and/or TG(16:0_10:1_18:1)), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof; and a processing unit configured for using or analysing the level of the one or more lipid biomarkers to determine the presence or absence of the breast cancer in the subject. It is noted that the step of determining a concentration level or an expression level may be performed by the mass spectrometry units and/or may be performed at least in part by a pre- processing unit. That pre-processing unit may be the same as or different to the processing unit performing the steps of analysing the concentration or expression level. For example, the pre- processing unit may receive data from the mass spectrometry unit indicative of a number of fragments (e.g., lipid headgroup and/or acyl chain) of the lipid biomarkers for respective mass values. This data may also be representative of the retention time of particular fragments. The pre-processing unit may then process this data to determine lipid biomarkers as combinations of fragments to thereby calculate the corresponding concentration or expression levels. Suitably, the mass spectrometry unit and the processing unit are that described herein. Computer-implemented methods It is envisaged that one or more steps of the methods described herein may be automated or implemented by a computer in the sense that the disclosed methods are implemented as software code that is stored on a non-volatile data storage medium. The computer executes the software code, which causes the computer to perform the methods disclosed herein. By way of example, comparing a concentration level or an expression level of the one or more lipid biomarker with, for example, a reference or threshold level or value may be carried by a computer executing software code describing the comparing step. Thus, the comparison may be carried out by a computer or computing device, such as by a processing unit. The value of the determined or detected amount of the one or more lipid biomarkers in the sample from the subject and the reference amount can be, for example, compared to each other and said comparison can be automatically carried out by a computer program executing an algorithm for the comparison. Additionally, the calculation of a risk or diagnostic score and/or its comparison to a reference risk or diagnostic score can be automatically carried out by a computer program executing an algorithm for the comparison. Suitably, such algorithms may be trained on one or more case and/or control samples. In some examples, a processor may utilize the concentration or expression level data and/or a risk or diagnostic score to calculate a likelihood of the subject in question having a breast cancer. The computer program carrying out the evaluation will suitably provide the desired assessment in a suitable output format. For a computer-assisted comparison, the value of the determined amount may be compared to values corresponding to suitable references, which are stored in a database by a computer program. The computer program may further evaluate the result of the comparison, i.e. automatically provide the desired assessment in a suitable output format. In some examples, the methods of the disclosure include one or more of the broad steps of: (i) optionally performing a measurement of the concentration or expression level of the one or more lipid biomarkers described herein; (ii) inputting or receiving the values from (i) into a processing system that is configured to determine the presence or absence of a breast cancer in a subject; (iii) optionally calculating a risk or diagnostic score from the level or expression level of the one or more lipid biomarkers by the processing system; (iv) comparing the concentration or expression level and/or the risk or diagnostic score obtained in step (iii) with a threshold value by the processing system; (v) determining the presence or absence of the breast cancer in the subject; and (vi) optionally providing a treatment for the breast cancer if present in the subject. The methods of the present disclosure suitably permit integration into existing or newly developed pathology architecture or platform systems. For example, the present disclosure contemplates a method of allowing a user to determine the status (e.g., the presence or absence of a breast cancer) of a subject, the method including the steps of: (a) receiving data in the form of concentration or expression levels of one or more lipid biomarkers for a test sample, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2) (such as TG(16:0_10:0_18:2) and/or TG(16:0_10:1_18:1)), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof; (b) optionally processing the subject data, such as with a processing unit or system, via univariate and/or multivariate analysis and/or machine learning algorithms (e.g., LASSO-penalised multivariate Cox regression, logistic regression, partial least squares discriminant analysis, random forest, decision tree, gradient boosting) to provide a risk or diagnostic score; (c) determining a status of the subject in accordance with the results of the concentration or expression levels and/or the risk or diagnostic score in comparison with predetermined or reference concentration or expression levels and/or risk or diagnostic score values, such as with a processing unit or system; and (d) transferring or providing an indication of the status (e.g., a diagnosis of breast cancer or no breast cancer) of the subject to the user. In some examples, the above method further includes the step of producing or generating the concentration level or expression level data by determining a concentration level or an expression level of one or more lipid biomarkers in the biological sample obtained from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2) (such as PC(18:1_18:1) and/or PC(18:0_18:2)), PC(36:4) (e.g., PC(18:2/18:2) and/or PC(16:0/20:4)), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2) (such as TG(16:0_10:0_18:2) and/or TG(16:0_10:1_18:1)), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof, such as by using one or more of those methodologies described herein. In one example, the method additionally includes: (a) having a user determine the data using a remote end station; and (b) transferring the data from the end station to a base station via a communications network. The base station can include first and second processing systems, in which case the method can include: (a) transferring the data to the first processing system; (b) causing the first processing system to perform a univariate or multivariate analysis function to generate the risk or diagnostic score. The method may also include: (a) transferring the results of the univariate or multivariate analysis function and/or the determined concentration or expression levels of the lipid biomarkers to the second processing system; and (b) causing the second processing system to determine the status of the subject. The second processing system may be coupled to a database adapted to store predetermined data and/or the univariate or multivariate analysis function, such that the computer-implemented method may include: (a) querying the database to obtain at least selected predetermined data or access to the univariate or multivariate analysis function from the database; and (b) comparing the selected predetermined data to the subject data or generating a predicted probability index. The second processing system can be coupled to a database, the method including storing the data in the database, such as by way of a memory unit. The reference concentration or expression level data comprises a level or a level of concentration or expression determined for the one or more lipid biomarkers within a biological sample selected from the group consisting of: (i) a biological sample from a normal or healthy subject, such as normal or healthy subject without breast cancer; (ii) a biological sample from a subject previously diagnosed or determined as having a breast cancer; (iii) an extract of any one of (i) to (ii); (iv) a data set comprising levels of concentration or expression for the lipid biomarkers within a normal or healthy individual or a population of normal or healthy individuals; (vi) a data set comprising levels of concentration or expression for the lipid biomarkers in an individual or a population of individuals having breast cancer; and (vii) a data set comprising levels of concentration or expression for the lipid biomarkers in the subject being tested wherein the levels of concentration or expression are determined for a sample having been taken at an earlier time point when the subject was known to not have breast cancer. Obtaining a sample from a subject The methods disclosed herein may further include the initial or earlier step of providing or collecting a biological sample from the subject that suitably contains lipid micro-vesicles or extracellular vesicles, such as a liquid biopsy. Such a sample may be obtained by freshly collecting a sample, or may be obtained from a previously collected and stored sample. By way of example, a sample may be obtained from a previously collected and stored (e.g., refrigerated or frozen) blood, plasma or serum. Suitably, a sample is obtained by freshly collecting a sample from the subject. Alternatively, a sample can be obtained from a previously collected and stored sample from the subject. In particular examples, the subject has fasted prior to collection or is in a fasted state at the time of collection of the biological sample for further testing by the methods provided herein. As used herein, the term “fasted” refers to the condition of not having consumed food or beverage during the period between from at least about 3 hours to about 12 hours (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 hours and any range therein) prior to providing a biological sample for testing. In alternative examples, the subject has not fasted prior to collection or is in a non-fasted (or “fed”) state at the time of collection of the biological sample for further testing by the methods provided herein. The term “non-fasted” as generally used herein refers to the condition of having consumed food and/or beverage within at least about 3 hours to about 12 hours (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 hours and any range therein) of providing a biological sample for testing. Suitable samples comprise a concentration of lipid micro-vesicles or extracellular vesicles. A suitable sample may also comprise circulating lipid micro-vesicles or extracellular vesicles. Circulating lipid micro-vesicles or extracellular vesicles may be found in a bodily fluid (e.g., blood, plasma, serum, urine, vomit, tears, sputum etc.) or other excrement (e.g., faeces). In certain examples, the biological sample is or comprises blood, plasma or serum. In some examples, the biological sample (e.g., blood, plasma and/or serum) has been enriched for extracellular vesicles and/or a lipid content thereof. In various examples, the biological sample is or comprises plasma. In other examples, the biological sample is or comprises serum. As used herein, the term “extracellular vesicle” or “EV” refers to a cell-derived vesicle comprising a membrane that encapsulates an interior space. Extracellular vesicles include all membrane-bound vesicles (e.g., exosomes, nanovesicles) whose diameter is typically smaller than the diameter of the cell from which they are derived. The biological sample may be subject to any suitable pre-treatment steps before measurement of the level of the one or more lipid biomarkers is performed, in order to improve the accuracy and/or efficiency of the measurement. Such pre-treatment steps may include extraction, centrifugation (e.g., ultracentrifugation), lyophilization, fractionation, separation (e.g., using column or gel chromatography), concentration or evaporation. In some instances, this treatment can include one or more extractions with solutions comprising any suitable solvent or combinations of solvents, such as, but not limited to acetonitrile, water, chloroform, methanol, butylated hydroxytoluene, trichloroacetic acid, toluene, hexane, benzene, or combinations thereof. In some examples, the biological sample may undergo one or more treatment steps so as to isolate, concentrate or enrich for extracellular vesicles and/or a lipid content thereof. Suitably, the biological sample is an extracellular vesicle (EV) sample or is a biological sample, such as a plasma sample, serum sample or blood sample, that has been enriched for extracellular vesicles. Thus, any of the methods disclosed herein may comprise a step of taking a biological sample from a subject and determining the level of expression, concentration or abundance of the one or more lipid biomarkers in the sample. Alternatively, any of the methods disclosed herein may not comprise a step of taking a biological sample from a subject and/or determining the level of expression, concentration or abundance of the one or more lipid biomarkers in the sample. Instead, the biological sample may have already been taken from the subject and/or the level of expression, concentration or abundance of the one or more lipid biomarkers in the sample may have been determined previously. So that preferred embodiments of the present disclosure may be fully understood and put into practical effect, reference is made to the following non-limiting examples. Examples Example 1. Plasma- and EV-derived lipidomics biomarker discovery The aim of the present Example was to identify a multivariate plasma-derived lipid biomarker signature that has high predictive power in stratifying controls and patients with breast cancer while being highly robust with respect to sample heterogeneities. The process was replicated using EV lipidome on the same set of subjects to obtain an EV-derived lipid signature and assess the predictive performance of plasma-based signature compared to the EV signature. For each dataset (plasma and EV), a signature of 20 or 30 lipids was identified using a robust, statistically rigorous feature selection algorithm based on the concept of random forest feature importance. These lipids were used to train an ensemble of 18 artificial intelligence (AI) or machine learning (ML) which are used to jointly predict disease status. The final plasma ensemble was capable of consistently and reliably detecting individuals with cancer (accuracy: 86.1%, sensitivity: 91.3%, specificity: 78.6%). The EV model obtained very similar performance (accuracy: 86.1%, sensitivity: 90.4%, specificity: 80.2%). Methods Figure 1 illustrates the pipeline developed for signature panel identification and predictive model development, which involves 2000 iterations of leave-group-out cross-validation, LGOCV (80% train, 20% test) to provide a high level of confidence in the generalisability of the results. Within each iteration, feature selection was performed, 18 classification models were trained, and performance was assessed on the held-out test data. Preprocessing During the data exploration phase, lipid concentrations were determined to exhibit over- dispersion. A log-transformation was used for its variance-stabilising properties. Prior to the log- transformation, a small, randomly selected [1e-6, 1e-5] offset was added to 0 values (i.e. undetected lipids). Signature panel identification Within each iteration of LGOCV, a subset of lipids was selected for use in the predictive modelling module using the 80% training set. These lipids were selected by Boruta, a robust, statistically rigorous feature selection algorithm based on the concept of random forest feature importance [2]. A p-value cut-off of 0.01 (Bonferroni Adjusted) was used to identify consistently important features over 100 iterations with 500 trees per random forest. Predictive modelling A diverse range of 24 predictive classification models provided by the ‘caret’ package was identified for use in training an ensemble model [1]. Within each iteration of LGOCV, the predictive models were provided with the features selected by Boruta for the 80% training set. Hyperparameter selection was performed for each model using a random search with a tuning length of 10 over 50 iterations of a nested LGOCV (splitting the training set further into 80% sub- train and 20% sub-test). Upon selecting the ideal set of hyperparameters, the model was refit using the entire training set. Model validation The optimised models were subsequently validated on the held-out 20% test set. Individual model predictions were obtained for each test sample. In the case of the ensemble model, predictions were obtained according to a majority vote across each model, with ties being predicted as cancer. Final ensemble model Informed by analysis of model validation results, a biomarker panel was specified, and the ideal hyperparameters for each algorithm were determined. The LGOCV procedure was repeated, this time keeping the feature set and algorithm hyperparameters constant. Models were repeatedly trained on a random 80% data split and validated on the remaining 20% of samples. External Validation During each training iteration in the LGOCV procedure of the Final ensemble model, the models were also used to predict each of the controls from the provided datasets in order to validate the TNR of the final model. A “Grand” model was subsequently trained using all the samples of the original data (not repeated random 80% data splits), and predictions were obtained for the datasets. Results A. Plasma analyses Performance of classifiers and patient-level predictions The pipeline described in Figure 1 was implemented and executed, resulting in 2000 lipid signatures and 2000 predictive modelling suits (comprising 18 classifiers and an ensemble approach based on the majority vote). Within each iteration, the hyperparameters of each classifier were optimised using a random search procedure over 50 iterations of nested leave-group-out cross-validation. Figure 2a demonstrates the average prediction of each model for individual patients across 2000 runs. The Ensemble model (majority vote) was the best performing method (acc=84.8±4.6%, tpr=89.6±5.7%, tnr=78.2±9.0%), closely followed by Distance Weighted Discrimination with a Radial basis function [3] (acc=84.6±4.8%, tpr=88.8±5.8%, tnr=78.7±9.1%) and Neural Networks Using Model Averaging [4] (acc=84.4±4.8%, tpr=87.4±6.3%, tnr=80.2±8.9%). It was decided to use the ensemble model of 18 classifiers due to its marginally better performance and, more importantly, its capacity to generate a more generalisable predictive approach. The proportion of lipids frequently selected as being important by the Boruta algorithm across 2000 iterations of the leave-group-out cross-validation were investigated. Figure 2b illustrates the top 30 features sorted as per the proportion of selections (/2000). The top 20 lipids (proportion: 80 – 100%, the accuracy of the corresponding signature: 84.6 – 84.8%) were selected as the final signature for training the final ensemble model. Final ensemble model The final ensemble model comprises 18 classifiers, each using the previously described 20 lipid features as its predictive variables. Hyperparameters for each classifier were chosen according to the model, which obtained its median accuracy across runs where the method obtained its best rank. For example, K-nearest neighbours ranked best in 44 out of 2000 runs. Of these 44 runs, the median accuracy was 90.4% which corresponded to a hyperparameter of k=9, which was subsequently chosen as the optimal parameter. The median run was selected to avoid biasing models towards overly difficult or simple testing samples. The final model was then trained using LGOCV (20% test, 80% train) and repeated for another 2000 runs to rigorously evaluate the performance of the final model, adjusting for the selection bias. The average performance of the ensemble model, as well as 18 classifiers (using the 20- lipid signature as predictive variables), across 2000 LGOC iterations is reported in Table 1. The ensemble model is the best performing model (acc=86.1±4.5%, tpr=91.4±5.4%, tnr=78.7±8.6%, c.f., Figure 3a-c) and shows stable results across iterations as demonstrated by Figure 3a boxplots representing the interquartile range (IQR) of different metrics. Also, the ensemble model can naturally represent the agreement between individual classifiers, which can be used as a measure of prediction ‘certainty’. Interestingly, an absolute certainty (i.e., complete agreement) in classifying 72.8% of correctly predicted samples was observed, while none of the misclassified samples was predicted with high certainty (Figure 3d). Signature size sensitivity analyses A sensitivity analysis was performed to assess the impact of reducing or increasing the number of features on the ensemble model accuracy. Using the top 30 lipids (Figure 2b) sorted as per their robustness, we reduced features one at a time (i.e., 30 lipids, 20 lipids, … 14 lipids) and trained the model using LGOCV. Figure 4 shows that the 20-lipid signature is the optimal size, but increasing or decreasing the signature size does not greatly influence the performance or accuracy of the model. Refinement of the plasma biomarker panel selection First, lipids known to contain duplicate information (i.e., measuring isomers from the same lipid species) were removed. This resulted in the removal of LID296 and LID408 in favour of LID240 and LID428. Model performance was not observed to be significantly impacted by this change to the biomarker panel (acc=86%, tpr=91.3%, tnr=78.5%). The inventors further explored the effect of removing two of the non-priority lipids (LID142 and LID240) in favour of an additional two “high-priority” lipids (LID371 and LID437). However, this was observed to have a slight impact on the ensemble model performance (acc=85.1%, tpr=89.6%, tnr=78.6%). These three biomarker panels are summarised in Table 2. B. EV analyses Performance of classifiers and patient-level predictions The pipeline described in Figure 1 was implemented and executed, resulting in 2000 lipid signatures and 2000 predictive modelling suits (comprising 18 classifiers and an ensemble approach based on the majority vote – 6 were removed due to poor model performance). Within each iteration, the hyperparameters of each classifier were optimised using a random search procedure over 50 iterations of nested leave-group-out cross-validation. Figure 5a demonstrates the average prediction of each model for individual patients across 2000 runs. The Ensemble model (majority vote) was the best performing method (acc=84.1±4.6%, tpr=89.3±5.7%, tnr=76.9±9.1%), closely followed by Neural Networks Using Model Averaging [4] (acc=83.4±4.8%, tpr=87.4±6.1%, tnr=77.9±9.3%) and Distance Weighted Discrimination with a polynomial basis function [3] (acc=83.3±4.9%, tpr=86.1±6.2%, tnr=79.4±9.1%). The inventors opted to use the ensemble model of 18 classifiers due to its marginally better performance and, more importantly, its capacity to generate a more generalisable predictive approach. The proportion of lipids frequently selected as being important by the Boruta algorithm across 2000 iterations of the leave-group-out cross-validation were investigated. Figure 5b illustrates the top 30 features sorted as per the proportion of selections (/2000). The top 20 lipids (proportion: 80 – 100%, the accuracy of the corresponding signature: 83.8 – 84.1%) were selected as the final signature for training the final ensemble model. Final ensemble model The final ensemble model comprises 18 classifiers, each using the previously described 20 lipid features as its predictive variables. Hyperparameters for each classifier were chosen according to the model, which obtained its median accuracy across runs where the method obtained its best rank. The median run was selected to avoid biasing models towards overly difficult or simple testing samples. The final model was then trained using LGOCV (20% test, 80% train) and then repeated for another 2000 runs to rigorously evaluate the performance of the final model with respect to the selection bias. The average performance of the ensemble model, as well as 18 classifiers (using the 20- lipid signature as predictive variables), across 2000 LGOC iterations is reported in Table 3. The ensemble model exhibited the best sensitivity (acc=86.1±4.4%, tpr=90.4±5.3%, tnr=80.2±8.7%, c.f., Figure 6a-c) and showed stable results across iterations as demonstrated by Figure 6a boxplots and the interquartile range (IQR) of different metrics; however, the Distance Weighted Discrimination model with a polynomial basis function managed to marginally outperform the ensemble model in terms of raw accuracy (acc=86.3±4.4%, tpr=88.3±5.7%, tnr=83.8±8.1%, c.f., Figure 6a-c). Also, the ensemble model can naturally represent the agreement between individual classifiers, which can be used as a measure of prediction ‘certainty’. Interestingly, an absolute certainty (i.e., complete agreement) in classifying 58.8% of correctly predicted samples was observed, while none of the misclassified samples was predicted with high certainty (Figure 6c). Signature size sensitivity analyses A sensitivity analysis was performed to assess the impact of reducing or increasing the number of features on the ensemble model accuracy. Using the top 30 lipids (Figure 2b) sorted as per their robustness, the inventors reduced features one at a time (i.e., 30 lipids, 20 lipids, … 14 lipids) and trained the model using LGOCV. Figure 7 shows that the 20-lipid signature is the optimal size, but increasing or decreasing the signature size does not greatly influence the performance or accuracy of the model. C. Plasma vs EV Comparisons In general, the results of the EV analyses were concordant with those of the Plasma analyses. There was strong overlap between the top 20 lipids selected by each either approach (12 out of 20, Figure 8a), model confidence was slightly higher in the Plasma models (Figure 9b) and model performance was largely similar with both approaches (Figure 9c).
Table 1. Final Plasma model results. Average performance measures of 19 classifiers, including the ensemble model across 2000 LGOCV where each model uses 20-lipid signature as its predictive variables. Details of each model and the corresponding parameters are available from the caret package [1].
Figure imgf000105_0001
Table 2. Refined plasma biomarker panel selection(s). Original Selection -- The original selection of 20 lipids suggested by the Boruta feature selection algorithm; No Flagged Duplicates – biomarker selection after removal of lipids flagged as containing duplicate information; Extra Lipids of Interest – biomarker selection after substitution of two low-priority lipids with high- priority lipids.
Figure imgf000106_0001
Table 3. Final EV model results. Average performance measures of 19 classifiers, including the ensemble model across 2000 LGOCV where each model uses 20-lipid signature as its predictive variables. Details of each model and the corresponding parameters are available from the caret package [1]
Table 4 – Identity of 30 lipid biomarker signature derived from plasma samples Lipid ID Lipid biomarker Skyline ID (LI+AD_RT 3dp_MzNoAD 4dp) # LID413 TG(50:1e)+NH4_17.195_818.7727 TG(16:0e_16:0_18:1)+NH4_17.206_818.7727 LID437 TG(58:2)+NH4_17.521_942.8615 TG(18:1_18:1_22:0)+NH4_17.523_942.8615 LID130 PE(38:6p)+H_8.689_747.5203 PE(16:0p_22:6)+H_8.638_747.5203 LID403 TG(42:2)+NH4_14.362_718.6111 TG(12:0_12:0_18:2)+NH4_14.34_718.6111 LID359 PE(38:2p)+H_12.531_755.5829 PE(20:0p_18:2)+H_12.502_755.5829 LID448 TG(60:5)+NH4_17.234_964.8459 TG(18:1_20:4_22:0)+NH4_17.24_964.8459 LID427 TG(56:0)+NH4_17.53_918.8615 TG(18:0_18:0_20:0)+NH4_17.519_918.8615 LID371 PI(38:6)-H_6.223_882.5258 PI(18:2_20:4)-H_6.167_882.5258 LID428 TG(56:1)+NH4_17.513_916.8459 TG(16:0_18:1_22:0)+NH4_17.517_916.8459 LID240 LPC(18:0)+H_3.698_523.3638 LPC(18:0)+H_3.674_523.3638 LID142 PE(36:5p)+H_8.11_721.5046 PE(16:0p_20:5)+H_8.043_721.5046 LID114 PI(36:3)-H_7.365_860.5415 PI(18:1_18:2)-H_7.286_860.5415 LID407 TG(44:2)+NH4_15.051_746.6424 TG(16:0_10:0_18:2)+NH4_15.017_746.6424 LID417 TG(52:3e)+NH4_16.938_842.7727 TG(16:0e_18:1_18:2)+NH4_16.944_842.7727 LID408 TG(44:2)+NH4_15.051_746.6424 TG(16:0_10:1_18:1)+NH4_15.017_746.6424 LID145 PE(36:3p)+H_9.458_725.5359 PE(18:1p_18:2)+H_9.415_725.5359 LID121 PG(36:1)-H_10.445_776.5567 PG(18:0_18:1)-H_10.388_776.5567 LID117 PI(36:1)-H_10.026_864.5728 PI(18:0_18:1)-H_9.938_864.5728 LID182 LPC(16:0)+H_2.839_495.3325 LPC(16:0)+H_2.8_495.3325 LID367 PI(36:4)-H_6.375_858.5258 PI(18:2_18:2)-H_6.318_858.5258 LID107 PS(36:1)-H_10.286_789.552 PS(18:0_18:1)-H_10.13_789.552 LID126 PE(40:6e)-H_10.941_777.5672 PE(18:1e_22:5)-H_10.867_777.5672 LID106 PS(38:4)-H_8.806_811.5363 PS(18:0_20:4)-H_8.663_811.5363 LID146 PE(36:2p)+H_11.174_727.5516 PE(18:0p_18:2)+H_11.13_727.5516 LID153 PE(34:2p)+H_9.427_699.5203 PE(16:0p_18:2)+H_9.379_699.5203 LID184 LPC(14:0)+H_1.973_467.3012 LPC(14:0)+H_1.916_467.3012 LID291 PC(36:4)+H_7.174_781.5622 PC(18:2_18:2)+H_7.106_781.5622 LID296 PC(36:2)+H_9.901_785.5935 PC(18:1_18:1)+H_9.826_785.5935 LID298 PC(36:2)+H_9.901_785.5935 PC(18:0_18:2)+H_9.826_785.5935 LID363 PE(38:6e)-H_9.787_749.5359 PE(18:1e_20:5)-H_9.742_749.5359 Table 5 – Identity of 30 lipid biomarker signature derived from EV samples Lipid ID Lipid biomarker (LI+AD_RT Isomer ID (LI+AD_RT 3dp_MzNoAD 4dp) # 3dp_MzNoAD 4dp) LID106 PS(38:4)-H_8.806_811.5363 PS(18:0_20:4)-H_8.806_811.5363 LID332 PC(36:0)+H_12.46_789.6248 PC(18:0_18:0)+H_12.46_789.6248 LID371 PI(38:6)-H_6.223_882.5258 PI(18:2_20:4)-H_6.223_882.5258 LID153 PE(34:2p)+H_9.427_699.5203 PE(16:0p_18:2)+H_9.427_699.5203 LID126 PE(40:6e)-H_10.941_777.5672 PE(18:1e_22:5)-H_10.941_777.5672 LID367 PI(36:4)-H_6.375_858.5258 PI(18:2_18:2)-H_6.375_858.5258 LID121 PG(36:1)-H_10.445_776.5567 PG(18:0_18:1)-H_10.445_776.5567 LID142 PE(36:5p)+H_8.11_721.5046 PE(16:0p_20:5)+H_8.11_721.5046 LID107 PS(36:1)-H_10.286_789.552 PS(18:0_18:1)-H_10.286_789.552 LID155 SM(d35:1)+H_8.528_716.5832 SM(d35:1)+H_8.528_716.5832 LID417 TG(52:3e)+NH4_16.938_842.7727 TG(16:0e_18:1_18:2)+NH4_16.938_842.7727 LID184 LPC(14:0)+H_1.973_467.3012 LPC(14:0)+H_1.973_467.3012 LID263 PC(34:0)+H_11.078_761.5935 PC(18:0_16:0)+H_11.078_761.5935 LID433 TG(58:1)+NH4_17.517_944.8772 TG(18:0_18:1_22:0)+NH4_17.517_944.8772 LID146 PE(36:2p)+H_11.174_727.5516 PE(18:0p_18:2)+H_11.174_727.5516 LID375 PS(40:7)-H_8.792_833.5207 PS(20:3_20:4)-H_8.792_833.5207 LID386 SM(d39:2)+H_10.258_770.6302 SM(d39:2)+H_10.258_770.6302 LID65 SM(d41:2)+H_11.798_798.6615 SM(d41:2)+H_11.798_798.6615 LID419 TG(53:0)+NH4_17.397_876.8146 TG(18:0_18:0_17:0)+NH4_17.397_876.8146 LID437 TG(58:2)+NH4_17.521_942.8615 TG(18:1_18:1_22:0)+NH4_17.521_942.8615 LID389 SM(d40:2)+H_11.106_784.6458 SM(d40:2)+H_11.106_784.6458 LID145 PE(36:3p)+H_9.458_725.5359 PE(18:1p_18:2)+H_9.458_725.5359 LID72 SM(d37:1)+H_10.366_744.6145 SM(d37:1)+H_10.366_744.6145 LID414 TG(51:0)+NH4_17.154_848.7833 TG(18:0_16:0_17:0)+NH4_17.154_848.7833 LID443 TG(59:1)+NH4_17.626_958.8928 TG(25:0_16:0_18:1)+NH4_17.626_958.8928 LID298 PC(36:2)+H_9.901_785.5935 PC(18:0_18:2)+H_9.901_785.5935 LID339 PC(36:5e)+H_7.966_765.5672 PC(16:0e_20:5)+H_7.966_765.5672 LID296 PC(36:2)+H_9.901_785.5935 PC(18:1_18:1)+H_9.901_785.5935 LID64 SM(d41:3)+H_10.493_796.6458 SM(d41:3)+H_10.493_796.6458 LID291 PC(36:4)+H_7.174_781.5622 PC(18:2_18:2)+H_7.174_781.5622 References 1. Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of statistical software, 28, 1-26. 2. Kursa, M. B., & Rudnicki, W. R. (2010). Feature selection with the Boruta package. Journal of statistical software, 36, 1-13. 3. Marron, J. S., Todd, M. J., & Ahn, J. (2007). Distance-weighted discrimination. Journal of the American Statistical Association, 102(480), 1267-1271. 4. Ripley, B. D. (2007). Pattern recognition and neural networks. Cambridge university press.
Example 2. Normalization and batch effect removal In this Example, the ability of a wide range of normalization methods to remove batch effects, while maintaining the separation of disease states, was investigated. The 20 lipid signature from Example 1 was utilised in this Example. A modular approach to normalization was considered, whereby different combinations could be tested to find the ideal normalization approach for this data. Across Subjects or (AS) refers to the fact that normalization was done to align subjects within a group. This is opposed to Within Subjects or (WS), which normalizes each subject separately. AS normalization approaches were also combined with Batch, where normalization is performed within patient groups, and/or Control-based (CB), where centering and scaling factors for normalization were calculated with respect to controls only. These general approaches to administering normalization were applied using the following functions: median, z-score, trimmed z-score (taking the .05-.95 interquartile range), and MinMax. Furthermore, the present inventors experimented with using measurements of standardized reference material (SRM) to account for batch effects as well as using ComBat (Johnson, Li, & Rabinovic, 2007), a normalization protocol commonly applied to bulk-genomics data. Models were trained on the P250 dataset (comprising 250 plasma samples from patients with similar demographic (all 250 are Eastern European) and are fasted status) and used to predict samples from the P550 dataset (comprising 550 plasma samples from patients with diverse demographic and fasting status; see Table 8 below). Six samples from the P250 dataset were repeated in the P550 dataset as controls and to assist with batch correction Predictive performance was assessed using diverse metrics (metric-agnostic approach) to capture different aspects of model performance and provide a holistic understanding of how well a predictive model is performing after batch correction or normalization. These included Accuracy (acc), True Positive Rate or Sensitivity (tpr), True Negative Rate or Specificity (tnr), and Diagnostic Odds Ratio (dor) (Glas, Lijmer, Pins, Bonsel, & Bossuyt, 2003). A comparison of model performance metrics for each of these approaches is provided in Figure 9a. In summary, each of the normalization strategies improved model performance and the control-based median and control-based trimmed z-score normalization normalization methods were identified as the best-performing normalization strategies. Per-lipid boxplots (Figure 9b) and UMAP projections (Figure 9c), before and after normalizations, demonstrate the ability of normalization methods in mitigating batch effects between datasets at the lipid- and sample-level. Table 6. Blind validation metrics on the P550 dataset stratified by patient groups after selected normalization methods. a) log-transformation only (no normalization); b) control- based median normalization; c) control-based trimmed z-score normalization. Reported metrics are accuracy (acc), sensitivity or true positive rate (tpr), specificity or true negative rate (tnr), and diagnostic odd ratio (dor).
Figure imgf000112_0001
Example 3. Fine Tuning Signature and Refining Ensemble This Example was designed to investigate the role of the lipid panel in model performance by replicating the feature selection process conducted during Example 1 using both of the P250 and P550 datasets described in Example 2 and the 80 lipid species present in the P550 dataset. A strong overlap was observed with the previously selected 20-lipid biomarker panel (Figure 10a) while identifying a further 5 lipids (Table 7), which may be useful for improved model performance (Figure 10b-d). Furthermore, it was observed that a signature containing only 15 biomarkers demonstrated robust diagnostic capabilities. Table 7 – Identity of 15 lipid biomarker signature Lipid ID # Lipid biomarker (LI+AD_RT Isomer ID (LI+AD_RT 3dp_MzNoAD 4dp) 3dp_MzNoAD 4dp) LID374 PS(40:6)-H_8.422_835.5363 PS(18:0_22:6)-H_8.266_835.5363 LID106 PS(38:4)-H_8.806_811.5363 PS(18:0_20:4)-H_8.663_811.5363 LID107 PS(36:1)-H_10.286_789.552 PS(18:0_18:1)-H_10.13_789.552 LID367 PI(36:4)-H_6.375_858.5258 PI(18:2_18:2)-H_6.318_858.5258 LID117 PI(36:1)-H_10.026_864.5728 PI(18:0_18:1)-H_9.938_864.5728 LID291 PC(36:4)+H_7.174_781.5622 PC(18:2_18:2)+H_7.106_781.5622 LID317 LPC(18:3)+H_1.907_517.3168 LPC(18:3)+H_1.896_517.3168 LID184 LPC(14:0)+H_1.973_467.3012 LPC(14:0)+H_1.916_467.3012 LID114 PI(36:3)-H_7.365_860.5415 PI(18:1_18:2)-H_7.286_860.5415 LID371 PI(38:6)-H_6.223_882.5258 PI(18:2_20:4)-H_6.167_882.5258 LID383 SM(d38:4)+H_9.342_752.5832 SM(d38:4)+H_9.269_752.5832 LID396 SM(d42:4)+H_9.859_808.6458 SM(d18:1_24:3)+H_9.762_808.6458 LID126 PE(40:6e)-H_10.941_777.5672 PE(18:1e_22:5)-H_10.867_777.5672 LID146 PE(36:2p)+H_11.174_727.5516 PE(18:0p_18:2)+H_11.13_727.5516 LID363 PE(38:6e)-H_9.787_749.5359 PE(18:1e_20:5)-H_9.742_749.5359 The inventors made further refinements to the ensemble model by excluding 5 models that did not allow for probabilities in the prediction, leaving a total of 13 classifiers in the ensemble. Two types of ensemble model were compared: the “Majority Vote” model, which replicates the ensemble used in Example 1 and involves each model voting for a single class (Cancer or Control), and the “Averaged Probability” model, which simply averages the probability outputs of the 13 component models (using 0.5 as the threshold for predicting cancer). The refined ensemble model, i.e., “Average Probability,” showed a marginal improvement in performance while allowing for standard ROC curves and fine thresholding analyses. This approach was then used for subsequent analyses, including those presented in Figure 10. Example 4. Training models on homogenous data Homogenous models The present Example explored the extent to which model performance is being modulated by batch effects versus an inherent difficulty represented by the cohort. To this end, the present inventors trained homogeneous models, such that the test and train sets were derived from the same patient group. In particular, the present inventors were interested in determining whether accurate prediction was possible in the AU-Fed group. Three patient groups were used in this analysis: P250, the original dataset from Example 1; EU, the European cancers and benign controls; and AU-Fed, the fed Australian cancers and controls (see Table 8). Table 8. Demographic characteristics of patient groups
Figure imgf000113_0001
(N=155) (N=109) (N=107) (N=149) (N=302) (N=822) Age Mean (SD) 52.6 (11.6) 54.9 (11.1) 57.9 (11.4) 56.6 (11.4) 58.5 (10.6) 56.5 (11.3) Median [Min, 53.0 [22.0, 55.0 [26.0, 57.0 [33.0, 57.0 [30.0, 59.0 [32.0, 57.0 [22.0, Max] 85.0] 85.0] 82.0] 86.0] 82.0] 86.0] Missing 8 (5.2%) 8 (7.3%) 0 (0%) 0 (0%) 0 (0%) 16 (1.9%) BMI Mean (SD) 24.3 (3.90) 25.9 (5.75) 28.1 (4.55) 27.2 (4.65) 27.4 (4.86) 26.8 (4.88) Median [Min, 23.7 [18.8, 24.6 [18.9, 27.4 [18.6, 26.6 [17.0, 27.2 [18.0, 26.2 [17.0, Max] 38.6] 55.5] 42.2] 41.8] 39.9] 55.5] Missing 42 (27.1%) 39 (35.8%) 0 (0%) 0 (0%) 0 (0%) 81 (9.9%) LDL
Figure imgf000114_0001
Max] NA] NA] 5.39] 5.87] [0.730, [0.730, 7.64] 7.64] Missing 155 109 0 (0%) 0 (0%) 0 (0%) 264 (100%) (100%) (32.1%) HDL Mean (SD) NA (NA) NA (NA) 1.34 1.44 1.47 1.44 (0.335) (0.398) (0.414) (0.398) Median [Min, NA [NA, NA [NA, 1.27 1.40 1.43 1.40 Max] NA] NA] [0.880, [0.650, [0.400, [0.400, 2.77] 3.07] 3.15] 3.15] Missing 155 109 0 (0%) 0 (0%) 0 (0%) 264
Figure imgf000114_0002
0 1 0 0 0 1 Intermediate 0 (0%) 1 (0.9%) 0 (0%) 0 (0%) 0 (0%) 1 (0.1%) Grade Tumour 1 = 3 0 (0%) 1 (0.9%) 0 (0%) 0 (0%) 0 (0%) 1 (0.1%) Tumour 2 = 2 N/A 0 (0%) 0 (0%) 57 (53.3%) 94 (63.1%) 112 263 (37.1%) (32.0%) Gx 0 (0%) 0 (0%) 0 (0%) 4 (2.7%) 1 (0.3%) 5 (0.6%) Missing 155 64 (58.7%) 0 (0%) 0 (0%) 0 (0%) 219 (100%) (26.6%)
Figure imgf000115_0001
(100%) (100%) (32.1%) The results are summarized in Figure 11 and Table 9. The results of this internal validation for each cohort represent an improvement in model performance in comparison to previous attempts. This is likely due to the model being optimized for each cohort and the training set being as similar to the test set as feasible. Table 9. Model performance metrics for homogeneous models. Accuracy, F1 Score, Sensitivity, Specificity, and Diagnostic Odds Ratio are reported.
Figure imgf000115_0002
Ensemble of homogenous models The previous analysis investigating the performance of homogeneous models was expanded on to determine the predictive capacity of each patient group (AU-Fed, EU, and P250) to predict other groups (Table 10). Furthermore, an ensemble of these homogeneous models was aggregated by averaging predictions across the 3 patient groups (Figure 12). The results are promising in that homogeneous models appear to be a viable way to obtain competitive performance on fed samples. Table 10. Model diagnostics for models trained on homogeneous cohorts when predicting each of the patient groups. The model group indicates which group was used to train the model, and the patient group indicates which group was used for prediction and, therefore, estimation of model diagnostics. Accuracy, F1 Score, Sensitivity, Specificity, and Diagnostic Odds Ratio are reported. Example 5. Training and internal validation of models on selected cohorts In this Example, two additional models were constructed and analysed around selected cohorts in order to complement the previous analyses. These two cohorts are comprised of the EU cancer and P250 patients (Figure 13a, Table 11b); and EU cancer, Australian fasted control, and P250 patients (Figure 13b, Table 11c). Models were trained using the original set of 20 lipids identified during Example 1 on the refined ensemble of models in order to yield prediction probability estimates between 0 and 1. These models were tested in order to explore the potential model performance when data with sub-optimal characteristics are excluded from consideration (e.g., fed patients and benign controls). The results suggest a strong improvement over previous observations may be possible. Table 11. Model performance metrics for each of the selected additional cohorts. (a) P250 only, (b) P450 (P250 + EU cancer patients), and (c) P250 + EU cancer + AU control patients.
Figure imgf000116_0001
Example 6 The aim of the present Example was to use QQQ-MS to identify a plasma-derived lipid biomarker signature. The samples tested, all collected from European patients, are summarized in Table 12. These samples have been divided into 4 analysis groups (AGs) according to the cohort and year in which they were collected. Demographic information for the 4 AGs is reported in Table 13. Table 12: Sample counts by analysis group, the cohort of origin, year collected, and disease classification
Figure imgf000117_0001
Table 13: Demographic information for study samples. Summary: • External validation of the lipid panel optimized on AG2-4 (Mix 0) resulted in an accuracy of 0.69, sensitivity of 0.46, specificity of 0.82, and AUC of 0.75. • External validation of the Economy panel (Mix 5) resulted in an accuracy of 0.65, sensitivity of 0.78, specificity of 0.57, and AUC of 0.75 • Analyses performed in addition to those in the planned SAP demonstrated favourable results by reducing the size of the optimized lipid panel. o A small, yet diverse, 12 lipid subset (Mix 7) of the optimized panel was identified which exhibited significantly improved performance during quasi-external validation (accuracy=0.73, sensitivity=0.76, specificity=0.71, AUC=0.77) • QQQ-MS and HR-MS demonstrated high fidelity in most lipids, and prediction models trained on each modality provided practically equivalent results. • A small number of lipids were quantified differently by the two modalities; however, these discrepancies can be explained and in some cases are expected. QQQ-MS optimized lipid panel identification Bootstrapped Boruta feature selection was used to identify a panel of 20 lipids suitable for predicting breast cancer diagnosis status from QQQ-MS quantified lipid concentrations using AG2-4 (see Table 13). By applying Boruta to 1,000 bootstrapped samples, the present inventors were able to identify lipids that are likely to be relevant to a broader population. Furthermore, the approach is less likely to be influenced by outliers.20 lipids were identified as important in greater than 70% of bootstrapped samples. Of which, 12 were identified as important in greater than 96% of bootstrapped samples (Figure 14a). The panel of 20 lipids was considered as the optimized panel for subsequent investigations. The optimized 20-lipid panel shows separation of cancer cases and controls of AG2-4 as in 2D space after UMAP dimensionality reduction (Figure 14b). This lipid panel includes lipids that are both upregulated and downregulated in cancer patients (Figure 14c). The panel includes acyl-carnitine (AcCa), lyso-glycerophospholipids (LPA, LPC, & LPI), glycerophospholipids (PC, PE, PI, PS), and sphingolipids (ceramides (Cer) and S1P)). No lysophosphatidylethanolamine (LPE), phosphatidylglycerol (PG), or triglycerides (TG) were identified. While collinearity is relatively low, some lipid species exhibit correlations (Figure 14d). Internal validation of the optimized 20-lipid panel LOOCV was performed on patients from AG2-4 using the 20-lipid panel identified above. For each iteration of LOOCV, one patient was held out and an ensemble of models, as detailed in the SAP, was trained on the remaining patients. The trained model was then used to predict the held-out patient. This process was repeated for each patient, and the results were reported by the patient group as well as in aggregate in Table 14. Table 14: Internal validation (LOOCV) of the ensemble model trained on the optimized panel from outcome 1a stratified by each of the analysis groups. N/A’s are present in AG4 due to the lack of controls (i.e. the analysis group is cancer-only).
Figure imgf000119_0001
External validation of optimized lipid panel External validation on AG1 An ensemble of models was trained on samples from AG2-4 samples using the lipid panel identified above. This model was evaluated in AG1, resulting in an accuracy of 0.69, sensitivity of 0.46, specificity of 0.82, and AUC of 0.75 (Table 15, AG1). The UMAP plot indicates good separation between cancer and control patients in this cohort (Figure 15b). External validation on AG2 and AG3 Models were trained on AG1/3/4 and AG1/2/4 and used to predict patients from AG2 and AG3 respectively. Predictions were not obtained for AG4 because this group was composed of only cancer patients. This analysis may be referred to as a quasi-external validation. Lipid panels were constructed with information of subjects in both AG2/3. Information leakage prevents this from being considered as a true external validation. Performance on AG3 was better than AG2 (F1 score of 0.83 vs 0.69). Table 15: External validation of ensemble model trained on the optimized panel from outcome 1a stratified by each of the analysis groups
Figure imgf000120_0001
Internal validation of Economy lipid panel LOOCV was performed on all patients (AG2-4) using the Economy panel (listed in Table 18). An ensemble of models was trained on all patients except for one and used to predict the held- out patient. This procedure was repeated for each patient and summary performance metrics are provided in Table 16. Table 16. Internal validation (LOOCV) of the Economy panel on AG2-4
Figure imgf000120_0002
External validation of Economy (Mix 5) lipid panel The procedure described above was repeated using the Economy lipid panel for model training. In comparison to the optimized panel, performance of the Economy panel was good. Performance was increased in AG1 (F1 score of 0.62 vs 0.52) and was roughly equivalent in both AG2 (0.70 vs 0.69) and AG3 (0.80 vs 0.83) (Table 17, Figure 16). Table 17. Quasi-external validation of the Economy Panel
Figure imgf000120_0003
Additional panels derived from the optimized 20-lipid signature Nine customized signatures were identified from the optimized 20-lipid panel, taking into account factors such as cost, collinearity, and the analytical reproducibility of the individual lipids. Additional lipid panels are named Mix1 to Mix10 which are detailed in Table 18 along with the Economy panel (Mix5) and the 20-lipid panel (Mix0).
Table 18. Lipids present in each of specified panels
Figure imgf000122_0001
* Mix0 refers to the 20-lipid panel. + Mix5 refers to the Economy panel Brief description of the panels: • Mix0 refers to the panel selected according to the top 20 lipids identified above. • Mix5 refers to the economy panel. • Mixes 1-5 do not have AcCa while mixes 0 and 6-10 do. • Mixes 1 and 6: Highly colinear lipids have had one or more of their clusters removed. • Mixes 2 and 7 have further excluded one or more lipids which form moderately colinear clusters. • Mixes 4 and 9 were selected such that internal standards exist for each lipid. • Mixes 8-10 experimented with removing LPAs as they are in low abundance. • Mix10 is easy to perform analytically and has internal standards for all lipids. Internal validation of additional lipid panels Internal validation of the additional panels generally indicated a similar level of performance across the board with each of the performance metrics generally tapering off as panel size decreases (Table 19, see Mixes 4, 9, and 10). The exception to this was Mix 3 which obtained a similar AUC to Mix 0, the full 20 lipid panel (Figure 17). Table 19. Internal validation results (LOOCV) for additional panels and the Economy panel (Mix5) aggregated on AG2-4.
Figure imgf000123_0001
Results for Mix0 (20-lipid signature) are repeated here for ease of reference. External validation of additional panels External validation on AG1 Table 20 and Figure 18 present the results of external validation on AG1 across each of the panels. Likewise, Table 21 and Figure 19 present the results of quasi-external validation on AG’s 2 and 3. The decrease in performance previously seen in smaller mixes was not observed in this set of validations. In particular, Mixes 2 and 7 were strong performers (AUC 0.79 and 0.77, ACC 0.72 and 0.73 respectively). Mixes 0 and 3, while exhibiting the best internal validation results, showed relatively low performance (AUC 0.75 and 0.75, ACC 0.69 and 0.66 respectively). Table 20. External validation results for additional panels on AG1. Results for Mix0 and Mix5 are repeated here for completeness.
Figure imgf000124_0001
Table 21. Quasi-external validation results for additional panels on AG1. Results for Mix0 and Mix5 are repeated here for completeness.
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Predictions at the patient level Figure 20 presents patient-level prediction scores for models trained on each of the lipid mixes during (quasi)-external validation. As previously seen, AG3 appears to provide the cleanest distinction between cancer and non-cancer controls, while the separation is less clear in AG1. Statistical significance analyses of performance differences Statistical analyses were performed to compare the accuracies, sensitivities, and specificities for quasi-external validation of the models reported in Tables 10 and 11. Wu’s test (Wu et al., Journal of Biopharmaceutical Statistics, vol.33, no.1, pp.31–42, Jan.2023) indicated that at least one of the panels tested had a significantly different sensitivity or specificity from the other panels (X=44, df=10, p=0.0015, X is the test statistics and df refers to the degree of freedom). Pairwise McNemar’s test of model accuracies is reported in Table 22. Mix7 is the only panel that statistically significantly (α = 0.05) outperforms the optimized panel. Mix7 also outperforms the Economy panel (p = 0.01). Table 22. Pairwise McNemar’s test p-values (unadjusted) comparing quasi-external validation performance (accuracy) between two sets of lipid panels.
Figure imgf000126_0001
Figure imgf000126_0002
Figure imgf000126_0003
Figure imgf000126_0004
Figure imgf000126_0005
Figure imgf000126_0006
Figure imgf000126_0007
Figure imgf000126_0008
Figure imgf000126_0009
Figure imgf000126_0010
QQQ-MS vs. HR-MS lipid-wise comparison QQQ-MS and HR-MS quantification methods were compared for each patient which is represented in both datasets (Table 23). Strong correlations were observed in log concentrations for most lipids investigated (Figures 21 and 22). QQQ-MS vs. HR-MS paired-sample comparison The bootstrapped Boruta procedure detailed above was performed on the subset of patients detailed in Table 23 to obtain a panel of lipids optimized on the same cohort. This was done to allow for a fair, practical comparison of models trained on the two modalities. LOOCV was performed on the subsets of AG2 and AG3 (see Table 23) for which both QQQ-MS and HR-MS data are available. For each subject, two models were trained and two predictions were obtained (QQQ-MS and HR-MS). Lachenbruch’s test (Wu 2023), i.e. special case of Wu’s test for n=2, was used to compare paired samples of predictions from the two modalities. Regardless of the panel used (mix0-10), no significant differences were found in predictive score (p>.28). Therefore, there do not appear to be any practical differences in the two data sources (QQQ-MS and HR-MS) for the purposes of model training and prediction (Figure 23). Table 23. Breakdown of AG2 and AG3 for QQQ-MS vs. HR-MS paired-sample comparison; in parentheses are P250 sample numbers. * Partial cohort selected. Conclusions An optimized lipid panel was obtained using a bootstrapped Boruta approach applied to AG2-4 patient cohorts. A number of smaller panels were also derived from the optimized lipid panel. Mix7 was identified as outperforming both the optimized panel as well as economy panel (Mix5) (Figure 24). Mix7 exhibited significantly better performance than both mixes 0 and 5 (Table 12). Mix7 consistently performed the best across each of the (quasi-)external validations (Tables 10 and 11). Mix7 is a small panel (n=12 lipids) comprising a diverse range of lipid classes. Furthermore, no triglycerides were included in this panel which should reduce analytical run time. Additional analyses were performed to compare QQQ-MS and HR-MS modalities. With the exception of a small handful of lipids, strong correlations were observed in measured concentrations. No significant differences could be found in models trained on QQQ-MS or HR- MS modalities indicating practical equivalence of the two measurements.
Table 14: Lipid concentration changes between healthy and diseased individuals in QQQ data (AG1-3) and HR data (P250). P-values have been adjusted using false-discovery-rate (FDR) corrections. FBR.name LID Mean Mean fold p-value Mean Mean fold p- concentration change in (QQQ) concentration change in value higher in (QQQ): Cancer higher in (HR): Cancer (HR) (QQQ): (HR): AcCa(18:2) LID572 Healthy -0.2 0<.001 NA NA NA Cer(d36:1) LID288 Cancer 0.07 0.041 Cancer 0.04 0.268 LPA(18:0) LID570 Healthy -0.3 0<.001 Healthy -0.37 0<.001 LPA(18:2) LID571 Healthy -0.4 0<.001 Healthy -0.39 0<.001 LPC(14:0) LID184 Healthy -0.51 0<.001 Healthy -0.56 0<.001 LPC(16:0) LID182 Healthy -0.25 0<.001 Healthy -0.27 0<.001 LPC(16:0e) LID181 Healthy -0.3 0<.001 Healthy -0.21 0<.001 LPC(18:0) LID240 Healthy -0.35 0<.001 Healthy -0.32 0<.001 LPC(18:2) LID237 Healthy -0.26 0<.001 Healthy -0.28 0<.001 LPC(18:3) LID317 NA NA NA Healthy -0.52 0<.001 LPI(18:1) LID568 Healthy -0.37 0<.001 Healthy -0.48 0<.001 PC(32:2) LID562 Healthy -0.26 0.001 Healthy -0.42 0<.001 PC(34:0) LID263 NA NA NA Healthy -0.23 0<.001 PC(36:0) LID332 Healthy -0.1 0<.001 Healthy -0.25 0<.001 PC(36:2) LID296 Cancer 0 0.857 Healthy -0.25 0<.001 PC(36:4) LID291 Healthy -0.41 0<.001 Healthy -0.57 0<.001 PC(36:5e) LID339 NA NA NA Healthy -0.3 0<.001 PC(38:5) LID225 Cancer 0.1 0.001 Healthy -0.02 0.47 PE(34:2p) LID153 Healthy -0.42 0<.001 Healthy -0.57 0<.001 PE(36:2) LID563 Healthy -0.27 0<.001 Healthy -0.38 0<.001 PE(36:2p) LID146 NA NA NA Healthy -0.5 0<.001 PE(36:3p) LID145 NA NA NA Healthy -0.47 0<.001 PE(36:5p) LID142 NA NA NA Healthy -0.68 0<.001 PE(38:2p) LID359 NA NA NA Healthy -0.39 0<.001 PE(38:6p) LID130 NA NA NA Healthy -0.26 0<.001 PE(38:6e) LID363 NA NA NA Healthy -0.57 0<.001 PE(40:6e) LID126 NA NA NA Healthy -0.35 0<.001 PE(40:7e) LID123 Healthy -0.24 0<.001 Healthy -0.29 0<.001 PG(36:1) LID121 Healthy -0.08 0.226 Healthy -0.28 0<.001 PI(36:1) LID117 Healthy -0.26 0<.001 Healthy -0.44 0<.001 PI(36:3) LID114 NA NA NA Healthy -0.46 0<.001 PI(36:4) LID367 NA NA NA Healthy -0.76 0<.001 PI(38:6) LID371 NA NA NA Healthy -0.49 0<.001 PS(36:1) LID107 Healthy -0.54 0<.001 Healthy -0.53 0<.001 PS(38:4) LID106 Healthy -0.5 0<.001 Healthy -0.58 0<.001 PS(40:6) LID374 NA NA NA Healthy -0.3 0<.001 PS(40:7) LID375 NA NA NA Healthy -0.31 0.012 SM(d35:1) LID155 Cancer 0.09 0.001 Cancer 0.12 0.003 SM(d36:2) LID166 Cancer 0.11 0<.001 Cancer 0.09 0.008 SM(d37:1) LID72 NA NA NA Cancer 0.08 0.094 SM(d38:4) LID383 NA NA NA Cancer 0.12 0<.001 SM(d39:2) LID386 NA NA NA Cancer 0.07 0.255 SM(d40:2) LID389 NA NA NA Cancer 0.01 0.846 SM(d41:2) LID65 NA NA NA Cancer 0.11 0.034 SM(d41:3) LID64 NA NA NA Cancer 0.09 0.067 SM(d42:4) LID396 NA NA NA Cancer 0.11 0.003 SphP(d18:1) LID567 Healthy -0.34 0<.001 Healthy -0.46 0<.001 TG(42:2) LID403 NA NA NA Healthy -0.99 0<.001 TG(44:2) LID407 NA NA NA Healthy -0.85 0<.001 TG(50:1e) LID413 NA NA NA Healthy -0.67 0<.001 TG(51:0) LID414 NA NA NA Healthy -0.41 0.001 TG(52:3e) LID417 Healthy -0.32 0<.001 Healthy -0.56 0<.001 TG(53:0) LID419 NA NA NA Healthy -0.29 0.002 TG(56:0) LID427 NA NA NA Healthy -0.59 0<.001 TG(56:1) LID428 NA NA NA Healthy -0.6 0<.001 TG(58:1) LID433 NA NA NA Healthy -0.64 0<.001 TG(58:2) LID437 Healthy -0.75 0<.001 Healthy -0.64 0<.001 TG(59:1) LID443 NA NA NA Healthy -0.26 0.004 TG(60:5) LID448 NA NA NA Healthy -0.48 0<.001 Cer(d42:1) LID252 Healthy -0.14 0 Healthy -0.01 0.871 PC(36:0) LID332 Healthy -0.1 0 Healthy -0.25 0
Figure imgf000129_0001
-0.17 0 Healthy -0.25 0 PC(18:1_18:1) LID296 Cancer 0 0.857 Healthy -0.25 0 PE(34:1) LID307 Healthy -0.04 0.536 Healthy -0.11 0.197 PI(34:1) LID119 Healthy -0.24 0 Healthy -0.33 0
Example 7 The aim of the present Example was to use HR-MS to identify a plasma-derived lipid biomarker signature. The samples tested were all collected from Australian women. These samples have been divided into 2 analysis groups based on whether or not they had breast cancer (i.e., 49 control samples and 50 case samples). The blood samples were processed to plasma and acquired using HR-MS. Analysis was performed using LipidSearch and Tracefinder software. Lipid biomarker levels were compared between control and breast cancer plasma samples, with biomarkers showing the greatest differential abundance further analysed for statistical significance. As can be observed in Figure 25, the levels of the lipid biomarkers of Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4) (i.e., PC(16:0/20:4) isomer) and PI(40:5) were all significantly elevated in breast cancer patients versus case controls and therefore could be useful in diagnostic assays for detecting breast cancer in patients.

Claims

CLAIMS: 1. A method of diagnosing a subject with a breast cancer, said method including the step of measuring a level of one or more lipid biomarkers in a biological sample from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof, and wherein the level of the one or more lipid biomarkers is diagnostic or indicative of the subject having the breast cancer.
2. The method of claim 1, wherein an increased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4), PC(38:5), PI(40:5), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3) and/or SM(d42:4), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and/or TG(60:5), or a fragment, variant or derivative thereof, is diagnostic or indicative of the subject having the breast cancer.
3. The method of Claim 1 or Claim 2, further including the step of administering a treatment for the breast cancer to the subject.
4. A method for measuring a level of one or more lipid biomarkers in a biological sample from a subject, said method including the steps of: (a) providing the biological sample; and (b) measuring the level of the one or more lipid biomarkers in the biological sample, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof.
5. The method of Claim 4, wherein the subject is suspected of having a breast cancer or has been previously diagnosed with a breast cancer.
6. The method of Claim 4 or Claim 5, wherein the measuring step includes determining the presence or absence of: (i) an increased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4), PC(38:5), PI(40:5), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3) and/or SM(d42:4), or a fragment, variant or derivative thereof, and/or (ii) a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and/or TG(60:5), or a fragment, variant or derivative thereof, in the biological sample of the subject.
7. A method of treating a breast cancer in a subject, said method including the step of performing a treatment in respect of the subject in which a level of one or more lipid biomarkers has been measured in a biological sample therefrom that is diagnostic or indicative of the subject having the breast cancer, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof.
8. The method of Claim 7, wherein the treatment includes administering a therapeutically effective amount of an anti-cancer treatment to the subject.
9. The method of Claim 7 or Claim 8, in which an increased level of Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPI(18:0), PC(36:4), PC(38:5), PI(40:5), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3) and/or SM(d42:4), or a fragment, variant or derivative thereof, and/or a decreased level of AcCa(18:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and/or TG(60:5), or a fragment, variant or derivative thereof, was measured from the biological sample of the subject.
10. The method of any one of the preceding claims, wherein the one or more lipid biomarkers comprise LPC(14:0), or a fragment, variant or derivative thereof, and one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof.
11. The method of any one of the preceding claims, wherein the one or more lipid biomarkers comprise PI(38:6), or a fragment, variant or derivative thereof, and one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof.
12. The method of any one of the preceding claims, wherein the one or more lipid biomarkers comprise LPC(14:0) and PI(38:6), or a fragment, variant or derivative thereof, and optionally one or more other lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof.
13. The method of any one of the preceding claims, wherein the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2), TG(50:1e), TG(52:3e), TG(56:0), TG(56:1), TG(58:2) and TG(60:5), or a fragment, variant or derivative thereof.
14. The method of any one of Claims 1 to 10, wherein the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), PC(36:2), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) and TG(52:3e), or a fragment, variant or derivative thereof.
15. The method of any one of Claims 1 to 10, wherein the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2), TG(52:3e) and TG(56:1), or a fragment, variant or derivative thereof.
16. The method of any one of Claims 1 to 12, wherein the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(16:0), PC(36:2), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(44:2), TG(52:3e) and TG(58:2), or a fragment, variant or derivative thereof.
17. The method of any one of Claims 1 to 12, wherein the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d37:1), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), TG(51:0), TG(52:3e), TG(53:0), TG(58:1), TG(58:2) and TG(59:1), or a fragment, variant or derivative thereof.
18. The method of any one of Claims 1 to 12, wherein the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d39:2), SM(d41:2), TG(52:3e), TG(53:0), TG(58:1) and TG(58:2), or a fragment, variant or derivative thereof.
19. The method of any one of Claims 1 to 10, wherein the one or more lipid biomarkers are selected from the group consisting of LPC(14:0), LPC(18:3), PC(36:4), PE(36:2p), PE(38:6e), PE(40:6e), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), SM(d38:4) and SM(d42:4), or a fragment, variant or derivative thereof.
20. The method of any one of Claims 1 to 10, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4), PC(38:5), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
21. The method of Claim 20, wherein the one or more lipid biomarkers comprise, consist of or consist essentially of: (a) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1); (b) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); (c) Cer(d36:1), LPA(18:0), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PS(38:4), SM(d36:2) and SphP(d18:1); (d) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1); (e) Cer(d36:1), Cer(d42:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), PC (36:0), PC(36:2) (such as PC(18:0_18:2) and/or PC(18:1_18:1)), PE(34:1), PI(34:1), SM(d36:2) and SphP(d18:1); (f) Cer(d36:1), Cer(d42:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), PC (36:0), PC(18:0_18:2), PC(18:1_18:1), PE(34:1), PI(34:1), SM(d36:2) and SphP(d18:1); (g) AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1); (h) AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); (i) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); (j) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1); (k) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1); (l) AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1); (m) Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4), PI(40:5) and SM(d36:2); (n) Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) and SM(d36:2); or (o) Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1); or a fragment, variant or derivative thereof.
22. The method of any one of the preceding claims, wherein the level of the one or more lipid biomarkers is or has been measured, at least in part, by mass spectrometry.
23. The method of any one of the preceding claims, wherein the predictive accuracy of the method, as determined by an ROC AUC value, is at least about 0.65, at least about 0.70, at least about 0.75 or at least about 0.80.
24. A system for determining the presence or absence of a breast cancer in a subject, the system comprising: a mass spectrometry unit configured for determining a level of one or more lipid biomarkers in a biological sample obtained from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof; and a processing unit configured for using or analysing the level of the one or more lipid biomarkers to determine the presence or absence of the breast cancer in the subject.
25. A kit for determining the presence or absence of a breast cancer in a subject, the kit comprising one or more reagents for determining a level of one or more lipid biomarkers in a biological sample obtained from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof.
26. The kit of Claim 25, wherein the one or more reagents comprise one or more probes, each probe being specific or selective for one of the one or more lipid biomarkers.
27. The method, system or kit of any one of the preceding claims, wherein the one or more lipid biomarkers comprises two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), LPC(18:3), LPI(18:0), LPI(18:1), PC(32:2), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PC(38:5), PE(34:2p), PE(36:2), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PE(40:7e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PI(40:5), PS(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d35:1), SM(d36:2), SM(d37:1), SM(d38:4), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), SM(d42:4), SphP(d18:1), TG(42:2), TG(44:2), TG(50:1e), TG(51:0), TG(52:3e), TG(53:0), TG(56:0), TG(56:1), TG(58:1), TG(58:2), TG(59:1) and TG(60:5), or a fragment, variant or derivative thereof.
28. The method, system or kit of Claim 27, wherein the one or more lipid biomarkers comprises two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:2p), PE(38:6p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(42:2), TG(44:2), TG(50:1e), TG(52:3e), TG(56:0), TG(56:1), TG(58:2) and TG(60:5), or a fragment, variant or derivative thereof.
29. The method, system or kit of Claim 27, wherein the one or more lipid biomarkers comprises two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), LPC(16:0), PC(36:2), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2) and TG(52:3e), or a fragment, variant or derivative thereof.
30. The method, system or kit of Claim 27, wherein the one or more lipid biomarkers comprises two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), LPC(16:0), LPC(18:0), PC(36:2), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PS(36:1), PS(38:4), TG(44:2), TG(52:3e) and TG(56:1), or a fragment, variant or derivative thereof.
31. The method, system or kit of Claim 27, wherein the one or more lipid biomarkers comprises two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), LPC(16:0), PC(36:2), PC(36:4), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(38:6e), PE(40:6e), PG(36:1), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), TG(44:2), TG(52:3e) and TG(58:2), or a fragment, variant or derivative thereof.
32. The method, system or kit of Claim 27, wherein the one or more lipid biomarkers comprises two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PC(36:2), PC(36:4), PC(36:5e), PE(34:2p), PE(36:2p), PE(36:3p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d37:1), SM(d39:2), SM(d40:2), SM(d41:2), SM(d41:3), TG(51:0), TG(52:3e), TG(53:0), TG(58:1), TG(58:2) and TG(59:1), or a fragment, variant or derivative thereof.
33. The method, system or kit of Claim 27, wherein the one or more lipid biomarkers comprises two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), PC(34:0), PC(36:0), PE(34:2p), PE(36:2p), PE(36:5p), PE(40:6e), PG(36:1), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:7), SM(d35:1), SM(d39:2), SM(d41:2), TG(52:3e), TG(53:0), TG(58:1) and TG(58:2), or a fragment, variant or derivative thereof.
34. The method, system or kit of Claim 27, wherein the one or more lipid biomarkers comprises two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), LPC(18:3), PC(36:4), PE(36:2p), PE(38:6e), PE(40:6e), PI(36:1), PI(36:3), PI(36:4), PI(38:6), PS(36:1), PS(38:4), PS(40:6), SM(d38:4) and SM(d42:4), or a fragment, variant or derivative thereof.
35. The method, system or kit of Claim 27, wherein the one or more lipid biomarkers comprises two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of AcCa(18:2), Cer(d36:1), LPA(18:0), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), LPI(18:1), PC(32:2), PC(36:4), PC(38:5), PE(34:2p), PE(36:2), PE(40:7e), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1), or a fragment, variant or derivative thereof.
36. The method of Claim 27, wherein the one or more lipid biomarkers comprises two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of: (a) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1); (b) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); (c) Cer(d36:1), LPA(18:0), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PS(38:4), SM(d36:2) and SphP(d18:1); (d) Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1); (e) Cer(d36:1), Cer(d42:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), PC (36:0), PC(36:2) (such as PC(18:0_18:2) and/or PC(18:1_18:1)), PE(34:1), PI(34:1), SM(d36:2) and SphP(d18:1); (f) Cer(d36:1), Cer(d42:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(16:0), LPC(18:0), LPC(18:2), PC (36:0), PC(18:0_18:2), PC(18:1_18:1), PE(34:1), PI(34:1), SM(d36:2) and SphP(d18:1); (g) AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d35:1), SM(d36:2) and SphP(d18:1); (h) AcCa(18:2), Cer(d36:1), LPA(18:2), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); (i) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); (j) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0), LPC(16:0e), LPC(18:0), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1); (k) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), SM(d36:2) and SphP(d18:1); (l) AcCa(18:2), Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(18:2), LPI(18:0), PC(32:2), PC(36:4), PE(36:2), PI(40:5), PS(38:4), SM(d36:2) and SphP(d18:1); (m) Cer(d36:1), Cer(d38:1), Cer(d39:1), Cer(d40:1), Cer(d41:1), Cer(d41:2), Cer(d42:2), LPC(14:0), LPC(16:0e), LPI(18:0), PC(32:2), PC(36:4), PI(40:5) and SM(d36:2); (n) Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) and SM(d36:2); (o) AcCa(18:2), Cer(d36:1), LPC(14:0), LPC(16:0e), LPC(18:2), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), PE(36:2), PS(38:4), SM(d36:2) and SphP(d18:1); or (p) Cer(d36:1), LPC(14:0), LPC(16:0e), PC(32:2), PC(36:4) (e.g., PC(18:2/18:2)), SM(d36:2) and SphP(d18:1); or a fragment, variant or derivative thereof.
37. The method, system or kit of any one of the preceding claims, wherein the biological sample is or comprises a blood sample, a plasma sample, a serum sample and/or an extracellular vesicle (EV) sample.
38. The system or kit of any one of Claims 24 to 37, for use in the method of any one of 1 to 20.
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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023215941A1 (en) * 2022-05-10 2023-11-16 BCAL Diagnostics Limited Diagnostic signature

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023215941A1 (en) * 2022-05-10 2023-11-16 BCAL Diagnostics Limited Diagnostic signature

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
CÍFKOVÁ EVA; HOLčAPEK MICHAL; LÍSA MIROSLAV; VRÁNA DAVID; GATěK JIřÍ; MELICHAR B: "Determination of lipidomic differences between human breast cancer and surrounding normal tissues using HILIC-HPLC/ESI-MS and multivariate data analysis", ANALYTICAL AND BIOANALYTICAL CHEMISTRY, SPRINGER BERLIN HEIDELBERG, BERLIN/HEIDELBERG, vol. 407, no. 3, 29 October 2014 (2014-10-29), Berlin/Heidelberg, pages 991 - 1002, XP035434159, ISSN: 1618-2642, DOI: 10.1007/s00216-014-8272-z *
DÍAZ-BELTRÁN LETICIA, GONZÁLEZ-OLMEDO CARMEN, LUQUE-CARO NATALIA, DÍAZ CARIDAD, MARTÍN-BLÁZQUEZ ARIADNA, FERNÁNDEZ-NAVARRO MÓNICA,: "Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer", CANCERS, MDPI AG, CH, vol. 13, no. 1, CH , pages 147, XP093311252, ISSN: 2072-6694, DOI: 10.3390/cancers13010147 *
EGHLIMI RYAN, SHI XIAOJIAN, HROVAT JONATHAN, XI BOWEI, GU HAIWEI: "Triple Negative Breast Cancer Detection Using LC–MS/MS Lipidomic Profiling", JOURNAL OF PROTEOME RESEARCH, AMERICAN CHEMICAL SOCIETY, vol. 19, no. 6, 5 June 2020 (2020-06-05), pages 2367 - 2378, XP093311250, ISSN: 1535-3893, DOI: 10.1021/acs.jproteome.0c00038 *
LIU LIN, KAWASHIMA MASAHIRO, SUGIMOTO MASAHIRO, SONOMURA KAZUHIRO, PU FENGLING, LI WEI, TAKEDA MASASHI, GOTO TAKAYUKI, KAWAGUCHI K: "Discovery of lipid profiles in plasma‐derived extracellular vesicles as biomarkers for breast cancer diagnosis", CANCER SCIENCE, JAPANESE CANCER ASSOCIATION, TOKYO, JP, vol. 114, no. 10, 1 October 2023 (2023-10-01), JP , pages 4020 - 4031, XP093311253, ISSN: 1347-9032, DOI: 10.1111/cas.15935 *

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