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WO2025171441A1 - Signature pronostique pour la récidive du cancer et la réactivité au cancer - Google Patents

Signature pronostique pour la récidive du cancer et la réactivité au cancer

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Publication number
WO2025171441A1
WO2025171441A1 PCT/AU2025/050117 AU2025050117W WO2025171441A1 WO 2025171441 A1 WO2025171441 A1 WO 2025171441A1 AU 2025050117 W AU2025050117 W AU 2025050117W WO 2025171441 A1 WO2025171441 A1 WO 2025171441A1
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Prior art keywords
cancer
sample
biomarker
cell
therapy
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English (en)
Inventor
Sudha RAO
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QIMR Berghofer Medical Research Institute
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Queensland Institute of Medical Research QIMR
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Priority claimed from AU2024900337A external-priority patent/AU2024900337A0/en
Application filed by Queensland Institute of Medical Research QIMR filed Critical Queensland Institute of Medical Research QIMR
Publication of WO2025171441A1 publication Critical patent/WO2025171441A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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    • 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
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • 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
    • 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
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6845—Methods of identifying protein-protein interactions in protein mixtures
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/54—Determining the risk of relapse

Definitions

  • This invention relates generally to methods of determining the likelihood of a subject with cancer responding to an anti-cancer therapy.
  • the invention also relates to methods of determining the likelihood of cancer recurrence in a subject. More particularly, the invention relates to novel response to therapy biomarkers, and cancer recurrence biomarkers, and the measurement of these biomarkers in prognostic methods.
  • Phosphatidylinositol is one of a number of phospholipids found in cell membranes which play an important role in intracellular signal transduction.
  • Cell signaling via 3’-phosphorylated phosphoinositides has been implicated in a variety of cellular processes, e.g., malignant transformation, growth factor signaling, inflammation, and immunity (Rameh et al. (1999) J. Biol. Chem., 274:8347-8350).
  • phosphoinositide 3-kinase also referred to as PI3-kinase or PI3K
  • PI3-kinase The enzyme responsible for generating these phosphorylated signaling products, phosphoinositide 3-kinase (also referred to as PI3-kinase or PI3K), was originally identified as an activity associated with viral oncoproteins and growth factor receptor tyrosine kinases that phosphorylate phosphatidylinositol (PI) and its phosphorylated derivatives at the 3’-hydroxyl of the inositol ring (Panayotou et al. (1992) Trends Cell Biol 2: 358-60).
  • PI phosphatidylinositol
  • Phosphoinositide 3-kinases are lipid kinases that phosphorylate lipids at the 3-hydroxyl residue of an inositol ring (Whitman et al. (1988) Nature, 332:664).
  • the 3-phosphorylated phospholipids (PIP3s) generated by PI3-kinases act as second messengers recruiting kinases with lipid binding domains (including pleckstrin homology (PH) regions), such as Akt and phosphoinositide-dependent kinase-1 (PDK1 ).
  • PH pleckstrin homology
  • Akt and PDK1 Binding of Akt to membrane PIP3s causes the translocation of Akt to the plasma membrane, bringing Akt into contact with PDK1 , which is responsible for activating Akt.
  • the tumour-suppressor phosphatase, PTEN dephosphorylates PIP3 and therefore acts as a negative regulator of Akt activation.
  • the PI3- kinases Akt and PDK1 are important in the regulation of many cellular processes including cell cycle regulation, proliferation, survival, apoptosis and motility and are significant components of the molecular mechanisms of diseases such as cancer, diabetes and immune inflammation (Vivanco et al. (2002) Nature Rev. Cancer 2 4-89 Phillips et al. (1998) Cancer 83:41 ).
  • the main PI3K isoform in cancer is the Class I PI3-kinase, p1 10 a (alpha) (as described in U.S. Pat. Nos. 5,824,492; 5,846,824; 6,274,327).
  • Other isoforms are implicated in cardiovascular and immune-inflammatory disease (Workman P (2004) Biochem See Trans 32:393-396; Patel et al. (2004) Proceedings of the American Association of Cancer Research (Abstract LB-247) 95th Annual Meeting, March 27-31 , Orlando, Florida, USA; Ahmadi K and Waterfield MD (2004) Encyclopedia of Biological Chemistry (Lennarz W J, Lane M D eds) Elsevier Academic Press).
  • PI3K/Akt/PTEN pathway is an attractive target for cancer drug development since such modulating or inhibitory agents would be expected to inhibit proliferation, reverse the repression of apoptosis and surmount resistance to cytotoxic agents in cancer cells (Folkes et al. (2008,) J. Med. Chem. 51 : 5522-5532; Yaguchi et al. (2006) J. Nat. Cancer Inst. 98(8):545-556).
  • the present invention is predicated in part on the discovery that a number of gene expression and protein biomarkers can be used to determine the likelihood of cancer recurrence in a subject, in addition to the likelihood that a subject with cancer would respond to an anti-cancer therapy. These methods have important clinical outcomes in prognostic methods for assessing cancer patients and determining treatment options.
  • a method of monitoring the responsiveness of a cancer to an anti-cancer treatment in a subject comprising the step of determining an expression level of one or a plurality of biomarkers in a biological sample obtained from the subject, wherein the biomarkers comprise one or more of DCN, LCN2, LOX, HM0X1, and PDCD1LG2; wherein an altered or modulated expression level of the one or plurality of markers indicates or correlates with relatively increased or decreased responsiveness of the cancer to the anti-cancer treatment.
  • the biomarkers could be measured by the presence or abundance of the expression product of DCN, LCN2, LOX, HM0X1, and/or PDCD1LG2.
  • a method of determining the likelihood of cancer recurrence in a subject comprising the step of determining a level of a cancer recurrence biomarker in a biological sample obtained from the subject, wherein the biomarker comprises the level of interaction of p850 and H3K27Me3 in a cellular compartment, and an altered or modulated level of the biomarker indicates or correlates with a relatively increased likelihood or relatively decreased likelihood of cancer recurrence.
  • a reduced level of interaction between p850 and H3K27Me3 as compared to a control or reference sample e.g., a predetermined threshold
  • the cellular compartment comprises the cell nucleus and/or the cell cytoplasm. In some preferred embodiments, the cellular compartment comprises the cell cytoplasm.
  • the biological sample is a whole blood sample or a tumour sample.
  • the invention provides a method of determining the likelihood of a cancer responding to an anti-cancer treatment, the method comprising the step of determining presence or level of one or a plurality of response to therapy biomarkers in a biological sample obtained from the subject, wherein the response to therapy biomarkers are selected from IL6, EPCAM, ABCB5, and GBP2 and an altered or modulated level of the one or a plurality of biomarkers indicates or correlates with relatively increased or decreased likelihood of the cancer responding to the anti-cancer treatment.
  • the biological sample comprises circulating tumour cells.
  • a method of determining the likelihood of a cancer responding to an anti-cancer treatment comprising the step of determining an expression level of one or a plurality of response to therapy biomarkers in a biological sample obtained from the subject, wherein the response to therapy biomarkers comprise one or both of IL6 and GBP2 and an altered or modulated expression level of the one or both markers indicates or correlates with a relatively increased or decreased likelihood of the cancer responding to the anti-cancer treatment.
  • an increased level of the response to therapy biomarkers indicates or correlates with an increased likelihood of the subject responding the anti-cancer treatment.
  • the increased level is as compared to a control or reference sample. In some of the same embodiments and some other embodiments, the increased level is as compared to a predetermined threshold.
  • the anti-cancer treatment is an anti-PD1 immunotherapy, or a PARP inhibitor therapy.
  • kits for determining an indicator used in assessing a likelihood of a subject with cancer responding to cancer therapy comprising, consisting, or consisting essentially of (a) at least one reagent that allows quantification of a polynucleotide or polypeptide expression product of the biomarkers described above and/or elsewhere herein in a biological sample; and optionally (b) instructions for using the at least one reagent; wherein the cancer therapy comprises therapy with an immune checkpoint inhibitor.
  • the kit further comprises at least one reagent that allows quantification of a polynucleotide or polypeptide expression product of DCN, LCN2, LOX, HM0X1, PDCD1LG2, IL6, EPCAM, ABCB5, and/or GBP2 in the biological sample.
  • kits for determining an indicator used in assessing a likelihood of a subject having cancer recurrence comprising, consisting, or consisting essentially of (a) at least one reagent that allows quantification of a polynucleotide or polypeptide expression product of the cancer recurrence biomarkers described above and/or elsewhere herein in a biological sample; and optionally (b) instructions for using the at least one reagent; wherein the cancer therapy comprises therapy with an immune checkpoint inhibitor.
  • the kit further comprises at least one reagent that allows quantification of a polynucleotide or polypeptide expression product of PIK3R2 'm the biological sample.
  • a solid support for determining an indicator used in assessing a likelihood of a subject with cancer (or who has had cancer) having a recurrence of the cancer, the solid support comprising, consisting, or consisting essentially of at least one first oligonucleotide primer or probe immobilized to the solid support, wherein the at least one first oligonucleotide primer or probe hybridizes to a PIK3R2 transcript or cDNA.
  • the support further comprises a PIK3R2 transcript or cDNA thereof hybridized to the at least one first oligonucleotide primer or probe.
  • the cDNA may correspond to mRNA derived from a cell or cell population (e.g., is a tumour cell or tumour cell population) .
  • compositions for determining an indicator used in assessing a likelihood of a cancer recurring in a subject comprising, consisting, or consisting essentially of tumour cells, a detection agent that binds to the polypeptide expression product of PIK3R2.
  • the detection agents are antibodies or antigen-binding fragments thereof.
  • the subject has undergone, or is undergoing, a cancer therapy (e.g., immunotherapy).
  • FIG. 1 PI3K-mTOR inhibitor treatment increases “anti-tumour” immune cell populations in 4T1 tumours.
  • Frozen tumour tissues were collected from Balb/c 4T1 mice treated +/- GDC-0084 (7.5 mg/kg) in combination with aPD1 (10 mg/kg). Tumours were subjected to spatial immune profiling and cellular phenotypes analyzed.
  • A Differential location of all cellular phenotypes in aPD1 versus GDC-0084 combination with aPD1 tumour tissues.
  • FIG. 1 PI3K-mTOR inhibitor treatment increases “anti-tumour” immune cell populations in 4T1 tumours.
  • Frozen tumour tissues were collected from Balb/c 4T1 mice treated +/- GDC-0084 (7.5 mg/kg) in combination with aPD1 (10 mg/kg). Tumours were subjected to spatial immune profiling and cellular phenotypes analyzed.
  • A Differential location of all cellular phenotypes in
  • FIG. 1 PI3K-mT0R inhibitor treatment increases B cell and T cell enriched cellular neighborhoods in 4T1 tumours.
  • A Graphical representation of immune cell populations (% of total cells) expressed within ten identified cellular neighborhoods. Red: B cells; purple: Bone marrow blast cells; Blue: CD11 b+ neutrophils; light green: CD11c+ dendritic cells; dark green: CD24+ dendritic cells; purple CD38+ myeloid cells; dark orange: endothelial cells; orange: erythrocytes; yellow: neutrophils; light brown: proliferating cells; dark brown: T cells; pink: unidentified; dark pink: vascular cells; grey: other.
  • FIG. 3 PI3K-mTOR inhibitor treatment increases the cross-talk between “anti-tumour” immune cells and bone marrow blasts. Spatial proximity analysis from aPD1 (filled columns; left-hand columns) versus GDC-0084 combination with aPD1 (open columns, right-hand columns) tumour tissues.
  • A Mean number of immune cell populations within 50 pm of bone marrow blast cells, vascular cells and endothelial cells.
  • B Mean distance (pm) of bone marrow blast cells, vascular cells and endothelial cells to nearest immune cell phenotypes.
  • FIG. 4 Down-regulation of “pro-tumour” immune cell profile following PI3K-mTOR inhibitor treatment.
  • A Nanostring nCounter cell abundance analysis of regulatory T cells (Treg), neutrophils, and mast cell immune populations from GDC-0084 with/without aPD1 treated tumours.
  • B Toluidine staining for mast cell detection in aPD1 and GDC-0084 combination with aPD1 treated tumours.
  • FIG. 5 Enhancement of “anti-tumour” immune cell profile following PI3K-mTOR inhibitor treatment.
  • A Nanostring nCounter cell abundance analysis of dendritic cell, NK, B and T cell immune populations from GDC-0084 with/without aPD1 treated tumours.
  • B Gene expression levels of cytotoxicity-related cytokines, including IFN-y (IFNy) and Granzyme B (GzmB) in GDC-0084 with/without aPD1 treated tumours.
  • C Nanostring nCounter cell abundance analysis of exhausted T cell immune populations from GDC-0084 +/- aPD1 treated tumours.
  • FIG. Induction of “anti-tumour” immune-related pathways by PI3K-mTOR inhibitor treatment.
  • FIG. 7 PI3K-mTOR inhibitor treatment increases cytotoxicity, tumour suppression, immune checkpoint and “anti-tumour” related genes. Bubble plot depicting differential expression of PI3K regulated genes in GDC-0084 without/with aPD1 treated tumours.
  • FIG. 8 Dual PI3K-mTOR inhibitors down-regulate IL-6 and increase viral mimicry gene expression in the MCF-7 resistance model.
  • A Schematic of MCF-7 resistance model.
  • B qPCR analysis of IL-6 and GBP2 mRNA expression following treatment with the PI3K inhibitors Idelalisib, LY294002, Dactolisib and GDC-0084 (expressed as % DMSO control). ***, p ⁇ 0.001 , ****, p ⁇ 0.0001 , versus DMSO control, Dunnett’s post test.
  • FIG. 9 PI3K-mTOR inhibition down-regulates IL-6 and increases viral mimicry gene expression in the MDA-MB-231 reversal maintenance model.
  • A Schematic of the MDA-MB-231 reversal maintenance model.
  • B qPCR analysis of IL-6 and GBP2 mRNA expression following treatment with the PI3K-mTOR dual inhibitor, GDC-0084 (expressed as % vehicle control). *, p ⁇ 0.05, **, p ⁇ 0.01 , versus DMSO control, Dunnett’s post test.
  • FIG. 10 IL-6/EpCAM/ABCB5 CTC biomarker predicts treatment response in melanoma patients.
  • FIG. 11 p85p:H3K27Me3 interaction biomarker predicts recurrence in triple negative breast cancer patients.
  • DUOLINK analysis was performed on PBMC isolated from TNBC patients, CH002 (non-recurrence), CH007 (non-recurrence), CH003 (recurrence) pre- and post-chemotherapy.
  • A Representative super resolution images of DUOLINK staining profiles in PBMC isolated from TNBC patient blood samples. White spots indicate p850:H3K27Me3 interactions within cell nuclei (blue).
  • B QuPath image analysis of p850:H3K27Me3 interactions within the whole cell, nucleus and cytoplasm pre- and postchemotherapy.
  • FIG. 12 Associations between PIK3R2 gene expression and survival probability amongst TNBC breast and ovarian cancer patient cohorts.
  • DMFS Distant metastasis-free survival
  • Patients were grouped into PIK3R2-high or PIK3R2-low depending on the expression levels of PIK3R2: high is above 75th percentile (solid lines) and low is below 75th percentile (dotted lines).
  • Kaplan-Meier survival curves for plotted using distant metastasis as the endpoint.
  • C Assessing PIK3R2 mRNA expression levels in triple-negative breast cancer patients who had residual tumours after treatment with neoadjuvant chemotherapy (NACT) (see, Blaye et al., 2022). All patients here had residual tumours post NACT, however those who presented with a distant relapse had higher pretreatment levels of PIK3R2 compared to those who did not present with a distant relapse.
  • NACT neoadjuvant chemotherapy
  • the “amount” or “level” of a biomarker is a detectable level in a sample. These can be measured by methods known to one skilled in the art and also disclosed herein. The expression level or amount of biomarker assessed can be used to determine the response to treatment. [0038] As used herein, “and/or” refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative (or).
  • control subject may refer to a subject known to have a particular state (e.g., responsive to a cancer therapy, or has a cancer that does not recur) (positive control), or to a subject known not have the particular state (e.g., not responsive to a cancer therapy or has a cancer that recurs) (negative control). It is understood that control subjects include data obtained and used as a standard, i.e. it can be used over and over again for multiple different subjects.
  • the data from the control sample could have been obtained in a different set of experiments, for example, it could be an average obtained from a number of healthy subjects and not actually obtained at the time the data for the subject was obtained.
  • correlated and “associated” are used interchangeably herein to refer to the association between two measurements (or measured entities).
  • the disclosure provides genetic and/or epigenetic variations, the level(s) of which are associated with disease diagnosis and/or prognosis and/or response to treatment.
  • the terms “decrease”, “reduced”, “reduction”, “inhibit”, “suppress”, “attenuate” and the like are all used herein to mean a decrease by a statistically significant amount. In some embodiments, these terms typically mean a decrease by at least 10% as compared to a reference level (e.g., the absence of a given treatment or agent) and can include, for example, a decrease by at least about 10%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 98%, at least about 99%, or more.
  • a reference level e.g., the absence of a given treatment or agent
  • “reduction”, “suppression”, and “inhibition” does not necessitate a complete inhibition or reduction as compared to a reference level. “Complete inhibition” and the like is a 100% inhibition as compared to a reference level. A decrease can be preferably down to a level accepted as within the range of normal (e.g., for an individual without a given disorder).
  • immobilized means that a molecular species of interest is fixed to a solid support, suitably by covalent linkage. This covalent linkage can be achieved by different means depending on the molecular nature of the molecular species. Moreover, the molecular species may be also fixed on the solid support by electrostatic forces, hydrophobic or hydrophilic interactions or Van-der-Waals forces. The above described physicochemical interactions typically occur in interactions between molecules.
  • the molecules remain immobilized or attached to a support under conditions in which it is intended to use the support, for example in applications requiring nucleic acid amplification and/or sequencing or in in antibody-binding assays.
  • oligonucleotides or primers are immobilized such that a 3' end is available for enzymatic extension and/or at least a portion of the sequence is capable of hybridizing to a complementary sequence.
  • immobilization can occur via hybridization to a surface attached primer, in which case the immobilized primer or oligonucleotide may be in the 3'-5' orientation.
  • immobilization can occur by means other than basepairing hybridization, such as the covalent attachment.
  • the terms “increased”, “increase”, “enhance”, or “activate” are all used herein to mean an increase by a statistically significant amount.
  • the terms “increased”, “increase”, “enhance”, or “activate” can mean an increase of at least 10% as compared to a reference level (e.g., the absence of a given treatment or agent) and can include, for example, of at least about 10% as compared to a reference level, for example an increase of at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 98%, at least about 99%, or up to and including a 100% increase or any increase between 10-100% as compared to a reference level or at least about
  • label refers to any atom or molecule that can be used to provide a detectable and/or quantifiable signal.
  • the label can be attached, directly or indirectly, to a nucleic acid or protein.
  • Suitable labels that can be attached include, but are not limited to, radioisotopes, fluorophores, quenchers, chromophores, mass labels, electron dense particles, magnetic particles, spin labels, molecules that emit chemiluminescence, electrochemically active molecules, enzymes, cofactors, and enzyme substrates.
  • a label can include an atom or molecule capable of producing a visually detectable signal when reacted with an enzyme.
  • the label is a “direct” label which is capable of spontaneously producing a detectible signal without the addition of ancillary reagents and is detected by visual means without the aid of instruments.
  • colloidal gold particles can be used as the label.
  • the label is other than a naturally-occurring nucleoside.
  • label also refers to an agent that has been artificially added, linked or attached via chemical manipulation to a molecule.
  • level with reference to a biomarker refers to the amount or concentration of the biomarker in a sample.
  • the amount or concentration may be absolute or may be relative, and can be determined using any method known in the art.
  • the term “likelihood” is used as a measure of whether subjects with a particular biomarker profile actually have a particular state (e.g., a cancer that is likely to recur) based on a given mathematical model.
  • An increased likelihood for example may be relative or absolute and may be expressed qualitatively or quantitatively.
  • an increased risk may be expressed as simply determining the subject’s level of a given biomarker and placing the test subject in an “increased risk” category, based upon previous population studies.
  • a numerical expression of the test subject’s increased risk may be determined based upon biomarker level analysis.
  • Measurement means assessing the presence, absence, quantity or amount (which can be an effective amount) of a given substance within a sample, including the derivation of qualitative or quantitative concentration levels of such substances, or otherwise evaluating the values or categorization of a subject's clinical parameters.
  • the term “assaying,” “detecting” or “detection” may be used to refer to all measuring or measurement as described in this specification.
  • samples so obtained include, for example, nucleic acid extracts or polypeptide extracts isolated or derived from a particular source.
  • the extract may be isolated directly from a biological fluid or tissue of a subject.
  • overexpress As used herein, the terms “overexpress,” “overexpression,” “overexpressing” or “overexpressed” interchangeably refer to a gene (e.g., PI3KCA gene) that is transcribed or translated at a detectably greater level, usually in a cancer ceil, in comparison to a normal cell.
  • Overexpression therefore, refers to both overexpression of protein and RNA (due to increased transcription, post transcriptional processing, translation, pesttranslationai processing, altered stability and altered protein degradation), as well as local overexpression due to altered protein traffic patterns and augmented functional activity, for example, as in an increased enzyme hydrolysis of substrate.
  • Overexpression can also be by 0%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or more in comparison to a normal cell or comparison ceil (e.g., a breast cell).
  • PI3K inhibitor and grammatical variants thereof are used herein to refer to a molecule that decreases or inhibits at least one function or biological activity of PI3K.
  • PI3K inhibitors may inhibit or reduce the enzymatic activity of PI3K and/or may inhibit or reduce the expression of PI3K.
  • the term “positive response” means that the result of a treatment regimen includes some clinically significant benefit, such as the prevention, or reduction of severity, of symptoms, or a slowing of the progression of the condition. For example, a reduction in tumour size or tumour burden, or a slowing in the rate of tumour growth or spread (i.e., metastasizing), can indicate a positive response.
  • the term “negative response” or “non-response” means that a treatment regimen provides no or minimal clinically significant benefit, such as the prevention, or reduction of severity, of symptoms, or increases the rate of progression of the condition.
  • the Response Evaluation Criteria in Solid Tumors is used to assess positive or negative response to therapy (Eisenhauer et at. (2009) EurJ Cancer. 45: 228-47; and http://www. irrecist.com/recist/).
  • a positive response may include a “partial response” or “complete response”, such as defined by RECIST 1 .1 , while a negative response may be equivalent to “stable disease”.
  • a predetermined threshold refers to a value, above or below which, indicates a particular state, such as a subject that is likely (or unlikely) to respond to a cancer therapy, or a cancer that is likely (or unlikely) to recur.
  • a predetermined threshold may represent the level of a biomarker in a sample from an appropriate control subject, such as a subject known to respond to a cancer therapy, or in pooled samples from multiple control subjects or medians or averages of multiple control subjects.
  • a level above or below the threshold indicates whether a subject is likely to respond to a cancer therapy, as taught herein.
  • a predetermined threshold may represent a value larger or smaller than the level determined for a control subject so as to incorporate a further degree of confidence that a level above or below the predetermined threshold is indicative of the presence of the particular state.
  • the predetermined threshold may represent the average or median level of a biomarker in a group of control subjects, plus or minus 1 , 2, 3 or more standard deviations. Those skilled in the art can readily determine an appropriate predetermined threshold based on analysis of biological samples from appropriate control subjects.
  • solid support refers to a solid inert surface or body to which a molecular species, such as a nucleic acid and polypeptides can be immobilized.
  • solid supports include glass surfaces, plastic surfaces, latex, dextran, polystyrene surfaces, polypropylene surfaces, polyacrylamide gels, gold surfaces, and silicon wafers.
  • the solid supports are in the form of membranes, chips or particles.
  • the solid support may be a glass surface (e.g., a planar surface of a flow cell channel).
  • the solid support may comprise an inert substrate or matrix which has been “functionalized”, such as by applying a layer or coating of an intermediate material comprising reactive groups which permit covalent attachment to molecules such as polynucleotides.
  • such supports can include polyacrylamide hydrogels supported on an inert substrate such as glass.
  • the molecules e.g., polynucleotides
  • the intermediate material e.g., a hydrogel
  • the intermediate material can itself be non-covalently attached to the substrate or matrix (e.g., a glass substrate).
  • the support can include a plurality of particles or beads each having a different attached molecular species.
  • a “subject” means a human or animal. Usually the animal is a vertebrate such as a primate, rodent, domestic animal or game animal. Primates include chimpanzees, cynomolgus monkeys, spider monkeys, and macaques (e.g., Rhesus). Rodents include mice, rats, woodchucks, ferrets, rabbits, and hamsters.
  • Domestic and game animals include cows, horses, pigs, deer, bison, buffalo, feline species (e.g., domestic cat), canine species (e.g., dog, fox, wolf), avian species (e.g., chicken, emu, ostrich), and fish (e.g., trout, catfish, and salmon).
  • the subject is a mammal (e.g., a primate (e.g., a human)).
  • a primate e.g., a human
  • subject are used interchangeably herein.
  • treatment includes not just the improvement of symptoms or markers, but also a cessation of, or at least slowing of, progress or worsening of symptoms compared to what would be expected in the absence of treatment.
  • Beneficial or desired clinical results include, but are not limited to, alleviation of one or more symptom(s), diminishment of extent of disease, stabilized (i.e., not worsening) state of disease, delay or slowing of disease progression, amelioration, or palliation of the disease state, remission (whether partial or total), and/or decreased mortality, whether detectable or undetectable.
  • treatment also includes providing relief from the symptoms or side-effects of the disease (including palliative treatment).
  • a treatment need not cure a disorder (i.e., complete reversal or absence of disease) to be considered effective.
  • tumor refers to any neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues.
  • cancer and “cancerous” referto or describe the physiological condition in mammals that is typically characterized in part by unregulated cell growth.
  • cancer refers to non-metastatic and metastatic cancers, including early stage and late stage cancers.
  • precancerous refers to a condition or a growth that typically precedes or develops into a cancer.
  • non-metastatic refers to a cancer that is benign or that remains at the primary site and has not penetrated into the lymphatic or blood vessel system or to tissues other than the primary site.
  • a non -metastatic cancer is any cancer that is a stage 0, I or II cancer.
  • head stage cancer is meant a cancerthat is not invasive or metastatic or is classified as a stage 0, I or II cancer.
  • late stage cancer generally refers to a stage III or IV cancer, but can also refer to a stage II cancer or a sub stage of a stage II cancer.
  • stage II cancer is benign or that remains at the primary site and has not penetrated into the lymphatic or blood vessel system or to tissues other than the primary site.
  • a non -metastatic cancer is any cancer that is a stage 0, I or II cancer.
  • middle stage cancer is meant a cancerthat is not invasive or metastatic or is classified as a stage 0, I or II cancer.
  • late stage cancer generally refers to a stage III or IV cancer, but can also refer to a
  • cancer examples include, but are not limited to, breast cancer, prostate cancer, ovarian cancer, cervical cancer, pancreatic cancer, colorectal cancer, lung cancer, hepatocellular cancer, gastric cancer, liver cancer, bladder cancer, cancer of the urinary tract, thyroid cancer, renal cancer, carcinoma, melanoma, brain cancer, non-small cell lung cancer, squamous cell cancer of the head and neck, endometrial cancer, multiple myeloma, mesothelioma, rectal cancer and esophageal cancer.
  • the cancer is breast cancer.
  • the present invention discloses that the association between phosphatidylinositol 3-kinase (PI3K) 85 kDa regulatory subunit beta (p850) and histone 3, trimethylated at lysine 27 (H3K27Me3) can be measured as a biomarker to indicate the likelihood of cancer recurrence in as subject.
  • the biomarker described herein as being useful for indicating the likelihood of cancer recurrence is the association (i.e., the proximity) between p850 and H3K27Me3.
  • the present invention provides a method of determining the likelihood of cancer recurrence in a subject, the method comprising the step of determining a level (i.e., proximity) of at least one biomarker in a biological sample obtained from the subject, wherein the biomarker comprises p85p:H3K27Me3, and an altered or modulated level of the biomarker indicates or correlates with relatively increased or likelihood of cancer recurrence.
  • the level of the biomarker is measured by determining the cellular compartment of PI3K p85p and histone 3, as described in more detail below and/or elsewhere herein.
  • the wild-type human p850 amino acid sequence has a UniProt accession no. 000459, and provided below: MAGPEGFQYRALYPFRRERPEDLELLPGDVLVVSRAALQALGVAEGGERCPQSVGW MPGLNERTRQRGDFPGTYVEFLGPVALARPGPRPRGPRPLPARPRDGAPEPGLTLP DLPEQFSPPDVAPPLLVKLVEAIERTGLDSESHYRPELPAPRTDWSLSDVDQWDTAAL ADGIKSFLLALPAPLVTPEASAEARRALREAAGPVGPALEPPTLPLHRALTLRFLLQHL GRVASRAPALGPAVRALGATFGPLLLRAPPPPSSPPPGGAPDGSEPSPDFPALLVEK LLQEHLEEQEVAPPALPPKPPKAKPASTVLANGGSPPSLQDAEWYWGDISREEVNEK LRDTPDGTFLVRDASSKIQGEYTLTLRKGGNNKLIKVFHRDGHYGFSEPLTFCSVVDLI NHYRHESLAQYNAKLD
  • Histone 3 is a DNA packing protein.
  • the tri-methylation is known to be associated with the downregulation of nearby genes via the formation of heterochromatic regions.
  • the wild-type histone H3 protein has an UniProt accession no. P68431 , and an amino acid sequence as set out, below:
  • Biological samples that can be used with the present invention include cancer cells.
  • Cancer cells for the practice of the present invention can be obtained from any suitable cancer-cell containing patient sample, illustrative examples of which include tumour biopsies, circulating tumour cells (CTCs), primary cell cultures, or cell lines derived from tumours or exhibiting tumour-like properties, as well as preserved tumour samples, such as formalin-fixed, paraffin-embedded tumour samples, or frozen tumour samples.
  • the sample is obtained prior to treatment with a therapy.
  • the sample is obtained after treatment with a therapy.
  • the sample comprises a tumour tissue sample, which can be formalin-fixed and paraffin-embedded, archival, fresh or frozen.
  • the whole blood comprises immune cells, circulating tumor cells, and any combinations thereof.
  • Presence and/or levels/amount of a biomarker can be determined qualitatively and/or quantitatively based on any suitable criterion known in the art.
  • presence and/or expression levels/amount of a biomarker in a first sample is increased or elevated as compared to presence/absence and/or expression levels/amount in a second sample (e.g., before treatment with a therapy).
  • presence/absence and/or levels/amount of a biomarker in a first sample is decreased or reduced as compared to presence and/or levels/amount in a second sample.
  • the second sample is a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue. Additional disclosures for determining presence/absence and/or levels/amount of an association biomarker are described herein.
  • an elevated level refers to an overall increase of about any of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or greater, in the level of biomarker, as detected by standard art known methods such as those described herein, as compared to a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue.
  • an elevated level refers to the increase in level/amount of a biomarker in the sample wherein the increase is at least about any of 1 .5x, 1 ,75x, 2x, 3x, 4x, 5x, 6x, 7x, 8x, 9x, 10x, 25x, 50x, 75x, or 100x the level/amount of the respective biomarker in a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue.
  • an elevated level refers to an overall increase of greater than about 1 .5-fold, about 1 .75-fold, about 2-fold, about 2.25-fold, about 2.5-fold, about 2.75-fold, about 3-fold, or about 3.25-fold as compared to a reference sample, reference cell, reference tissue, control sample, control cell, control tissue, or internal control.
  • a reduced level refers to an overall reduction of about any of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or greater, in the level of biomarker, detected by standard art known methods such as those described herein, as compared to a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue.
  • reduced level refers to a decrease in level/amount of a biomarker in the sample wherein the decrease is at least about any of 0.9x, 0.8x, 0.7x, 0.6x, 0.5x, 0.4x, 0.3x, 0.2x, 0.1 x, 0.05x, or 0.01 x the level/amount of the respective biomarker in a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue.
  • the predictive methods suitably comprise: (i) obtaining a sample from a subject, wherein the sample comprises a cancer cell (e.g., a CTC); (ii) contacting the sample with a first antigen-binding molecule that binds to p850 in the sample and a second antigen-binding molecule that binds to H3K27Me3 in the sample; and (Hi) detecting localization of the first and second antigen-binding molecule(s) in the nucleus of the cancer cell, wherein localization of the first and second antigen binding molecules in the nucleus of the cancer cell is indicative that the cancer cell has reduced likelihood of recurrence.
  • a cancer cell e.g., a CTC
  • Assessment of the localization of p850 and H3K27Me3 in the cytoplasm and/or the nucleus of a cancer cell may be performed using any suitable localization identification technique, e.g., by immunohistochemistry (IHC), typically using an p850 antibody that has a different detectable moiety or label than a H3K27Me3 partner antibody.
  • IHC immunohistochemistry
  • a number of localization assays are known in the art, for example, chemiluminescence assays, FRET, sequential CHIP assays, co-immunoprecipitaion, ELISAs, etc.
  • spatial proximity assays also referred to as “proximity assays” are employed, which can be used to assess the formation of a complex between p850 and H3K27Me3.
  • Proximity assays rely on the principle of “proximity probing”, wherein an analyte, typically an antigen, is detected by the coincident binding of multiple (i.e., two or more, generally two, three or four) binding agents or probes, which when brought into proximity by binding to the analyte (hence “proximity probes”) allow a signal to be generated.
  • At least one of the proximity probes comprises a nucleic acid domain (or moiety) linked to the analyte-binding domain (or moiety) of the probe, and generation of the signal involves an interaction between the nucleic acid moieties and/or a further functional moiety which is carried by the other probe(s).
  • signal generation is dependent on an interaction between the probes (more particularly by the nucleic acid or other functional moieties/domains carried by them) and hence only occurs when both the necessary two (or more) probes have bound to the analyte, thereby lending improved specificity to the detection system.
  • Proximity assays are typically used to assess whether two particular proteins or portions thereof are in close proximity, e.g., proteins that are bound to each other, fusion proteins, and/or proteins that are positioned in close proximity.
  • One such assay known as proximity ligation assay (PLA)
  • PLA proximity ligation assay
  • PLA probes which are species-specific secondary antibodies with a unique oligonucleotide strand attached, are then bound to the appropriate primary antibodies.
  • the oligonucleotide strands of the PLA probes can interact with additional ssDNA and DNA ligase such they can be circulated and amplified via rolling circle amplification (RCA).
  • RCA rolling circle amplification
  • highly processive DNA polymerases such as Phi 29 DNA polymerase is used, the circular DNA template can be replicated hundreds to thousands of times longer and as a result producing ssDNA molecules from hundreds of nanometers to microns in length (see, Angewandte Chemie International Edition, 2008, 47, 6330-6337).
  • the replicated DNA can be detected via detection systems.
  • a visible signal is indicative that the targets of interest are in close proximity.
  • DB dual binders
  • the dual binders comprise Fab fragments with fast off-rate kinetics
  • the dual binders are washed off if only one of the Fab fragments is bound to its epitope (simultaneous cooperative binding of both Fab fragments of the dual binder prevents dissociation of the dual binder and leads to positive staining/visibility).
  • proximity assays and tools employ a biotin ligase substrate and an enzyme to perform a proximity assay.
  • the method provides detection of target molecules and proximity while maintaining the cellular context of the sample.
  • biotin ligase such as an enzyme from E. co// and peptide substrate such as amino-acid substrate for that enzyme provides for a sensitive and specific detection of protein-protein interactions in FFPE samples.
  • biotin ligase can efficiently biotinylate appropriate peptide substrate in the presence of biotin and the reaction can only occur when the enzyme makes physical contact with the peptide substrate, biotin ligase and the substrate can be separately conjugated to two antibodies that recognize targets of interest respectively.
  • Cancer recurrence biomarkers of the present disclosure include PIK3R2, and more particularly the expression products of on PIK3R2.
  • the on PIK3R2 gene encodes the P850 subunit of phosphatidylinositol 3-kinase (PI3K), which (as described above) regulates the activity of the PI3K enzyme.
  • PI3K phosphatidylinositol 3-kinase
  • PIK3R2 can be used as a cancer recurrence biomarker to predict whether a cancer in a subject is likely to recur.
  • Expression products of PIK3R2, including polynucleotide (e.g., mRNA) and polypeptide expression products of PIK3R2 correlate with the recurrence of cancer therapy and can be assessed so as to predict whether a cancer is likely to recur in the subject.
  • the PIK3R2 biomarker may be used either by itself or in combination with one or more other cancer therapy biomarkers for the determination of the indicator for assessing the likelihood of a subject responding to cancer therapy.
  • a method for determining an indicator used in assessing a likelihood of a cancer recurring in a subject comprising, consisting or consisting essentially of:
  • the expression product of PIK3R2 is a polynucleotide and the biomarker value for PIK3R2, is indicative of the abundance or concentration of the polynucleotide in the sample.
  • the polynucleotide expression product comprises a nucleotide sequence having at least 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91 %, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% sequence identity with the sequence set forth in SEQ ID NO: 3, or a complement thereof.
  • the expression product of PIK3R2 is a polypeptide and the biomarker value for PIK3R2 is indicative of the abundance or concentration of the polypeptide in the sample.
  • the polypeptide expression product comprises an amino acid sequence having at least 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% sequence identity with the sequence set forth in SEQ ID NO: 1 .
  • the abundance or concentration of the polynucleotide or polypeptide expression product of PIK3R2 is about the same as the abundance or concentration that correlates with a cancer that known to recur, and the indicator is thereby determined to be at least partially indicative of cancer that is likely to recur.
  • the abundance or concentration of the polynucleotide or polypeptide expression product of PIK3R2 is decreased relative the abundance or concentration that correlates with a cancer that is known to recur, and the indicator is thereby determined to be at least partially indicative of a cancer that is not likely to recur.
  • the abundance or concentration of the polynucleotide or polypeptide expression product of PIK3R2 is increased relative to the abundance or concentration that correlates with a cancer that is known to recur, and the indicator is thereby determined to be at least partially indicative of a cancer that is known to recur.
  • the abundance or concentration of the polynucleotide or polypeptide expression product of PIK3R2 is about the same as the abundance or concentration that correlates with a positive response to cancer therapy, and the indicator is thereby determined to be at least partially indicative of a positive response to therapy.
  • the sample is a clinical sample.
  • the sample is obtained from a primary or metastatic tumour. Tissue biopsy is often used to obtain a representative piece of tumour tissue.
  • tumour cells can be obtained indirectly in the form of tissues or fluids that are known or thought to contain the tumour cells of interest.
  • samples of lung cancer lesions may be obtained by resection, bronchoscopy, fine needle aspiration, bronchial brushings, or from sputum, pleural fluid or blood.
  • Genes or gene products can be detected from cancer or tumour tissue or from other body samples such as urine, sputum, serum or plasma. The same techniques discussed above for detection of target genes or gene products in cancerous samples can be applied to other body samples.
  • a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue is a single sample or combined multiple samples from the same subject or individual that are obtained at one or more different time points than when the test sample is obtained.
  • a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue is obtained at an earlier time point from the same subject or individual than when the test sample is obtained.
  • Such reference sample, reference cell, reference tissue, control sample, control cell, or control tissue may be useful if the reference sample is obtained during initial diagnosis of cancer and the test sample is later obtained when the cancer becomes metastatic.
  • a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue is a combination of multiple samples from one or more healthy individuals who are not the subject or individual.
  • a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue is a combination of multiple samples from one or more individuals with a disease or disorder (e.g., cancer) who are not the subject or individual.
  • a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue is pooled RNA samples from normal tissues or pooled plasma or serum samples from one or more individuals who are not the subject or individual.
  • the sample is a tissue sample from the individual.
  • the tissue sample is a tumour tissue sample (e.g., biopsy tissue).
  • the tissue sample is lung tissue.
  • the tissue sample is renal tissue.
  • the tissue sample is skin tissue.
  • the tissue sample is pancreatic tissue.
  • the tissue sample is gastric tissue.
  • the tissue sample is bladder tissue.
  • the tissue sample is esophageal tissue.
  • the tissue sample is mesothelial tissue.
  • the tissue sample is breast tissue.
  • the tissue sample is thyroid tissue.
  • the tissue sample is colorectal tissue.
  • the tissue sample is head and neck tissue. In some embodiments, the tissue sample is osteosarcoma tissue. In some embodiments, the tissue sample is prostate tissue. In some embodiments, the tissue sample is ovarian tissue, HCC (liver), blood cells, lymph nodes, and/or bone/bone marrow tissue. In some embodiments, the tissue sample is colon tissue. In some embodiments, the tissue sample is endometrial tissue. In some embodiments, the tissue sample is brain tissue (e.g., glioblastoma, neuroblastoma, and so forth).
  • a tumour tissue sample may encompass part or all of the tumour area occupied by tumour cells.
  • a tumour or tumour sample may further encompass tumour area occupied by tumour associated intratumoral cells and/or tumour associated stroma (e.g., contiguous peri-tumoral desmoplastic stroma).
  • tumour associated intratumoral cells and/or tumour associated stroma may include areas of immune infiltrates immediately adjacent to and/or contiguous with the main tumour mass.
  • tumour cell staining is expressed as the percentage of all tumour cells showing staining (e.g., membranous, cytoplasmic or nuclear staining) of any intensity.
  • Infiltrating immune cell staining may be expressed as the percentage of the total tumour area occupied by immune cells that show staining of any intensity.
  • the total tumour area encompasses the malignant cells as well as tumour-associated stroma, including areas of immune infiltrates immediately adjacent to and contiguous with the main tumour mass.
  • infiltrating immune cell staining may be expressed as the percent of all tumour infiltrating immune cells.
  • the tumour is a malignant cancerous tumour (i.e., cancer).
  • the tumour and/or cancer is a solid tumour or a non-solid or soft tissue tumour.
  • soft tissue tumours include leukemia (e.g., chronic myelogenous leukemia, acute myelogenous leukemia, adult acute lymphoblastic leukemia, acute myelogenous leukemia, mature B-cell acute lymphoblastic leukemia, chronic lymphocytic leukemia, prolymphocytic leukemia, or hairy cell leukemia) or lymphoma (e.g., non-Hodgkin’s lymphoma, cutaneous T-cell lymphoma, or Hodgkin’s disease).
  • leukemia e.g., chronic myelogenous leukemia, acute myelogenous leukemia, adult acute lymphoblastic leukemia, acute myelogenous leukemia, mature B-cell acute lymphoblastic leukemia, chronic lymphocytic leuk
  • a solid tumour includes any cancer of body tissues other than blood, bone marrow, or the lymphatic system. Solid tumours can be further divided into those of epithelial cell origin and those of non-epithelial cell origin. Examples of epithelial cell solid tumours include tumours of the gastrointestinal tract, colon, colorectal (e.g., basaloid colorectal carcinoma), breast, prostate, lung, kidney, liver, pancreas, ovary (e.g.
  • the cancer is non-small cell lung cancer (NSCLC).
  • the cancer is a second-line or third-line locally advanced or metastatic non-small cell lung cancer.
  • the cancer is adenocarcinoma. In some embodiments, the cancer is a squamous cell carcinoma. In some embodiments, the cancer is non-small cell lung cancer (NSCLC), glioblastoma, neuroblastoma, melanoma, breast carcinoma (e.g., triple-negative breast cancer), gastric cancer, colorectal cancer (CRC), or hepatocellular carcinoma. In some embodiments, the cancer is a primary tumour. In some embodiments, the cancer is a metastatic tumour at a second site derived from any of the above types of cancer.
  • biomarkers described herein as being useful for indicating the likelihood of a cancer responding to an anti-cancer therapy are IL-6, EpCAM, ABCB5, and GBP2.
  • the present invention provides a method of determining the likelihood of a cancer responding to an anticancer therapy, the method comprising the step of determining an expression level of one or a plurality of biomarkers in a biological sample obtained from the subject, wherein the biomarkers comprise one or more of IL-6, EpCAM, ABCB5, and GBP2, and an altered or modulated expression level of the one or the one or a plurality of markers indicates or correlates with relatively increased or decreased likelihood of the cancer responding to the anti-cancer treatment.
  • RNA or protein biomarkers quantification of RNA or protein biomarkers from raw sample (e.g., biological fluid such as blood, serum, etc.) without or with limited processing .
  • raw sample e.g., biological fluid such as blood, serum, etc.
  • whole cells or tissue sections are isolated and analysed for cancer recurrence and/or cancer therapy biomarker expression, such as using immunohistochemistry (IHC) or flow cytometry.
  • IHC immunohistochemistry
  • flow cytometry flow cytometry
  • Methods may comprise steps of homogenizing a sample in a suitable buffer, removal of contaminants and/or assay inhibitors, adding a cancer recurrence and/or cancer therapy biomarker capture reagent (e.g., a magnetic bead to which is linked an oligonucleotide complementary to a target cancer recurrence and/or cancer therapy biomarker), incubated under conditions that promote the association (e.g., by hybridization) of the target biomarker with the capture reagent to produce a target biomarker: capture reagent complex, incubating the target biomarker: capture complex under target biomarker-release conditions.
  • a cancer recurrence and/or cancer therapy biomarker capture reagent e.g., a magnetic bead to which is linked an oligonucleotide complementary to a target cancer recurrence and/or cancer therapy biomarker
  • multiple cancer recurrence and/or cancer therapy biomarkers are isolated in each round of isolation by adding multiple cancer recurrence and/or cancer therapy biomarker capture reagents (e.g., specific to the desired biomarkers) to the solution.
  • multiple cancer therapy biomarker capture reagents each comprising an oligonucleotide specific for a different cancer recurrence and/or cancer therapy biomarker can be added to the sample for isolation of multiple cancer recurrence and/or cancer therapy biomarkers.
  • the methods encompass multiple experimental designs that vary both in the number of capture steps and in the number of target cancer recurrence and/or cancer therapy biomarkers captured in each capture step.
  • an avidin target capture reagent may be used to isolate and purify targets comprising a biotin moiety
  • an antibody may be used to isolate and purify targets comprising the appropriate antigen or epitope
  • an oligonucleotide may be used to isolate and purify a complementary polynucleotide.
  • nucleic acids including single-stranded and double-stranded nucleic acids, that are capable of binding, or specifically binding, to a target cancer recurrence and/or cancer therapy biomarker can be used as the capture reagent.
  • nucleic acids include DNA, RNA, aptamers, peptide nucleic acids, and other modifications to the sugar, phosphate, or nucleoside base.
  • cancer recurrence and/or therapy biomarker capture reagents may comprise a functionality to localize, concentrate, aggregate, etc. the capture reagent and thus provide a way to isolate and purify the target cancer therapy biomarker when captured (e.g., bound, hybridized, etc.) to the capture reagent (e.g., when a target: capture reagent complex is formed).
  • the portion of the capture reagent that interacts with the cancer recurrence and/or cancer therapy biomarker is linked to a solid support (e.g., a bead, surface, resin, column, and the like) that allows manipulation by the user on a macroscopic scale.
  • a solid support e.g., a bead, surface, resin, column, and the like
  • the solid support allows the use of a mechanical means to isolate and purify the target: capture reagent complex from a heterogeneous solution.
  • separation is achieved by removing the bead from the heterogeneous solution, e.g., by physical movement.
  • the bead is magnetic or paramagnetic
  • a magnetic field is used to achieve physical separation of the capture reagent (and thus the target cancer therapy biomarker) from the heterogeneous solution.
  • the cancer recurrence or cancer therapy biomarkers may be quantified or detected using any suitable technique.
  • the cancer therapy biomarkers are quantified using reagents that determine the level, abundance or amount of individual cancer recurrence and/or cancer therapy biomarkers, either as isolated biomarker or as expressed in or on a cell.
  • Non-limiting reagents of this type include reagents for use in nucleic acid- and protein-based assays.
  • Cancer cells for the practice of the present invention can be obtained from any suitable cancer-cell containing patient samples, illustrative examples of which include tumour biopsies, circulating tumour cells (CTCs), primary cell cultures, or cell lines derived from tumours or exhibiting tumour-like properties, as well as preserved tumour samples, such as formalin-fixed, paraffin-embedded tumour samples, or frozen tumour samples.
  • the sample is obtained prior to treatment with a therapy.
  • the sample is obtained after treatment with a therapy.
  • the sample comprises a tumour tissue sample, which can be formalin fixed and paraffin embedded, archival, fresh or frozen.
  • the sample is -.
  • the whole blood comprises immune cells, circulating tumor cells, and any combinations thereof.
  • Presence and/or levels/amount of a biomarker can be determined qualitatively and/or quantitatively based on any suitable criterion known in the art, including but not limited to proteins and protein fragments.
  • presence and/or expression levels/amount of a biomarker in a first sample is increased or elevated as compared to presence/absence and/or expression levels/amount in a second sample (e.g., before treatment with a therapy).
  • presence/absence and/or levels/amount of a biomarker in a first sample is decreased or reduced as compared to presence and/or levels/amount in a second sample.
  • the second sample is a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue. Additional disclosures for determining presence/absence and/or levels/amount of a gene are described herein.
  • an elevated level refers to the increase in level/amount of a biomarker in the sample wherein the increase is at least about any of 1 .5x, 1 ,75x, 2x, 3x, 4x, 5x, 6x, 7x, 8x, 9x, 10x, 25x, 50x, 75x, or 100x the level/amount of the respective biomarker in a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue.
  • an elevated level refers to an overall increase of greater than about 1 .5-fold, about 1 .75-fold, about 2-fold, about 2.25-fold, about 2.5-fold, about 2.75-fold, about 3.0-fold, or about 3.25-fold as compared to a reference sample, reference cell, reference tissue, control sample, control cell, control tissue, or internal control (e.g., housekeeping gene).
  • a reduced level refers to an overall reduction of about any of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or greater, in the level of biomarker (e.g., protein or nucleic acid (e.g., gene or mRNA)), detected by standard art known methods such as those described herein, as compared to a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue.
  • biomarker e.g., protein or nucleic acid (e.g., gene or mRNA)
  • reduced level refers to a decrease in level/amount of a biomarker in the sample wherein the decrease is at least about any of 0.9x, 0.8x, 0.7x, 0.6x, 0.5x, 0.4x, 0.3x, 0.2x, 0.1 x, 0.05x, or 0.01 x the level/amount of the respective biomarker in a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue.
  • Presence and/or level/amount of various biomarkers in a sample can be analyzed by a number of methodologies, many of which are known in the art and understood by the skilled artisan, including, but not limited to, immunohistochemistry (“IHC”), Western blot analysis, immunoprecipitation, molecular binding assays, ELISA, ELIFA, fluorescence activated cell sorting (“FACS”), MassARRAY, proteomics, quantitative blood based assays (as for example serum ELISA), biochemical enzymatic activity assays, in situ hybridization, Southern analysis, Northern analysis, whole genome sequencing, polymerase chain reaction (“PCR”) including quantitative real time PCR (“qRT-PCR”) and other amplification type detection methods, such as, for example, branched DNA, SISBA, TMA and the like), RNA-Seq, FISH, microarray analysis, gene expression profiling, and/or serial analysis of gene expression (“SAGE”), as well as any one of the wide variety of assays that can
  • presence and/or level/amount of a biomarker is determined using a method comprising: (a) performing gene expression profiling, PCR (such as RT-PCR or qRT-PCR), RNA-seq, microarray analysis, SAGE, MassARRAY technique, or FISH on a sample (such as a subject cancer sample); and (b) determining presence and/or expression level/amount of a biomarker in the sample.
  • PCR such as RT-PCR or qRT-PCR
  • RNA-seq RNA-seq
  • microarray analysis e.g., SAGE, MassARRAY technique, or FISH
  • the presence and/or expression level/amount of biomarker proteins in a sample is examined using IHC and staining protocols. IHC staining of tissue sections has been shown to be a reliable method of determining or detecting presence of proteins in a sample.
  • the level of a response to therapy biomarker e.g., IL-6, EpCAM, ABCB5, and/or GBP2
  • IHC IHC staining protocol
  • the level of a response to therapy biomarker (e.g., IL-6, EpCAM, ABCB5, and/or GBP2) in a sample from an individual is an elevated level and, in further embodiments, is determined using IHC.
  • tumour or tumour sample may encompass part or all of the tumour area occupied by tumour cells.
  • a tumour or tumour sample may further encompass tumour area occupied by tumour associated intratumoural cells and/or tumour associated stroma (e.g., contiguous peri-tumoral desmoplastic stroma).
  • Tumour associated intratumoural cells and/or tumour associated stroma may include areas of immune infiltrates (e.g., tumour infiltrating immune cells as described herein) immediately adjacent to and/or contiguous with the main tumour mass.
  • immune infiltrates e.g., tumour infiltrating immune cells as described herein
  • response to therapy biomarker expression is evaluated on tumour cells.
  • a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue is a single sample or combined multiple samples from the same subject or individual that are obtained at one or more different time points than when the test sample is obtained.
  • a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue is obtained at an earlier time point from the same subject or individual than when the test sample is obtained.
  • Such reference sample, reference cell, reference tissue, control sample, control cell, or control tissue may be useful if the reference sample is obtained during initial diagnosis of cancer and the test sample is later obtained when the cancer becomes metastatic.
  • a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue is a combination of multiple samples from one or more healthy individuals who are not the subject or individual.
  • a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue is a combination of multiple samples from one or more individuals with a disease or disorder (e.g., cancer) who are not the subject or individual.
  • a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue is pooled RNA samples from normal tissues or pooled plasma or serum samples from one or more individuals who are not the subject or individual.
  • a reference sample, reference cell, reference tissue, control sample, control cell, or control tissue is pooled RNA samples from tumour tissues or pooled plasma or serum samples from one or more individuals with a disease or disorder (e.g., cancer) who are not the subject or individual.
  • a disease or disorder e.g., cancer
  • the sample is a tissue sample from the individual.
  • the tissue sample is a tumour tissue sample (e.g., biopsy tissue).
  • the tissue sample is lung tissue.
  • the tissue sample is renal tissue.
  • the tissue sample is skin tissue.
  • the tissue sample is pancreatic tissue.
  • the tissue sample is gastric tissue.
  • the tissue sample is bladder tissue.
  • the tissue sample is esophageal tissue.
  • the tissue sample is mesothelial tissue.
  • the tissue sample is breast tissue.
  • the tissue sample is thyroid tissue.
  • the tissue sample is colorectal tissue.
  • a tumour tissue sample may encompass part or all of the tumour area occupied by tumour cells.
  • a tumour or tumour sample may further encompass tumour area occupied by tumour associated intratumoral cells and/or tumour associated stroma (e.g., contiguous peri-tumoral desmoplastic stroma).
  • tumour associated intratumoral cells and/or tumour associated stroma may include areas of immune infiltrates immediately adjacent to and/or contiguous with the main tumour mass.
  • tumour cell staining is expressed as the percentage of all tumour cells showing staining (e.g., membranous, cytoplasmic or nuclear staining) of any intensity.
  • soft tissue tumours include leukemia (e.g., chronic myelogenous leukemia, acute myelogenous leukemia, adult acute lymphoblastic leukemia, acute myelogenous leukemia, mature B-cell acute lymphoblastic leukemia, chronic lymphocytic leukemia, prolymphocytic leukemia, or hairy cell leukemia) or lymphoma (e.g., non-Hodgkin’s lymphoma, cutaneous T-cell lymphoma, or Hodgkin’s disease).
  • a solid tumour includes any cancer of body tissues other than blood, bone marrow, or the lymphatic system. Solid tumours can be further divided into those of epithelial cell origin and those of non-epithelial cell origin.
  • epithelial cell solid tumours include tumours of the gastrointestinal tract, colon, colorectal (e.g., basaloid colorectal carcinoma), breast, prostate, lung, kidney, liver, pancreas, ovary (e.g. , endometrioid ovarian carcinoma), head and neck, oral cavity, stomach, duodenum, small intestine, large intestine, anus, gall bladder, labium, nasopharynx, skin, uterus, male genital organ, urinary organs (e.g., urothelium carcinoma, dysplastic urothelium carcinoma, transitional cell carcinoma), bladder, and skin.
  • colorectal e.g., basaloid colorectal carcinoma
  • breast prostate
  • lung kidney
  • liver pancreas
  • ovary e.g. , endometrioid ovarian carcinoma
  • head and neck oral cavity
  • stomach duodenum
  • small intestine large intestine
  • gall bladder labium
  • Solid tumours of non-epithelial origin include sarcomas, brain tumours, and bone tumours.
  • the cancer is non-small cell lung cancer (NSCLC).
  • the cancer is a second-line or third-line locally advanced or metastatic non-small cell lung cancer.
  • the cancer is adenocarcinoma.
  • the cancer is a squamous cell carcinoma.
  • the cancer is non-small cell lung cancer (NSCLC), glioblastoma, neuroblastoma, melanoma, breast carcinoma (e.g., triple-negative breast cancer), gastric cancer, colorectal cancer (CRC), or hepatocellular carcinoma.
  • the cancer is a primary tumour.
  • the cancer is a metastatic tumour at a second site derived from any of the above types of cancer.
  • the at least one response to therapy biomarker is detected in the sample using a method selected from the group consisting of FACS, Western blot, ELISA, immunoprecipitation, immunohistochemistry, immunofluorescence, radioimmunoassay, dot blotting, immunodetection methods, HPLC, surface plasmon resonance, optical spectroscopy, mass spectrometry, HPLC, qPCR, RT-qPCR, multiplex qPCR or RT- qPCR, RNA-seq, microarray analysis, SAGE, MassARRAY technique, and FISH, and combinations thereof.
  • the least one response to therapy biomarker is/ detected using FACS analysis.
  • expression of EpCAM is elevated in samples from individuals that respond to treatment with a therapy, suitably chemotherapy, and/or an immunotherapy (e.g., one that comprises an anti-immune checkpoint molecule antibody, illustrative examples of which include an anti-PD-1 antibody; or a PARP inhibitor therapy).
  • an immunotherapy e.g., one that comprises an anti-immune checkpoint molecule antibody, illustrative examples of which include an anti-PD-1 antibody; or a PARP inhibitor therapy.
  • expression of and ABCB5 is elevated in samples from individuals that respond to treatment with a therapy, suitably chemotherapy, and/or an immunotherapy (e.g., one that comprises an anti-immune checkpoint molecule antibody, illustrative examples of which include an anti-PD-1 antibody; or a PARP inhibitor therapy).
  • the expression level of one or more biomarker genes, proteins and/or cellular composition may be compared to a reference which may include a sample from a subject not receiving a therapy (e.g., a PI3K inhibitor therapy).
  • a reference may include a sample from the same subject before receiving a therapy (e.g., a cytotoxic therapy or an immunotherapy).
  • a reference may include a reference value from one or more samples of other subjects receiving a therapy (e.g., a cytotoxic therapy or an immunotherapy).
  • a population of patients may be treated, and a mean, average, or median value for expression level of the at least one response to therapy biomarker, cancer recurrence biomarker, and/or treatment monitoring biomarker, may be generated from the population as a whole.
  • a set of samples obtained from cancers having a shared characteristic e.g., the same cancer type and/or stage, or exposure to a common therapy
  • This set may be used to derive a reference, e.g., a reference number, to which a subject’s sample may be compared. Any of the references described herein may be used as a reference for monitoring PD activity.
  • a sample may include cancer cells.
  • the sample may be a peripheral blood sample (e.g., from a patient having a tumour).
  • the sample is a tumour sample.
  • a tumour sample may include cancer cells, lymphocytes, leukocytes, stroma, blood vessels, connective tissue, basal lamina, and any other cell type in association with the tumor.
  • the sample is a tumour tissue sample containing tumor-infiltrating leukocytes.
  • the sample may be processed to separate or isolate one or more cell types (e.g., leukocytes).
  • the sample may be used without separating or isolating cell types.
  • the sample may be a peripheral blood sample.
  • a peripheral blood sample may include white blood cells, PBMCs, and the like. Any technique known in the art for isolating leukocytes from a peripheral blood sample may be used. For example, a blood sample may be drawn, red blood cells may be lysed, and a white blood cell pellet may be isolated and used for the sample. In another example, density gradient separation may be used to separate leukocytes (e.g., PBMCs) from red blood cells.
  • a fresh peripheral blood sample i.e., one that has not been prepared by the methods described above may be used.
  • a peripheral blood sample may be prepared by incubation in a solution to preserve mRNA and/or protein integrity.
  • responsiveness to therapy may refer to any one or more of: extending survival (including overall survival and progression free survival); resulting in an objective response (including a complete response or a partial response); or improving signs or symptoms of cancer.
  • responsiveness may refer to improvement of one or more factors according to the published set of RECIST guidelines for determining the status of a tumour in a cancer patient (i.e., responding, stabilizing, or progressing). For a more detailed discussion of these guidelines, see, Eisenhauer et al. (2009, Eur. J. Cancer 45: 228- 47), Topalian et al. (2012, N. Engl. J. Med. 366:2443-54), Wolchok et al.
  • a responsive subject may refer to a subject whose cancer(s) show improvement, e.g., according to one or more factors based on RECIST criteria.
  • a non-responsive subject may refer to a subject whose cancer(s) do not show improvement, e.g., according to one or more factors based on RECIST criteria.
  • response criteria may not be adequate to characterize the antitumour activity of therapeutic agents of the invention, which can produce delayed responses that may be preceded by initial apparent radiological progression, including the appearance of new lesions. Therefore, modified response criteria have been developed that account for the possible appearance of new lesions and allow radiological progression to be confirmed at a subsequent assessment. Accordingly, in some embodiments, responsiveness may refer to improvement of one of more factors according to immune-related response criteria (irRC) (see, e.g., Wolchok et al. (2009, supra)). In some embodiments, new lesions are added into the defined tumour burden and followed, e.g., for radiological progression at a subsequent assessment.
  • irRC immune-related response criteria
  • presence of non-target lesions is included in assessment of complete response and not included in assessment of radiological progression.
  • radiological progression may be determined only on the basis of measurable disease and/or may be confirmed by a consecutive assessment >4 weeks from the date first documented.
  • responsiveness may include immune activation. In some embodiments, responsiveness may include treatment efficacy. In some embodiments, responsiveness may include immune activation and treatment efficacy.
  • the biomarkers of the present invention can be used in predictive and/or prognostic tests to assess, determine, and/or qualify (used interchangeably herein) response to therapy signature status in a patient and therefore, direct treatment of the patient.
  • the phrase “response to therapy signature status” includes a high response to therapy signature (RT high) and a low response to therapy signature (RT low). Based on this status, further procedures may be indicated, including additional tests or therapeutic procedures or regimens.
  • biomarkers are disclosed herein, and it is understood that when combinations, subsets, interactions, groups, etc., of these biomarkers are disclosed that while specific reference of each various individual and collective combinations and permutation of these compounds may not be explicitly disclosed, each is specifically contemplated and described herein. Thus, if a panel of biomarkers A, B, and C are disclosed as well as a class of biomarkers D, E, and F and an example of a combination panel A-D is disclosed, then even if each is not individually recited each is individually and collectively contemplated meaning combinations, A-E, A-F, B-D, B-E, B-F, C-D, C-E, and C-F are considered disclosed.
  • any subset or combination of these is also disclosed.
  • the sub-group of A-E, B-F, and C-E would be considered disclosed.
  • This concept applies to all aspects of this application including, but not limited to, steps in methods of using the disclosed biomarkers.
  • steps in methods of using the disclosed biomarkers are a variety of additional steps that can be performed, it is understood that each of these additional steps can be performed with any specific embodiment or combination of embodiments of the disclosed methods.
  • the response to therapy signature panel suitably includes one or more of IL- 6, EpCAM, ABCB5, and/or GBP2.
  • these signatures include IL-6, and at least one other biomarker selected from the following biomarker combinations: (a) EpCAM; (b) EpCAM and ABCB5; (c) EpCAM and GBP2; (d) EpCAM, ABCB5, and GBP2; (e) ABCB5; (f) ABCB5, and GBP2; (g) EpCAM, ABCB5, and GBP2; and (h) GBP2.
  • the biomarker signatures of the present invention may show a statistical difference in different response to therapy statuses of at least p ⁇ 0.05, p ⁇ 1 O’ 2 , p ⁇ 1 O’ 3 , p ⁇ 1 O’ 4 or p ⁇ 1 O’ 5 .
  • Predictive or prognostic tests that use these biomarkers may show an ROC of at least 0.6, at least about 0.7, at least about 0.8, or at least about 0.9.
  • the biomarkers are measured in a patient sample using the methods described herein and a response to therapy signature status is calculated.
  • the measurement(s) may then be compared with a relevant predictive or prognostic amount(s), cut-off(s), or multivariate model scores that distinguish a high therapy response signature (RT high) status from a low therapy response signature (RT low) status.
  • the predictive or prognostic amount(s) represents a measured amount of a biomarker(s) above which or below which a patient is classified as having a particular therapy response signature status.
  • the particular predictive or prognostic cutoffs can be determined, for example, by measuring the level or amount of biomarkers in a statistically significant number of samples from patients with different response to therapy signature statuses, and drawing the cut-off to suit the desired levels of specificity and sensitivity.
  • the values measured for biomarkers of a biomarker panel are mathematically combined and the combined value is correlated to the underlying predictive or prognostic question of high or low response to therapy signature.
  • Biomarker values may be combined by any appropriate mathematical method known in the art.
  • Well-known mathematical methods for correlating a biomarker combination to a disease status employ methods like discriminant analysis (DA) (e.g., linear-, quadratic-, regularized-DA), discriminant functional analysis (DFA), kernel methods (e.g., SVM), multidimensional scaling (MDS), nonparametric methods (e.g., k-nearest-neighbor classifiers), PLS (partial least squares), tree-based methods (e.g., logic regression, CART, random forest methods, boosting/bagging methods), generalized linear models (e.g., logistic regression), principal components based methods (e.g., SIMCA), generalized additive models, fuzzy logic based methods, neural networks and genetic algorithms based methods.
  • DA discriminant analysis
  • DFA discriminant functional analysis
  • kernel methods e.g., SVM
  • MDS multidimensional scaling
  • nonparametric methods e.g., k-nearest-neigh
  • the method used in a correlating a biomarker combination of the present invention is selected from DA (e.g., linear-, quadratic-, regularized discriminant analysis), DFA, kernel methods (e.g., SVM), MDS, nonparametric methods (e.g., k- nearest-neighbor classifiers), PLS (partial least squares), tree-based methods (e.g., logic regression, CART, random forest methods, boosting methods), or Generalized linear models (e.g., logistic regression), and principal components analysis. Details relating to these statistical methods are found in the following references: Ruczinski et al., J.
  • data that are generated using samples such as “known samples” can then be used to “train” a classification model.
  • a “known sample” is a sample that has been pre-classified.
  • the data that are used to form the classification model can be referred to as a “training data set”.
  • the training data set that is used to form the classification model may comprise raw data or pre-processed data.
  • the classification model can recognize patterns in data generated using unknown samples.
  • the classification model can then be used to classify the unknown samples into classes. This can be useful, for example, in predicting whether or not a particular biological sample is associated with a certain biological condition.
  • Classification models can be formed using any suitable statistical classification or learning method that attempts to segregate bodies of data into classes based on objective parameters present in the data. Classification methods may be either supervised or unsupervised. Examples of supervised and unsupervised classification processes are described in Jain, “Statistical Pattern Recognition: A Review”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1 , January 2000, the teachings of which are incorporated by reference.
  • supervised classification training data containing examples of known categories are presented to a learning mechanism, which learns one or more sets of relationships that define each of the known classes. New data may then be applied to the learning mechanism, which then classifies the new data using the learned relationships.
  • supervised classification processes include linear regression processes (e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal components regression (PCR)), binary decision trees (e.g., recursive partitioning processes such as CART), artificial neural networks such as back propagation networks, discriminant analyses (e.g., Bayesian classifier or Fischer analysis), logistic classifiers, and support vector classifiers (support vector machines).
  • linear regression processes e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal components regression (PCR)
  • binary decision trees e.g., recursive partitioning processes such as CART
  • artificial neural networks such as back propagation networks
  • discriminant analyses e.g., Bayesian classifier or Fischer analysis
  • Another supervised classification method is a recursive partitioning process.
  • Recursive partitioning processes use recursive partitioning trees to classify data derived from unknown samples. Further details about recursive partitioning processes are provided in U.S. Patent Publication No. 2002/0138208 to Paulse et al., “Method for analyzing mass spectra.”
  • the classification models that are created can be formed using unsupervised learning methods.
  • Unsupervised classification attempts to learn classifications based on similarities in the training data set, without pre-classifying the spectra from which the training data set was derived.
  • Unsupervised learning methods include cluster analyses. A cluster analysis attempts to divide the data into “clusters” or groups that ideally should have members that are very similar to each other, and very dissimilar to members of other clusters. Similarity is then measured using some distance metric, which measures the distance between data items, and clusters together data items that are closer to each other.
  • Clustering techniques include the MacQueen's K-means algorithm and the Kohonen's SelfOrganizing Map algorithm.
  • the training data set and the classification models according to embodiments of the invention can be embodied by computer code that is executed or used by a digital computer.
  • the computer code can be stored on any suitable computer readable media including optical or magnetic disks, sticks, tapes, etc., and can be written in any suitable computer programming language including R, C, C++, visual basic, etc.
  • the learning algorithms described above are useful both for developing classification algorithms for the biomarkers already discovered, and for finding new biomarkers.
  • the classification algorithms form the base for diagnostic/prognostic tests by providing diagnostic/prognostic values (e.g., cut-off points) for biomarkers used singly or in combination.
  • any of the classification methods disclosed herein may be performed at least in part by one or more computers and/or may be stored in a database on a non-transitory computer medium. In some embodiments any of the classification methods disclosed herein may be embodied or stored at least in part on a computer-readable medium having computer-executable instructions thereon. In some embodiments a computer- readable medium comprises any non-transitory and/or tangible computer-readable medium.
  • Biomarker values can be measured biomarker values, which are values of biomarkers directly measured for the subject, or alternatively could be “derived” biomarker values, which are values that have been derived from one or more measured biomarker values, for example by applying a function to the one or more measured biomarker values.
  • biomarkers to which a function has been applied are referred to as “derived biomarkers”.
  • the biomarker values may be determined in any one of a number of ways that are well known in the art. For example, a comprehensive description of biomarker value determination can be found in Inti. Pat. Pub. No. WO 2015/117204, which is incorporated herein by reference in its entirety.
  • the process of determining biomarker values can include measuring the biomarker values, for example by performing tests on the subject or on sample(s) obtained from the subject.
  • the step of determining the biomarker values includes having an electronic processing device receive or otherwise obtain biomarker values that have been previously measured or derived. This could include for example, retrieving the biomarker values from a data store such as a remote database, obtaining biomarker values that have been manually input, using an input device, or the like.
  • the indicator may be determined using a combination of a plurality of biomarker values, the indicator being at least partially indicative of cancer recurrence or responsiveness to cancer therapy. Assuming the method is performed using an electronic processing device, an indication of the indicator is optionally displayed or otherwise provided to the user.
  • the biomarkers used within the above-described method can define a biomarker profile for cancer recurrence or cancer therapy responsiveness, which includes a minimal number of biomarkers (e.g., at least one biomarker), whilst maintaining sufficient performance to allow the biomarker profile to be used in making a clinically relevant determination.
  • a biomarker profile for cancer recurrence or cancer therapy responsiveness which includes a minimal number of biomarkers (e.g., at least one biomarker), whilst maintaining sufficient performance to allow the biomarker profile to be used in making a clinically relevant determination.
  • Minimizing the number of biomarkers used minimizes the costs associated with performing diagnostic or prognostic tests and in the case of polypeptide biomarkers, allows the test to be performed utilizing relatively straightforward techniques such as quantitative RT-PCR and/or immunofluorescence, and allowing the test to be performed rapidly in a clinical environment.
  • the indicator-determining methods suitably include determining at least one biomarker value, wherein the biomarker value is a value measured or derived for at least one cancer therapy biomarker of the subject and is at least partially indicative of a concentration or abundance of the cancer recurrence or cancer therapy biomarker in a sample taken from the subject.
  • the cancer recurrence biomarker comprises an expression product of MAPI LC3B.
  • the derived biomarker value is then used to determine the indicator for use in determining the likelihood of a subject responding to cancer therapy, either by using the derived biomarker value as an indicator value, or by performing additional processing, such as comparing the derived biomarker value to a reference or the like, as generally known in the art and as described in more detail below, or to another biomarker value.
  • the indicator is indicative of a level, concentration or abundance of an expression product of PIK3R2. In other embodiments, the indicator is indicative of a level or abundance of an expression product of PIK3R2.
  • the derived biomarker values could be combined using a combining function such as an additive model; a linear model; a support vector machine; a neural network model; a random forest model; a regression model; a genetic algorithm; an annealing algorithm; a weighted sum; a nearest neighbor model; and a probabilistic model.
  • the indicator is compared to an indicator reference, with a likelihood of responsiveness to cancer being determined in accordance with results of the comparison.
  • the indicator reference may be derived from indicators determined for a number of individuals in a reference population.
  • the reference population typically includes individuals having different characteristics, such as a plurality of individuals of different sexes; and/or ethnicities, with different groups being defined based on different characteristics, with the subject's indicator being compared to indicator references derived from individuals with similar characteristics.
  • the reference population can include a plurality of individuals known to have a recurrence of cancer or those that are responsive to cancer therapy (including completely responsive and/or partially responsive), and in particular therapy using an immune checkpoint inhibitor; or a plurality of individuals known to be non-responsive to cancer therapy, and in particular therapy using an immune checkpoint inhibitor.
  • the indicator-determining methods of the present invention are performed using at least one electronic processing device, such as a suitably programmed computer system or the like.
  • the electronic processing device typically obtains at least one measured biomarker value, either by receiving this from a measuring or other quantifying device, or by retrieving these from a database or the like.
  • the processing device determines the indicator by any suitable means.
  • the processing device can then generate a representation of the indicator, for example by generating a sign or alphanumeric indication of the indicator, a graphical indication of a comparison of the indicator to one or more indicator references or an alphanumeric indication of the likely responsiveness of the subject to have a recurrence or cancer and/or respond to a cancer therapy.
  • the indicator-determining methods of the present invention typically include obtaining a sample from a subject who has been diagnosed with cancer, and quantifying or otherwise assessing at least one of the biomarkers within the sample to determine biomarker values. This can be achieved using any suitable technique, and will depend on the nature of the biomarker, as described above. Suitably, an individual measured or biomarker value corresponds to the level, abundance or concentration of a cancer therapy biomarker or to a function that is applied to that level or amount.
  • the indicator in some embodiments of the indicator-determining method of the present invention which uses a plurality of cancer recurrence and/or cancer therapy biomarkers, is based on a ratio of concentrations of two polynucleotides or two polypeptides
  • this process would typically include quantifying the polynucleotides or polypeptides by any means known in the art, including quantitative RT-PCR or immunofluorescence, or by a functional assay.
  • the likelihood of a subject responding to cancer is established by determining one or more cancer therapy biomarker values, wherein an individual cancer therapy biomarker value is indicative of a value measured or derived for a cancer therapy biomarker in a subject or in a sample obtained from the subject.
  • sample cancer therapy biomarkers are referred to herein as “sample cancer therapy biomarkers.”
  • a sample cancer therapy biomarker will correspond to a reference cancer therapy biomarker (also referred to herein as a “corresponding cancer therapy biomarker”).
  • corresponding cancer therapy biomarker is meant a cancer therapy biomarker that is structurally and/or functionally similar to a reference cancer therapy biomarker.
  • Representative corresponding cancer therapy biomarkers include expression products of allelic variants (same locus), homologues (different locus), and orthologues (different organism) of reference cancer therapy biomarker genes.
  • Nucleic acid variants of reference cancer therapy biomarker genes and encoded cancer therapy biomarker polypeptides can contain nucleotide substitutions, deletions, inversions and/or insertions. Variation can occur in either or both the coding and noncoding regions. The variations can produce both conservative and non-conservative amino acid substitutions (as compared in the encoded product).
  • conservative variants include those sequences that, because of the degeneracy of the genetic code, encode the amino acid sequence of a reference cancer therapy polypeptide. The same approach applies for the cancer recurrence biomarkers.
  • Corresponding cancer biomarkers include amino acid sequences that display substantial sequence similarity or identity to the amino acid sequence of a reference cancer biomarker polypeptide.
  • an amino acid sequence that corresponds to a reference amino acid sequence will display at least about 80, 81 , 82, 83, 84, 85, 86, 97, 88, 89, 90, 91 , 92, 93, 94, 95, 96, 97, 98, 99% or even up to 100% sequence similarity or identity to a reference amino acid sequence.
  • Corresponding biomarkers also include nucleic acid sequences that display substantial sequence similarity or identity to the nucleic acid sequence of a reference biomarker polynucleotide.
  • a nucleic acid sequence that corresponds to a reference nucleic acid sequence will display at least about 70, 71 , 72, 73, 74, 75, 76, 77, 78, 79, 80, 81 , 82, 83, 84, 85, 86, 97, 88, 89, 90, 91 , 92, 93, 94, 95, 96, 97, 98, 99% or even up to 100% sequence similarity or identity to a reference nucleic acid sequence.
  • the sequences are aligned for optimal comparison purposes (e.g., gaps can be introduced in one or both of a first and a second amino acid or nucleic acid sequence for optimal alignment and non-homologous sequences can be disregarded for comparison purposes).
  • the length of a reference sequence aligned for comparison purposes is at least 30%, usually at least 40%, more usually at least 50%, 60%, and even more usually at least 70%, 80%, 90%, 100% of the length of the reference sequence.
  • the amino acid residues or nucleotides at corresponding amino acid positions or nucleotide positions are then compared.
  • sequence of a cDNA of a mRNA transcript and the sequence of the mRNA itself, are deemed to have 100% sequence identity, although it is understood that one molecule is a DNA molecule and thus comprises “T” while the other is a RNA molecule and this comprises “U”.
  • the percent identity between nucleotide sequences is determined using the GAP program in the GCG software package (available at http://www.gcg.com), using a NWSgapdna.CM P matrix and a gap weight of 40, 50, 60, 70, or 80 and a length weight of 1 , 2, 3, 4, 5, or 6.
  • An nonlimiting set of parameters includes a Blossum 62 scoring matrix with a gap penalty of 12, a gap extend penalty of 4, and a frameshift gap penalty of 5.
  • the percentage identity or similarity between amino acid or nucleotide sequences can be determined using the algorithm of E. Meyers and W. Miller (1989, Cabios, 4: 11 -17) which has been incorporated into the ALIGN program (version 2.0), using a PAM 120 weight residue table, a gap length penalty of 12 and a gap penalty of 4.
  • nucleic acid and protein sequences described herein can be used as a “query sequence” to perform a search against public databases to, for example, identify other family members or related sequences.
  • Such searches can be performed using the NBLAST and X BLAST programs (version 2.0) of Altschul, et al., (1990, J. Mol. Biol., 215 : 403- 10).
  • Gapped BLAST can be utilized as described in Altschul et al., (1997, Nucleic Acids Res. 25: 3389-3402).
  • the default parameters of the respective programs e.g., XBLAST and NBLAST
  • XBLAST and NBLAST can be used .
  • Corresponding cancer therapy biomarker and cancer recurrence biomarker polynucleotides also include nucleic acid sequences that hybridize to reference cancer therapy biomarker polynucleotides, or to their complements, under stringency conditions described below.
  • the term “hybridizes under low stringency, medium stringency, high stringency, or very high stringency conditions” describes conditions for hybridization and washing.
  • “Hybridization” is used herein to denote the pairing of complementary nucleotide sequences to produce a DNA-DNA hybrid or a DNA-RNA hybrid.
  • Complementary base sequences are those sequences that are related by the base-pairing rules. In DNA, A pairs with T and C pairs with G.
  • RNA U pairs with A and C pairs with G.
  • mismatch refers to the hybridization potential of paired nucleotides in complementary nucleic acid strands. Matched nucleotides hybridize efficiently, such as the classical A-T and G-C base pair mentioned above. Mismatches are other combinations of nucleotides that do not hybridize efficiently.
  • medium stringency conditions includes hybridizing in 6 x SSC at about 45°C, followed by one or more washes in 0.2 x SSC, 0.1% SDS at 60°C.
  • High stringency conditions include and encompass from at least about 31% v/v to at least about 50% v/v formamide and from about 0.01 M to about 0.15 M salt for hybridization at 42°C, and about 0.01 M to about 0.02 M salt for washing at 55°C.
  • High stringency conditions also may include 1% BSA, 1 mM EDTA, 0.5 M NaHPCk (pH 7.2), 7% SDS for hybridization at 65°C, and (i) 0.2 x SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPC (pH 7.2), 1 % SDS for washing at a temperature in excess of 65°C.
  • One embodiment of high stringency conditions includes hybridizing in 6 x SSC at about 45°C, followed by one or more washes in 0.2 x SSC, 0.1% SDS at 65°C.
  • a corresponding cancer therapy biomarker or cancer recurrence biomarker polynucleotide is one that hybridizes to a disclosed nucleotide sequence under very high stringency conditions.
  • very high stringency conditions includes hybridizing 0.5 M sodium phosphate, 7% SDS at 65°C, followed by one or more washes at 0.2 x SSC, 1% SDS at 65°C.
  • the composition contains PIK3R2 transcript or cDNA thereof, and at least one oligonucleotide primer or probe that hybridizes to the PIK3R2 transcript or cDNA.
  • the level of transcript or cDNA in the composition is reflective of the level of transcript in the subject from which the sample is taken and obtained, and can thus be used in the method of the present invention to assess the likelihood of a subject responding to cancer therapy or cancer recurrence.
  • the compositions may comprise a polypeptide expression product of p85p and a detection agent that binds to the polypeptide expression product of PIK3R2.
  • the composition contains tumour cells that comprise the polypeptide expression product of PIK3R2.
  • the detection agents are antibodies or antigen-binding fragments thereof that are specific for the polypeptide expression product of PIK3R2.
  • Solid supports of the present invention include those to which at least one oligonucleotide primer or probe that hybridizes to a, for example, PIK3R2 transcript or cDNA thereof, are immobilized.
  • a PIK3R2 transcript or cDNA thereof is/are hybridized to their respective oligonucleotides or probes.
  • kits for determining expression of biomarkers including the response to therapy biomarkers, the cancer recurrence biomarkers, and/or the treatment monitoring biomarkers, disclosed herein, which include reagents that allow detection and/or quantification of the biomarkers.
  • reagents include, for example, compounds or materials, or sets of compounds or materials, which allow quantification of the biomarkers.
  • kits may also optionally include appropriate reagents for detection of labels, positive and negative controls, washing solutions, blotting membranes, microtiter plates, dilution buffers and the like.
  • a nucleic acid-based detection kit may include (i) a treatment monitoring biomarker polynucleotide (which may be used as a positive control), (ii) a primer or probe that specifically hybridizes to a treatment monitoring biomarker polynucleotide.
  • enzymes suitable for amplifying nucleic acids including various polymerases (reverse transcriptase, Taq, SequenaseTM, DNA ligase etc.
  • kits will comprise, in suitable means, distinct containers for each individual reagent and enzyme as well as for each primer or probe.
  • Such individuals include patients that are predicted to be non-responsive or weakly responsive to the therapy and thus have a decreased likelihood of benefiting from administration of the therapy relative to other patients having different characteristic(s) (e.g., responsiveness to the therapy), or a low or substantially no likelihood of benefiting from such treatment, such that it may be desirable to use a different or additional treatment.
  • whether a subject is an appropriate candidate for therapy with a therapy is determined based on an assay of at least one response to therapy biomarker.
  • PI3K-mTOR induces an anti-tumour immune profile.
  • the cellular neighbourhood (CN) investigation revealed ten unique cellular neighbourhoods, including CN1 enriched in erythrocytes; CN6, CN7, CN9, and CN10 with abundance of B cells, CD1 1 c+ dendritic cells, T cells, and CD1 1 b+ neutrophils (Figure 2A).
  • PI3K-mTOR inhibition diminishes mast cell and Treg infiltration into the tumour
  • NanoString nCounter analysis of relative cell abundance also revealed heightened total tumour infiltrating lymphocyte (TIL) counts in both GDC-0084 monotherapy and combination therapy. This elevation was characterized by enrichment in subsets of immune cells with potent anti-tumour properties, including dendritic cells (DCs), natural killer (NK) cells, T cells and B cells (Figure 5A). Importantly, elevated TIL levels have more favourable therapeutic outcomes in the context of metastatic breast cancer.
  • GDC-0084 treatment also enhanced cytotoxic cell numbers and associated IFN-y and Granzyme B mRNA expression (Figure 5B), confirming re-invigoration of exhausted T cell populations. Moreover, the presence of exhausted T cells was more pronounced in both the control and anti-PD-1 treated groups, indicative of their potential involvement in immune suppression (Figure 5C).
  • tumour microenvironment signature (Table 1 ).
  • This panel of 16 genes which were all up-regulated following GDC-0084 treatment, includes the tumour suppressor genes CDH1, DCN, LCN2, LOX, and SOCS1; the anti-tumour genes CXCR6, NKG7, and N0S2; the immune checkpoint-associated genes CD274 (PD-L1 ) and PDCD1LG2 (PD-L2) and the cytotoxicity-related genes GZMB, IFNG, and HM0X1 ( Figure 7; and Table 1 ). Additionally, the pro-tumour genes FAM83A, PDGFRB, and F0XP3were all down-regulated in response to PI3K-mT0R inhibition ( Figure 7; Table 1 ).
  • CTCs Circulating tumour cells
  • EpCAM EpCAM
  • ABCB5 antibodies directed against IL-6 and the CTC markers
  • Figure 10 elevated levels of IL-6/EpCAM/ABCB5+ cells were observed pretreatment when inflammation is prominent.
  • the proportion of CTCs expressing IL6 dramatically reduced confirming the utility of IL-6/EpCAM/ABCB5+ CTCs as a biomarker for patient response to treatment.
  • PBMC biomarker predicts cancer recurrence
  • PI3Ks are composed of p110 catalytic subunits and p85 regulatory subunits. Recent studies have demonstrated that p850 promotes tumorigenesis by inducing nuclear translocation (see, Hao et al., 2022). Nuclear p850 recruits deubiquitinase USP7 to stabilize the histone methyltransferases, EZH1 and EZH2, and enhances histone H3 lysine 27 trimethylation (H3K27Me3). To investigate the direct interaction of p850 and H3K27Me3, the inventors employed the DUOLINK® proximity ligation assay. PBMCs were prepared from TNBC patient blood samples (pre- and post-chemotherapy) and subjected to the DUOLINK® proximity ligation assay ( Figure 11 A).
  • PIK3R2 which encodes p85p
  • GSE158309 dataset as described in Heimes et al., 2020.
  • Patients with basal-like/TNBC expressing high levels of PIK3R2 (solid lines) showed poorer distant metastasis-free survival (DMFS) than patients with low expression ( Figure 12A).
  • PIK3R2 mRNA expression in stage IV metastatic TNBC post chemotherapy (carboplatin, nab-paclitaxel) + pembrolizumab durvalumab + olaparib in a single-arm phase-2 trial (Wilkerson et al., 2024) (Figure 12B).
  • Patients with higher mRNA expression of PIK3R2 showed poorer overall survival compared with patients with lower PIK3R2 mRNA expression.
  • mice Female BALB/c mice, 6-8 weeks of age, were procured from the Animal Resources Centre (ARC). Following acquisition, the mice were allowed a one-week acclimatization period within the containment suites at QIMR Berghofer Medical Research Institute (QIMRB). All experimental procedures were performed in accordance with the guidelines and regulations endorsed by the QIMRB Ethics Committee. 1 x 10 5 4T1 cells, suspended in phosphate-buffered saline (PBS) were administered into the mammary fat pad of the BALB/c mice. Treatments were started when tumours reached approximately 50-100 mm 3 .
  • PBS phosphate-buffered saline
  • mice were subjected to anti-PD-1 treatment (10 mg/kg) administered intraperitoneally every 5 days, and daily oral administration of GDC-0084 (7.5 mg/kg). Tumours were harvested prior to reaching ethical limits and fixed in 3.7% paraformaldehyde solution (FFPE) or frozen in OCT for downstream analysis.
  • FFPE paraformaldehyde solution
  • RNA samples were subjected to hybridize with a multiplexed mouse tumour signaling 360 panel codeset.
  • the hybridized samples were subsequently loaded onto the nCounter prep station chip, and data acquisition was accomplished using the nCounter Digital Analyzer, following manufacturer-prescribed procedures. Data analysis was undertaken via the utilization of the nSolver Analysis Software version 4.0, supplied by NanoString. Counts were subjected to normalization in accordance with the expression levels of housekeeping genes and positive/negative-control probes.
  • OCT frozen mouse tumour tissues were stained with an 18-panel antibody cocktail (CD19, CD21/35, CD3, TCRb, CD4, CD90.2, CD11 b, CD45, Ly6G, IgM, CD24, CD11c, CD38, CD31 , Teri 19, CD49f, CD71 , Ki67) and subjected to the Spatial Tissue Exploration Program (STEP) platform (Akoya Biosciences). Unsupervised clustering phenotyping and cellular neighborhood analysis
  • Data Analysis was performed using Akoya’s internal software, Multiplexed Image Analysis (MIA), which combines multiple existing analysis tools with a Graphical User Interface.
  • Cell segmentation was first performed using StarDistU] method, where the deep learning model was retrained by Akoya team on DeepCell TissueNet database ⁇ which contains more than one million cells annotated for segmentation purposes from different tissues and different platforms. Cytoplasm segmentation was then estimated from nuclear expansion by morphological dilation. Marker mean fluorescent intensity (MFI) was calculated for each segmented cell and z-score normalization was applied across all cells for each marker such that each marker has a mean equals to zero and standard deviation equals to one. Only QC-passed lineage markers were used for downstream analysis.
  • MFI Marker mean fluorescent intensity
  • MDA-MB-231 cell lines were maintained and cultured in DMEM (Invitrogen) supplemented with 10% FBS, 2 mM L- glutamine, and 1% PSN.
  • MCF-7 cells were stimulated with 1.29 ng/mL phorbol 12-myristate 13- acetate (PMA) (Sigma-Aldrich) or 5 ng/mL recombinant TGF-01 (R&D Systems) for 24 hours.
  • PMA phorbol 12-myristate 13- acetate
  • TGF-01 R&D Systems
  • RNA isolation was then lysed in 500 pl of TRIzol Reagent (Invitrogen) before RNA isolation using the Direct-zol RNA Miniprep Kit (Zymo, R2052) and treated with RNase-free DNase I (Qiagen, 79254) following the manufacturer’s protocol.
  • the Nanodrop was then used to determine the RNA concentration and purity (A260/A280).
  • RNA (1 pg) was then reverse-transcribed to cDNA using the Superscript VILO IV Master Mix (Thermo Fisher, 11756050) following the supplier’s protocol.
  • cDNA was diluted 1 :20 with RNase-free water for RT-qPCR.
  • CTCs were permeabilised by incubating with 0.5% Triton X-100 for 15 min, blocked with 1% BSA in PBS and were probed with either IL-6, EpCAM or ABCB5 and visualized with a donkey anti-rabbit AF 488, anti-mouse AF 568, donkey anti-goat 647. Cover slips were mounted on glass microscope slides with Prolong Glass Antifade reagent (Life Technologies). Protein targets were localised by digital pathology laser scanning microscopy. Single 0.5 pm sections were obtained using the ASI Digital pathology (ASI) platform. Digital images were analysed using automated ASI software as described previously (Applied Spectral Imaging, Carlsbad, CA).
  • ASI ASI Digital pathology
  • PBMCs were fixed in 3.7% paraformaldehyde and cytospinning was used to mount onto glass microscope slides.
  • the DUOLINK proximity ligation assay (MilliporeSigma) was performed using antibodies directed against P850 and H3K27Me3 and digital images were analyzed using Imaged software (Imaged, NIH, Bethesda, MD, USA).

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Abstract

La présente invention concerne de manière générale des biomarqueurs qui sont utiles pour déterminer si un cancer est susceptible de réapparaître chez un sujet et/ou déterminer si un sujet est susceptible de répondre à une thérapie anticancéreuse. L'invention concerne donc des méthodes, des kits et des compositions pour déterminer si un cancer est susceptible de réapparaître chez un sujet ou si un sujet est susceptible de répondre à une thérapie anticancéreuse, ainsi que des méthodes de traitement basées sur une détermination du fait qu'un sujet atteint d'un cancer est susceptible de répondre à une thérapie anticancéreuse.
PCT/AU2025/050117 2024-02-13 2025-02-13 Signature pronostique pour la récidive du cancer et la réactivité au cancer Pending WO2025171441A1 (fr)

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