[go: up one dir, main page]

WO2025083140A1 - Biomarqueurs pour prédiction de réponse à l'immunothérapie - Google Patents

Biomarqueurs pour prédiction de réponse à l'immunothérapie Download PDF

Info

Publication number
WO2025083140A1
WO2025083140A1 PCT/EP2024/079371 EP2024079371W WO2025083140A1 WO 2025083140 A1 WO2025083140 A1 WO 2025083140A1 EP 2024079371 W EP2024079371 W EP 2024079371W WO 2025083140 A1 WO2025083140 A1 WO 2025083140A1
Authority
WO
WIPO (PCT)
Prior art keywords
cells
subject
cancer
immune checkpoint
checkpoint inhibitor
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/EP2024/079371
Other languages
English (en)
Inventor
Raza Ali
Xiao Qian Wang
Luca Gianni
Bianchini GIAMPAOLO
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Cambridge Enterprise Ltd
Fondazione Michelangelo
Original Assignee
Cambridge Enterprise Ltd
Fondazione Michelangelo
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Cambridge Enterprise Ltd, Fondazione Michelangelo filed Critical Cambridge Enterprise Ltd
Publication of WO2025083140A1 publication Critical patent/WO2025083140A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G01N33/56966—Animal cells
    • G01N33/56972—White blood cells
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K39/00—Medicinal preparations containing antigens or antibodies
    • A61K39/395—Antibodies; Immunoglobulins; Immune serum, e.g. antilymphocytic serum
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P35/00—Antineoplastic agents
    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K16/00—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies
    • C07K16/18—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans
    • C07K16/28—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
    • C07K16/2803—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants against the immunoglobulin superfamily
    • C07K16/2827—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants against the immunoglobulin superfamily against B7 molecules, e.g. CD80, CD86
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57415—Specifically defined cancers of breast
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K39/00—Medicinal preparations containing antigens or antibodies
    • A61K2039/505—Medicinal preparations containing antigens or antibodies comprising antibodies
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K39/00—Medicinal preparations containing antigens or antibodies
    • A61K2039/545—Medicinal preparations containing antigens or antibodies characterised by the dose, timing or administration schedule
    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K2317/00—Immunoglobulins specific features
    • C07K2317/20—Immunoglobulins specific features characterized by taxonomic origin
    • C07K2317/24—Immunoglobulins specific features characterized by taxonomic origin containing regions, domains or residues from different species, e.g. chimeric, humanized or veneered
    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K2317/00—Immunoglobulins specific features
    • C07K2317/70—Immunoglobulins specific features characterized by effect upon binding to a cell or to an antigen
    • C07K2317/76—Antagonist effect on antigen, e.g. neutralization or inhibition of binding
    • 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

Definitions

  • the present invention relates to methods for predicting whether a human subject having cancer will be responsive to treatment with an immune checkpoint inhibitor.
  • the present invention also relates to methods of treating cancer.
  • TNBC Triple-negative breast cancer
  • HER2 human epidermal growth factor 2
  • TNBC Treatment for TNBC typically involves chemotherapy, along with surgery and radiotherapy.
  • TNBC is often more aggressive, harder to treat and more likely to recur than cancers which are hormone receptor-positive or HER2 positive.
  • HER2 hormone receptor-positive or HER2 positive.
  • Immunotherapy such as immune checkpoint blockade (ICB) represents an exciting therapeutic option for cancers.
  • chronic T cell stimulation leads to dysfunction, owing to interactions between cells expressing immune checkpoint receptors and ligands.
  • ICB prevents this, which can invigorate dysfunctional T-cells. These invigorated T-cells can then interact with target cancer cells to induce cell death. Interactions between proximate specialized cells in distinct activation states underpin the effect of ICB.
  • the efficacy of ICB therefore depends on both the cellular composition and multicellular organization of tumours.
  • Immunotherapy has transformed the treatment of solid tumours but its best use in breast cancer remains unclear.
  • TNBC immune checkpoint blockade
  • PD-1 programmed death protein 1
  • P-L1 programmed cell death ligand 1
  • pCR surrogate end point pathological complete response
  • pCR can be defined as the absence of residual invasive cancer on hematoxylin and eosin evaluation of the complete resected cancer specimen and all sampled regional lymph nodes following completion of neoadjuvant systemic therapy.
  • Several retrospective analyses including the one used by FDA for accepting pCR as a surrogate failed to show that an improved rate of pCR translated into an improved event free survival at a trial level. Therefore, there is little correlation between an improved rate of pCR and ultimate survival and response of the subject. It thus remains challenging to identify biomarkers which can identify true responders to immunotherapy in the long term. This is also the case for immunotherapy in TNBC.
  • the present invention has been devised in light of the above considerations.
  • the present inventors have found that the proliferative fraction of CD8 + TCF1 + T-cells and/or MHCI + MHCir cells in a sample from a subject having cancer is an accurate and reliable biomarker to identify long-term responders to immunotherapy. These findings are especially surprising, given that the inventors did not find any correlation between cell density of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells and the subjects response to immunotherapy. The proliferative fraction of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells therefore represents an unexpected and accurate biomarker for responsiveness to immunotherapy.
  • the present invention provides a method for predicting whether a human subject having cancer will be responsive to treatment with an immune checkpoint inhibitor, the method comprising: a) measuring the proportion of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells which comprise a detectable expression level of a proliferation marker in a sample obtained from the subject to obtain a CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells proliferative fraction profile of the subject, and b) making a prediction of whether the subject will be responsive or non-responsive to treatment with an immune checkpoint inhibitor therapy based on the proliferative fraction profile of the subject.
  • this method ensures that only subjects likely to be responsive are selected for treatment with an immune checkpoint inhibitor therapy. This ensures that immunotherapy is targeted only to those likely to obtain benefit from immunotherapy, which is especially important given that some subjects can exhibit tolerability issues or chronic side effects upon administration. By restricting immunotherapy only to subjects likely to benefit, this also reduces costs of administration.
  • the MHCI + MHCII + cells are cancer cells, optionally epithelial cells comprising a detectable expression level of one or more of CK5/14, CK/8/18, PanCK and AR.
  • step a) comprises measuring the proportion of CD8 + TCF1 + T-cells or MHCI + MHCII + cells which comprise a detectable expression level of a proliferation marker in a sample obtained from the subject to obtain a CD8 + TCF1 + T-cells or MHCI + MHCII + cells proliferative fraction profile of the subject.
  • step a) comprises measuring the proportion of CD8 + TCF1 + T-cells which comprise a detectable expression level of the proliferation marker in a sample obtained from the subject to obtain a CD8 + TCF1 + T-cell proliferative fraction profile of the subject.
  • the cancer comprises breast cancer, ovarian cancer, pancreatic cancer, colorectal cancer, lung cancer, pancreatic cancer, colon cancer, gastric cancer, bladder cancer, skin cancer, myeloma, non-Hodgkin lymphoma, prostate cancer, oesophageal cancer, head and neck cancer, endometrial cancer, hepatobiliary cancer, duodenal carcinoma, thyroid carcinoma, or renal cell carcinoma.
  • the cancer comprises breast cancer.
  • the cancer may comprise triple-negative breast cancer.
  • the immune checkpoint inhibitor therapy comprises atezolizumab.
  • the proliferation marker comprises Ki67.
  • step b) comprises comparing the proliferative fraction profile of the subject to a plurality of reference proliferative fraction profiles and making a prediction of whether the subject will be responsive or non-responsive to treatment based on the comparison.
  • the plurality of reference proliferative fraction profiles may be separated into at least two quantiles and the proliferative fraction profile of the subject is compared to the quantiles, wherein the subject is predicted to be responsive to treatment when the proliferative fraction profile of the subject fits into the top quantile.
  • the at least two quantiles comprise at least three quantiles.
  • Step a) may comprise measuring the proportion of CD8 + TCF1 + T-cells and MHCI + MHCII + cells which comprise a detectable expression level of the proliferation marker to obtain a CD8 + TCF1 + T-cells and MHCI + MHCII + cells proliferative fraction profile of the subject.
  • responsiveness to treatment comprises event-free survival for a time period of at least 18 months, optionally for a time period of at least 24 months.
  • the sample comprises tumour tissue.
  • the proportion of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells which comprise a detectable expression level of a proliferation marker is measured using immunohistochemistry (IHC) or imaging mass cytometry (IMC).
  • the sample may have been obtained from the subject prior to any treatment.
  • the subject is predicted to be responsive to the immune checkpoint inhibitor therapy, and wherein the method further comprises the step of administering a therapeutically effective amount of an immune checkpoint inhibitor.
  • the immune checkpoint inhibitor may be administered as part of a combination therapy with chemotherapy, optionally wherein the chemotherapy comprises carboplatin and/or nab-paclitaxel.
  • the method comprises concurrent, sequential or separate administration of: a) atezolizumab; and b) carboplatin and/or nab-paclitaxel.
  • the subject is predicted to be non-responsive to immune checkpoint inhibitor therapy, and wherein the method further comprises administering chemotherapy to the subject in the absence of any immune checkpoint inhibitor therapy.
  • the present invention provides a computer-implemented method for predicting whether a human subject having cancer will be responsive to treatment with an immune checkpoint inhibitor, the method comprising: a) Obtaining proliferative fraction profile data representing the proportion of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells which comprise a detectable expression level of a proliferation marker in a sample obtained from the subject, and b) making a prediction of whether the subject will be responsive or non-responsive to treatment with an immune checkpoint inhibitor therapy based on the proliferative fraction profile data of the subject.
  • the present invention also provides an immune checkpoint inhibitor for use in a method of treating cancer, wherein the method comprises: a) measuring the proportion of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells which comprise a detectable expression level of a proliferation marker in a sample obtained from the subject to obtain a CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells proliferative fraction profile of the subject; b) making a prediction of whether the subject will be responsive or non-responsive to treatment with an immune checkpoint inhibitor based on the proliferative fraction profile of the subject; and c) treating the subject with the immune checkpoint inhibitor if the subject is predicted to be responsive to therapy in b).
  • a method of treating cancer comprising administering an immune checkpoint inhibitor to a subject that has been determined to be responsive to the immune checkpoint inhibitor based on a CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells proliferative fraction profile of the subject.
  • the invention includes the combination of the aspects and preferred features described except where such a combination is clearly impermissible or expressly avoided.
  • Figure 1 Flowchart of longitudinal tumour sampling from the NEOTRIP randomised clinical trial for high parameter imaging.
  • Figure 2 Semi-supervised workflow for distinguishing epithelial and TME cells from multiplexed images.
  • Figure 3 Spatial predictors of immunotherapy response at baseline, a, Schematic illustrating cell phenotype density calculation, b, Odds ratios for associations between cell density and pCR for TME cell phenotypes, c, Boxplot of PD-L1 + IDO+ APC density across treatment arms and response.
  • Figure 4 Schematic illustrating principles of homotypic and heterotypic cell-cell interaction metrics.
  • Figure 5 Odds ratios for associations between heterotypic epithelial-to-TME cell phenotypes and pCR. Odds ratios are derived from univariate logistic regression: circles represent point estimates and whiskers indicate 95% confidence intervals. Depicted P values are derived from a term for interaction between the predictor and treatment in logistic regression models (including separate terms for the predictor and treatment). Asterisks indicate associations with an FDR ⁇ 0.1 by the Benjamini-Hochberg method, b, Bar charts of the proportion of tumours achieving pCR in patients with no selected epithelial— TME interactions per arm (0) or increasing tertiles of epithelial-TME interactions per arm (T1-T3). Numbers on bars are absolute numbers of patients in each category.
  • Figure 7. Boxplots of mean expression levels (per tumour) of activation markers for T cells in contact or not in contact with cancer cells at baseline, b, Proportion of T cells positive for Ki67 in contact or not in contact with tumour cells at baseline.
  • Figure 8. Proliferative fractions of cancer and TME cell phenotypes predict response to immunotherapy a, Schematic illustrating calculation of cell phenotype-specific proliferative fractions (proportion of Ki67+ cells).
  • b,c Odds ratios for associations between proliferative fraction and pCR for epithelial (b) and TME (c) cell phenotypes. Odds ratios are derived from univariate logistic regression: circles represent point estimates and whiskers indicate 95% confidence intervals. Depicted P values are derived from a term for interaction between the predictor and treatment in logistic regression models (including separate terms for the predictor and treatment) and have not been adjusted for multiple tests.
  • Asterisks indicate associations with an FDR ⁇ 0.1 by the Benjamini-Hochberg method, d, Bar charts of the proportion of tumours achieving pCR in patients with no Ki67+ cells of the selected phenotype per arm (0) or increasing proportion of Ki67+ cells per arm, as quantified by tertiles (T1-T3). Absolute numbers of patients in each category are depicted inside bar charts, e, Relationship between proliferative fraction of CD8+ TCF1+ T cells and MHCl&llhi cells. Spearman rank correlation coefficient. Shaded area represents 95% confidence interval of the loess regression line.
  • Figure 9 Cell phenotypes predictive of immunotherapy response early on-treatment, a, Odds ratios for associations between cell density and pCR for epithelial cell phenotypes, b, Boxplot of CD8+ GZMB+ T epithelial cell density across treatment arms and response, c, Odds ratios for associations between cell density and pCR for epithelial cell phenotypes.
  • odds ratios are derived from univariate logistic regression: circles represent point estimates and whiskers indicate 95% confidence intervals.
  • Depicted P values are derived from a term for interaction between the predictor and treatment in logistic regression models (including separate terms for the predictor and treatment) and have not been adjusted for multiple tests.
  • Asterisks indicate associations with an FDR ⁇ 0.1 by the Benjamini-Hochberg method, d, Boxplot of CD15+ cell density across treatment arms and response. For b and d, boxes show 25th, 50th and 75th centiles; whiskers indicate 75th centile plus 1.5 x inter-quartile range and 25th centile less 1 .5 x inter-quartile range; points beyond whiskers are outliers. ***P ⁇ 0.001 , based on two-sided Wilcoxon tests.
  • Multivariate modelling to predict ICB response a, Analytical workflow for predictive modelling using multitiered multiplexed imaging data. Three models were trained using (1) baseline variables alone, (2) on-treatment variables alone or (3) combining baseline and on-treatment variables, b, AUC statistics for prediction probabilities derived from multivariate regularized logistic regression models to predict pCR. AUCs were computed using random held-out test data repeated 100 times, as described in a; circles are mean AUCs, error bars are 95% confidence intervals, c, Diagram illustrating variable importance analysis including all predictors described in a.
  • CD8 + TCF1 + cell or “CD8 + TCF1 + T-cell” will be understood to refer to a T cell which comprises a detectable expression level of CD8 and TCF1. The cell will therefore be understood to be dual positive for CD8 and TCF1 .
  • MHCI + MHCir cell will be understood to refer to a cell which comprises a detectable expression level of MHCI and MHCII. The cell will therefore be understood to be dual positive for MHCI and MHCII.
  • the MHCTMHCir cell may be a cancer cell.
  • the MHCI + MHCH + cell is an epithelial cell.
  • Various markers of epithelial identity are known to the skilled person. These include, but are not necessarily limited to CK5/14, CK8/18, PanCK and AR.
  • the MHCI + MHCH + cell comprises a detectable expression level of one or more of CK5/14, CK8/18, PanCK and AR.
  • the MHCI + MHCH + cell comprises a detectable expression level of CK5/14.
  • the MHCI + MHCH + cell comprises a detectable expression level of CK8/18.
  • the MHCI + MHCH + cell comprises a detectable expression level of PanCK.
  • the MHCI + MHCH + cell comprises a detectable expression level of AR.
  • a detectable expression level will be understood to refer to a detectable amount of mRNA and/or protein of the marker (for example, the proliferative marker, CD8, TCF1 , MHCI or MHCII, depending on the context).
  • the detectable expression level comprises a detectable amount of marker mRNA.
  • the detectable expression level comprises a detectable amount of marker protein.
  • the detectable expression level comprises a detectable amount of marker protein and mRNA.
  • a detectable expression level comprises a detectable expression level above a threshold value.
  • a threshold value may be identified as a quantile threshold.
  • Normalisation of protein expression may be to a housekeeping protein product.
  • the skilled person will be aware of suitable housekeeping genes and products.
  • the expression and/or detectable amount of protein may comprise a normalised value.
  • the expression and/or detectable amount of protein may be detected using flow cytometry. Normalisation of protein expression may be to an isotype control antibody.
  • the expression and/or detectable amount of protein detected using flow cytometry may be quantified by population shift and/or mean fluorescence intensity.
  • RNA-seq The expression and/or detectable amount of mRNA can be detected using RNA-seq methods.
  • RNA-seq methods are commercially available and known to those skilled in the art. RNA-seq methods function by mapping the number of RNA reads aligned to each gene under each biological condition, to obtain a read count. The reads can then be normalised to provide a normalised read count.
  • the proportion of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells which comprise a detectable expression level of a proliferation marker is measured using imaging mass cytometry (IMC) or immunohistochemistry (IHC).
  • the proportion of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells which comprise a detectable expression level of a proliferation marker is measured using imaging mass cytometry (IMC).
  • IMC imaging mass cytometry
  • imaging mass cytometry is an imaging method which utilises cytometry by time-of-flight. In contrast to other imaging methods, imaging mass cytometry utilises metal- tagged antibodies rather than fluorochromes.
  • the proportion of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells which comprise a detectable expression level of a proliferation marker is measured using immunohistochemistry (IHC).
  • immunohistochemistry is a process where proteins in a tissue section are labelled by antibodies specific for the proteins.
  • the antibodies may comprise antibodies specific for the proliferation marker, CD8 and TCF1.
  • the antibodies comprise antibodies specific for the proliferation marker, MHCI and MHCII.
  • the antibodies comprise antibodies specific for the proliferation marker, CD8, TCF1 , MHCI and MHCII.
  • Various methods and reagents for performing IHC are known and commercially available to those skilled in the art.
  • proliferation markers are suitable for use in the present invention.
  • the skilled person will be aware of various proliferation markers.
  • Exemplary proliferation markers may include, but not necessarily be limited to Ki67, proliferating cell nuclear antigen (PCNA) and minichromosome maintenance (MCM) proteins.
  • the proliferation marker comprises Ki67.
  • the inventors have found that the proliferative fraction of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells in a sample from a subject having cancer is predictive of the subject’s response to immunotherapy, specifically to an immune checkpoint inhibitor.
  • immune checkpoint inhibitor or “immune checkpoint inhibitor therapy”, this will be understood to refer to therapies which boost anti-cancer response by specifically targeting immunologic receptors on the surface of T-cells.
  • immunologic receptors on the surface of T-cells.
  • immunologic checkpoint inhibitor may be used interchangeably with “immune checkpoint blockade”.
  • Immune checkpoint inhibitors known in the art include anti-CTLA-4, anti-PD-1 and anti-PD-L1 molecules.
  • the immune checkpoint inhibitor therapy comprises an anti-CTLA-4, anti- PD-1 or anti-PD-L1 molecule.
  • the immune checkpoint inhibitor comprises an anti- PD-1 or anti-PD-L1 molecule.
  • the molecule may comprise an antibody.
  • the immune checkpoint inhibitor therapy comprises an anti-PD-L1 molecule, for example an anti-PD-L1 antibody.
  • the anti-PD-L1 antibody may comprise an anti-PD-L1 monoclonal antibody.
  • An exemplary anti-CTLA-4 antibody is ipilimumab.
  • anti-PD-1 antibodies are known to those skilled in the art. These include nivolumab, pembrolizumab and cemiplimab.
  • anti-PD-L1 antibodies are known to those skilled in the art. These include atezolizumab, avelumab and durvalumab.
  • the immune checkpoint inhibitor therapy comprises atezolizumab.
  • Atezolizumab is a known anti-PD-L1 monoclonal antibody.
  • the cancer may comprise any cancer which has been found to be associated with expression of PD-1 within the tumour microenvironment (TME), for example expression of PD-1 on activated monocytes, dendritic cells (DCs), natural killer (NK) cells, T-cells and/or B-cells.
  • TAE tumour microenvironment
  • PD-1 on activated monocytes, dendritic cells (DCs), natural killer (NK) cells, T-cells and/or B-cells.
  • the cancer comprises breast cancer, ovarian cancer, pancreatic cancer, colorectal cancer, lung cancer, pancreatic cancer, colon cancer, gastric cancer, bladder cancer, skin cancer, myeloma, non-Hodgkin lymphoma, prostate cancer, oesophageal cancer, head and neck cancer, endometrial cancer, hepatobiliary cancer, duodenal carcinoma, thyroid carcinoma, or renal cell carcinoma.
  • the cancer comprises colon, breast, ovarian, lung, skin head and neck or pancreatic cancer.
  • the cancer may comprise lung cancer, skin cancer, head and neck cancer or breast cancer.
  • the lung cancer may comprise non-small-cell lung cancer (NSCLC).
  • the skin cancer may comprise Merkel cell carcinoma (MCC) or melanoma.
  • the head and neck cancer may comprise head and neck squamous cell carcinoma (HNSCC).
  • the cancer comprises breast cancer.
  • the cancer may comprise triple-negative breast cancer.
  • triple-negative breast cancer will be understood to refer to ER; HER2- primary breast cancer.
  • the proliferative fraction profile may comprise a percentage of CD8 + TCF1 + T-cells and/or MHCI + MHCH + cells which comprise a detectable expression level of the proliferative marker.
  • the proliferative fraction profile comprises a percentage of at least 0.1 % of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells which comprise a detectable expression level of the proliferative marker
  • the subject may be predicted to be responsive to treatment with an immune checkpoint inhibitor therapy.
  • the proliferative fraction profile comprises a percentage of at 1 %, optionally at least 5% of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells which comprise a detectable expression level of the proliferative marker, the subject is predicted to be responsive to treatment with an immune checkpoint inhibitor therapy.
  • the proliferative fraction profile comprises a percentage of at least 10% of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells which comprise a detectable expression level of the proliferative marker, the subject may be predicted to be responsive to treatment with an immune checkpoint inhibitor therapy.
  • the proliferative fraction profile comprises a percentage of at least 20% of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells which comprise a detectable expression level of the proliferative marker
  • the subject is predicted to be responsive to treatment with an immune checkpoint inhibitor therapy.
  • the proliferative fraction profile comprises a percentage of at least 30% of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells which comprise a detectable expression level of the proliferative marker
  • the subject is predicted to be responsive to treatment with an immune checkpoint inhibitor therapy.
  • step b) comprises comparing the proliferative fraction profile of the subject to a plurality of reference proliferative fraction profiles and making a prediction of whether the subject will be responsive or non-responsive to treatment based on the comparison.
  • the reference proliferative fraction profiles may comprise the proportion of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells which comprise a detectable expression level of a proliferation marker obtained or derived from a sample of reference subjects.
  • the reference subject is known not to have cancer.
  • the reference subject is a subject known to have and having already been treated for cancer.
  • the sample from the reference subject may be a baseline sample, i.e. a sample obtained prior to the subject starting treatment.
  • the plurality of reference proliferative fraction profiles may be separated into at least two quantiles and the proliferative fraction profile of the subject is compared to the quantiles, wherein the subject is predicted to be responsive to treatment when the proliferative fraction profile of the subject fits into the top quantile.
  • the at least two quantiles comprise at least three quantiles.
  • the upper quantile defines the top 50% of proliferative fraction profiles in a plurality of reference proliferative fraction profiles
  • the lower quantile defines the bottom 50% of proliferative fraction profiles in a plurality of reference proliferative fraction profiles.
  • the at least two quantiles comprise three quantiles.
  • the three quantiles comprise a lower quantile, a middle quantile and an upper quantile.
  • the lower quantile defines the bottom 33.33% of proliferative fraction profiles in a plurality of reference proliferative fraction profiles
  • the middle quantile defines the middle 33.33% of proliferative fraction profiles in a plurality of reference fraction profiles
  • the upper quantiles defines the top 33.33% of proliferative fraction profiles in a plurality of reference fraction profiles.
  • each quantile may otherwise be referred to as a fertile.
  • the proliferative fraction profile of the subject fits into the top (which may otherwise be referred to as “upper” quantile)
  • this may be indicative of the subject having a proliferative fraction which is higher than at least 50%, optionally at least 63% of reference subjects and is therefore predictive of the subject being responsive to immune checkpoint inhibitor therapy.
  • the plurality of reference proliferative fraction profiles comprises at least 150 or at least 200 reference proliferative fraction profiles.
  • Step a) may comprise measuring the proportion of CD8 + TCF1 + T-cells and MHCI + MHCII + cells which comprise a detectable expression level of the proliferation marker to obtain a CD8 + TCF1 + T-cells and MHCI + MHCII + cells proliferative fraction profile of the subject.
  • responsiveness to treatment comprises event-free survival for a time period of at least 18 months, optionally for a time period of at least 24 months. In some embodiments, responsiveness to treatment comprises event-free survival for a time period of at least 36 months, optionally at least 48 months and further optionally at least 60 months. In some embodiments, responsiveness to treatment comprises event-free survival for a time period of at least 10 years. In the context of the present invention, event free survival will be understood to refer the time from treatment of the subject to the first date of disease progression, disease recurrence (local, regional, distant, invasive, other invasive cancers) or death due to any cause, including unknown causes.
  • responsiveness to treatment comprises overall survival for a time period of at least 18 months, optionally for a time period of at least 24 months. In some embodiments, responsiveness to treatment comprises overall survival for a time period of at least 36 months, optionally at least 48 months and further optionally at least 60 months. In some embodiments, responsiveness to treatment comprises overall survival for a time period of at least 10 years. “Overall survival (“OS”), as used herein, will be understood to refer to the time from diagnosis of the subject or the start of treatment of the subject to the time of death.
  • OS Overall survival
  • the human subject may be female.
  • the human subject may be at least 18 years old, optionally at least 30 years old, further optionally at least 40 years old. In some embodiments, the human subject is at least 50 years old.
  • the sample may be a cell or tissue sample (e.g. a biopsy) or a biological fluid.
  • the sample may comprise tumour tissue, for example a breast tumour (primary or secondary).
  • the sample will generally comprise cells.
  • the sample may comprise blood or plasma.
  • the sample may be one which has been freshly obtained from the subject or may be one which has been processed and/or stored prior to making a determination (e.g. frozen, fixed or subjected to one or more purification, enrichment or extractions steps).
  • the sample is a fixed tumour tissue sample (such as e.g. a formalin-fixed paraffin-embedded (FFPE) tissue sample), or a frozen tumour tissue sample (such as e.g. a fresh frozen (FF) tissue sample).
  • FFPE formalin-fixed paraffin-embedded
  • FF fresh frozen
  • the preferred sample type according to the present invention is a FFPE tissue sample, as this type of sample is widely available. Indeed, FFPE tissue samples are commonly obtained in clinical settings, for example for histopathological diagnosis.
  • the sample may have been obtained from the subject prior to any treatment.
  • the inventors have advantageously found that the proliferative fractions obtained from samples obtained prior to any treatment have increased predictive value compared to proliferative fractions obtained from samples obtained later.
  • the sample was obtained from the subject prior to surgery and/or chemotherapy.
  • the sample was obtained from the subject during or after chemotherapy.
  • the sample was obtained from the subject prior to radiotherapy, optionally prior to radiotherapy and chemotherapy.
  • the sample comprises a plurality of samples obtained from the subject at different time points.
  • the sample may comprise at least two samples, comprising a first sample obtained from the subject prior to treatment starting and a second sample obtained from the subject during treatment.
  • the subject is predicted to be responsive to the immune checkpoint inhibitor therapy, and wherein the method further comprises the step of administering a therapeutically effective amount of an immune checkpoint inhibitor.
  • the method may further comprise steps of administering a therapeutically effective amount of an immune checkpoint inhibitor and radiotherapy.
  • the immune checkpoint inhibitor and radiotherapy may be administered concurrently, sequentially or separately.
  • the method may further comprise steps of administering a therapeutically effective amount of an immune checkpoint inhibitor and surgery to excise the tumour.
  • the immune checkpoint inhibitor may be administered with a further anti-cancer therapy and/or surgery to excise the tumour.
  • exemplary anti-cancer therapies include, but are not necessarily limited to cyclin inhibitors, endocrine therapy, tyrosine kinase inhibitors, TORC1/2 active drugs, chemotherapy and radiotherapy.
  • the further anti-cancer therapy may comprise one or more of cyclin inhibitors, endocrine therapy, tyrosine kinase inhibitors, TORC1/2 active drugs, chemotherapy, radiotherapy and surgery to excise the tumour.
  • the immune checkpoint inhibitor may be administered as part of a combination therapy with chemotherapy, optionally wherein the chemotherapy comprises carboplatin and/or nab-paclitaxel.
  • the immune checkpoint inhibitor is administered as part of a combination therapy with chemotherapy prior to or after surgery to excise the tumour.
  • the immune checkpoint inhibitor is administered as part of a combination therapy with one or more of cyclin inhibitors, endocrine therapy, tyrosine kinase inhibitors and TORC1/2 active drugs prior to or after surgery to excise the tumour.
  • the immune checkpoint inhibitor may be as described herein.
  • the immune checkpoint inhibitor may comprise an anti-CTLA-4, anti- PD-1 or anti-PD-L1 molecule.
  • the immune checkpoint inhibitor comprises an anti- PD-1 or anti-PD-L1 molecule.
  • the immune checkpoint inhibitor therapy comprises an anti-PD-L1 molecule, for example an anti-PD-L1 antibody.
  • the anti-PD-L1 antibody may comprise an anti-PD-L1 monoclonal antibody.
  • the immune checkpoint inhibitor may comprise atezolizumab.
  • the immune checkpoint inhibitor is administered concurrently with chemotherapy, for example, carboplatin and/or nab-paclitaxel.
  • the method comprises concurrent, sequential or separate administration of: a) atezolizumab; and b) carboplatin and/or nab-paclitaxel.
  • the method comprises concurrent administration of atezolizumab, carboplatin and nab-paclitaxel.
  • the immune checkpoint inhibitor may be administered every three weeks. In some embodiments, the immune checkpoint inhibitor is administered for eight cycles.
  • the subject is predicted to be non-responsive to immune checkpoint inhibitor therapy, and wherein the method further comprises administering chemotherapy to the subject in the absence of any immune checkpoint inhibitor therapy.
  • the chemotherapy may be any suitable chemotherapy.
  • the chemotherapy may comprise carboplatin and/or nab-paclitaxel.
  • the method may further comprise administering radiotherapy to the subject in the absence of any immune checkpoint inhibitor therapy.
  • the method may further comprise surgically removing the tumour in the absence of any immune checkpoint inhibitor therapy.
  • any combination of treatment may be envisaged (for example chemotherapy, surgery, radiotherapy, cyclin inhibitors, endocrine therapy, tyrosine kinase inhibitors, TORC1/2 active drugs or any combination thereof), provided that an immune checkpoint inhibitor is not administered to the subject.
  • the present invention provides a computer-implemented method for predicting whether a human subject having cancer will be responsive to treatment with an immune checkpoint inhibitor, the method comprising: a) Obtaining proliferative fraction profile data representing the proportion of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells which comprise a detectable expression level of a proliferation marker in a sample obtained from the subject, and b) making a prediction of whether the subject will be responsive or non-responsive to treatment with an immune checkpoint inhibitor therapy based on the proliferative fraction profile data of the subject.
  • the sample obtained from the subject may comprise a tumour tissue sample.
  • the sample was obtained from the subject prior to the subject having any treatment.
  • the sample comprises a plurality of samples obtained from the subject at different time points.
  • the sample may comprise at least two samples, comprising a first sample obtained from the subject prior to treatment starting and a second sample obtained from the subject during treatment.
  • Step b) may comprise providing the proliferative fraction profile data as input to a machine learning model comprising a classifier trained on a training data set comprising reference proliferative fraction profile data from at least one reference subject.
  • the reference proliferative fraction profile data and reference subject may be as defined in the above aspects.
  • step b) comprises providing the proliferative fraction profile data as input to a machine learning model comprising a classifier trained on a training data set comprising reference proliferative fraction profile data from at least one reference subject and receiving an output from the machine learning model, the output indicative of whether the subject will be responsive or non-responsive to treatment with an immune checkpoint inhibitor therapy.
  • the output may comprise a probabilistic score for whether the subject will be responsive or non- responsive to treatment with an immune checkpoint inhibitor therapy.
  • the present invention also provides an immune checkpoint inhibitor for use in a method of treating cancer, wherein the method comprises: a) measuring the proportion of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells which comprise a detectable expression level of a proliferation marker in a sample obtained from the subject to obtain a CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells proliferative fraction profile of the subject; b) making a prediction of whether the subject will be responsive or non-responsive to therapy with an immune checkpoint inhibitor based on the proliferative fraction profile of the subject; and c) treating the subject with the immune checkpoint inhibitor if the subject is predicted to be responsive to therapy in b).
  • the immune checkpoint inhibitor may be as defined in relation to the first aspect.
  • the immune checkpoint inhibitor may comprise an anti-CTLA-4, anti-PD-1 or anti-PD-L1 molecule.
  • the immune checkpoint inhibitor may comprise atezolizumab.
  • Step c) may comprise treating the subject with the immune checkpoint inhibitor and chemotherapy if the subject is predicted to be responsive to therapy in b).
  • the chemotherapy may be as defined in relation to any of the above aspects.
  • the subject may be predicted to be responsive to therapy with an immune checkpoint inhibitor when the proliferative fraction profile of the subject comprises a percentage of at least 10%, at least 20%, at least 30%, at least 40% or at least 50% of CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells, which comprise a detectable expression level of the proliferative marker.
  • a method of treating cancer comprising administering an immune checkpoint inhibitor to a subject that has been determined to be responsive to the immune checkpoint inhibitor based on a CD8 + TCF1 + T-cells and/or MHCI + MHCII + cells proliferative fraction profile of the subject.
  • Administration of the immune checkpoint inhibitor may be as defined in any of the above aspects.
  • the immune checkpoint inhibitor may comprise atezolizumab.
  • the immune checkpoint inhibitor may be administered with chemotherapy.
  • EXAMPLE 1 Identification of pCR predictors in atezolizumab treatment of breast cancer
  • ICB Interactions between proximate specialized cells in distinct activation states underpin the effect of ICB.
  • chronic T cell stimulation leads to dysfunction, owing to interactions between cells expressing immune checkpoint receptors and ligands.
  • ICB prevents this to invigorate dysfunctional T cells and these invigorated T cells must then interact with target cancer cells to induce cell death.
  • the efficacy of ICB therefore depends on both the cellular composition and multicellular organization of tumours because they orchestrate these interactions.
  • Breast tumours are heterocellular ecosystems of cancer and tumour microenvironment (TME) cells that self-organize as distinct, recurring multicellular structures. Despite this, the relationship between phenotypic spatial organization of tumours and ICB response has been little explored.
  • multicellular organization pre-treatment may indicate whether the immune response can be augmented by ICB, how ICB remodels tissue structure to achieve this remains obscure.
  • Serial tumour sampling before, during and after treatment could uncover treatment-induced remodelling but is challenging in routine clinical practice. This may explain why the relationship between tissue dynamics during treatment and response is unknown.
  • imaging mass cytometry IMC was used to precisely quantify the phenotype, activation state and spatial location of cells in tumours sampled at three timepoints from patients enrolled in a randomized trial of neoadjuvant immunotherapy.
  • NeoTRIPaPDLl or NeoTRIP was a neoadjuvant immunotherapy trial of early high-risk TNBC in which 280 patients were randomized to receive neoadjuvant carboplatin and nab-paclitaxel on days 1 and 8, with or without atezolizumab (anti-PD-L1) on day 1. This treatment was given every 3 weeks (one cycle), for a total of eight cycles.
  • Tumours were subsequently surgically excised and, if the responsible clinician opted to do so, an additional four cycles of a nth ra cy clines were given. Patients with treatment-naive, early high-risk TNBC were eligible. Tumour receptor and PD-L1 status (by SP142, Ventana Medical Systems) were determined by central pathology review. Tumour infiltrating lymphocytes were also assessed using established methods. The study protocol was approved at each participating institution; all patients provided written, informed consent. Core biopsies for research were obtained at baseline and after one cycle of therapy (first day of second treatment cycle; on-treatment). Following the full course of therapy, tumours were surgically removed (post-treatment).
  • TMAs Tissue microarrays
  • Base call files from each sequencing run were converted to fastq format using bcl2fastq conversion software v.2.20, replicate fastq files for each sample were merged and files were aligned to the Ensembl GRCh37 Homo sapiens reference using STAR v.2.5.2 (Dobin et al., 2013).
  • Transcript assembly and expression analysis were performed on each sample with cufflinks v.2.2.1 (Trapnell et al., 2012), resulting in fragments per kilobase million (FPKM) values for each transcript in the genes of interest.
  • the TNBC subtypes were determined using the minimal 101 -gene TNBCtype.
  • Candidate commercial antibodies intended for use in IMC were first validated by immunofluorescence using tonsil and breast cancer tissue to confirm optimal staining intensity, specificity and signal-to-noise ratio. Antibodies that passed validation by immunofluorescence were conjugated to metal isotopes and validated using IMC to ensure preservation of staining specificity and intensity. Sensitivity and specificity were further validated in multiplexed IMC experiments to ensure appropriate patterns of marker colocalization. Finally, optimal concentrations of all metal-conjugated antibodies were determined by visual inspection of IMC images in both tonsil and breast cancer tissue. Antibody conjugation
  • Indium, yttrium and lanthanide metals were conjugated to antibodies according to the manufacturer’s instructions (Maxpar X8 Multi-Metal Antibody Labelling Kit, Fluidigm). Platinum isotopes were conjugated directly to the reduced antibody without the polymer. Conjugation of bismuth to the antibody required substitution of the L buffer from the Maxpar X8 labelling kit with 5% nitric acid (HNO3) during loading of the metal onto the polymer and deionized water (MilliQ) during the washes. Metal-tagged antibodies were stored in a Candor Antibody Stabilizer (Candor Biosciences) at 4 °C.
  • FFPE slides were dewaxed in xylene and rehydrated in an alcohol gradient. Tissue underwent antigen retrieval (Tris pH 9.0, 95 °C for 30 min) before blocking with 3% BSA in TBS for 1 h. Slides were incubated with unconjugated primary antibodies (PD-L1 clone SP142, PD-1 clone NAT105) overnight at 4 °C, then with metal-conjugated secondary anti-mouse and anti-rabbit antibodies for 3 h at room temperature. Next, slides were incubated with the remainder of the metal-tagged antibodies overnight at 4 °C, then with 0.5 pM iridium for DNA detection (Fluidigm, 201192B) for 30 min. Slides were washed with TBS 0.1 % Tween between each labelling step, and air-dried following the final incubation.
  • FFPE tissue Two sequential sections of FFPE tissue were prepared from core biopsy (baseline and on-treatment) and TMA blocks (post-treatment). One was stained with H&E using an autostainer (Leica ST5020 Stainer/TS5025 Transfer Station/CV5030 Coverslipper Workstation). H&E slides were scanned using the Leica Aperio AT2 Automated Digital Whole Slide Scanner. For core biopsies, ROIs measuring 500 x 500 pm2 were identified by a breast pathologist for acquisition by IMC using the Aperio eSlideManager web application (Leica Biosystems). Three ROIs were selected for each sample unless the biopsy was too small or, for baseline samples, contained no tumour cells.
  • ROIs were mapped by manual inspection of annotated H&E images and subjected to IMC (Fluidigm): tissue was raster laser-ablated at 1 pm resolution, then ablated tissue aerosol was ionized using inductively coupled plasma, and resulting isotopic ion reporters quantified using time-of-flight mass spectrometry to infer protein abundance.
  • IMC Fluiddigm
  • a ‘spillover matrix’ quantifying crosstalk was generated using the Bioconductor CATALYST41 package and subsequently used to correct single-cell measurements.
  • Raw txt file data were converted into multistack image tiff files using existing software (Zanotelli et al., 2017) .
  • To identify regions of contiguous epithelium we labelled pixels as epithelial based on their expression of cytokeratins and used a random-forest pixel classifier (llastik, Berg et al., 2019) to assign all remaining pixels a probability of belonging to an epithelial region.
  • Probability maps were saved as a red, green and blue (RGB) tiff file, and epithelial regions segmented to generate image masks using standard segmentation tools.
  • RGB red, green and blue
  • Nuclei were mapped to whole-cell regions, and whole cells mapped to epithelial masks. To be considered ‘related’ to an epithelial mask, at least 30% of the pixels from a whole cell had to overlap with the epithelial region.
  • multistack tiff files were filtered for single hot pixels.
  • Single-cell proteomic measurements were taken by computing the mean ion count for each segmented whole cell; these data were spillover corrected using the spillover matrix described above with a nonnegative least squares linear model implemented in CATALYST (Chevrier et al., 2018). Small objects (cells with area less than 31 pm2 ) were excluded from analyses. We left one platinum isotopic channel empty for detection of carboplatin.
  • Carboplatin signal detected in other platinum isotopes, for which conjugated antibodies were included was corrected by fitting a linear model to all cells: log-transformed cell expression was predicted using the carboplatin isotope, and the resulting model residuals were taken as corrected values.
  • Cell phenotypes were assigned by semi-supervised clustering. Cells were first classified as epithelial or TME using multiple classification methods, in which the best performing method for each image was manually selected by visual inspection of tissue morphology and cytokeratin expression. The classification methods are listed below. A two-component Gaussian mixture model was fit to the log- transformed sum of all cytokeratins (panCK, CK8/18, CK5/14) to distinguish cells as positive or negative for cytokeratin. Cells related to epithelial masks based on a 30% area overlap were deemed ‘mask- positive’. All images were annotated with mask-positive, cytokeratin-positive or double-positive cells (those that were positive by both criteria).
  • Tumours poorly classified by these approaches were subjected to unsupervised clustering by Phenograph (Levine et al., 2015) per image. Every marker (except for DNA, H3, Carboplatin, c-PARP, CD68, Calponin and Caveolin) was used for clustering, and values were rescaled to lie between zero and one per image.
  • Clusters were categorized as either epithelial or TME by manual inspection of annotated images. Cell phenotypes were derived separately for epithelial and TME cells. Only proteins known to be expressed by epithelial or TME cells based on previous knowledge or manual inspection were included. The proliferation marker Ki67 was excluded from cell clustering.
  • Expression values were clipped at the 99th centile, mean centred and scaled before clustering.
  • Clustering was performed in two steps. First, a selforganizing map (SOM) was created using GigaSOM46 (Kratochvil et al., 2020), then median expression values per SOM node were passed to Phenograph45 and resulting clusters mapped back to single cells. Heatmaps of scaled median expression values were inspected, and clusters lacking meaningful differences merged. Resulting clusters were labelled based on their expression profiles. Cluster validity was further investigated by inspecting images annotated with cluster labels and expression profiles to ensure cell morphology and expression values were concordant with the cluster label. Extensive image curation was conducted under the supervision of a pathologist to identify invasive cancer cells and exclude in situ or normal epithelial cells from clinical correlative analyses. All TME cells were retained for downstream analysis.
  • Cell phenotype densities were calculated by dividing the number of total cells obtained per biopsy by the total area of the tissue acquired (per mm2 ; Fig. 3a). As tissue did not cover the entirety of all ROIs, the convex hull method was used to draw a ‘tissue’ area based on the existence of all segmented cells.
  • Cells were defined as participating in an interaction if their whole-cell masks were in direct contact (that is, their pixels were contiguous. Taking direct contact as the criterion, we computed interactions for all cells using CellProfiler. Cell phenotypes were mapped to cell-cell interaction maps.
  • the homotypic interactions for an epithelial cell phenotype of interest were computed as the total number of interactions between that phenotype and all other epithelial cells (regardless of phenotype), divided by the total number of cells in the tumour sample (epithelial and TME cells combined).
  • the epithelial heterotypic interactions for a TME phenotype of interest were computed as the number of epithelial-TME interactions (with that TME phenotype) divided by the total number of cells.
  • the definitions for TME- centric interactions were the same but computed from the perspective of the TME (heterotypic interactions were with different epithelial cell phenotypes).
  • the proportion of cells positive for the proliferation marker Ki67 was computed per cell phenotype per tumour per timepoint (Fig. 4). When a cell phenotype was absent, its corresponding proliferative fraction was also zero. Ki67 status per cell was determined using the method described above to find a suitable threshold for positivity.
  • Plots illustrating estimates of association between tissue features and pCR depict two odds ratios (and 95% confidence intervals) per predictor: one for each treatment arm, resulting from a univariate logistic regression model restricted to the relevant (C or C&l) study population. Adjacent to these two odds ratios, Rvalues for statistical interaction (Pinteraction, are depicted: these were derived from trivariate logistic regression models that included the feature of interest, treatment (C or C&l) and a term for statistical interaction between the feature of interest and treatment. Pinteraction values corresponded to the statistical term for interaction computed in these models.
  • Regularized logistic regression models were fitted to the training set (using cross validation to identify the minimal shrinkage factorlambda) and predictions made using the test data.
  • An AUC statistic was computed using the prediction probabilities in the test data.
  • an established feature selection algorithm (implemented in the R package Boruta) 2 o.
  • the principle of this method is repeated comparison of true values with randomly shuffled features to identify which outperform random data more often than would occur by chance.
  • all predictors are randomly shuffled, doubling the original predictor set (the original plus the shuffled data), and a random-forest classifier fitted to determine the importance of all predictors in the doubled dataset (importance is calculated by replacing a feature with its randomly permuted equivalent and computing the resulting percentage of misclassified tumours, and then scaled by dividing by the standard error from all misclassification rates).
  • the maximum feature importance achieved among all the randomly shuffled predictors is the threshold for a true feature to then be deemed important (on the basis that an important predictor must outperform random equivalents).
  • IMC formalin-fixed paraffin-embedded
  • NeoTRIP randomized controlled trial (Fig. 1).
  • NeoTRIP was a trial of early TNBC that compared neoadjuvantchemotherapy (carboplatin and nab-paclitaxel) with chemotherapy plus anti-PD-L1 immunotherapy (carboplatin, nab-paclitaxel and atezolizumab) by 1 :1 randomization.
  • IMC uses laser ablation and time-of-flight mass spectrometry to detect antibodies conjugated to rare earth metal reporters to infer protein abundance at subcellular resolution.
  • Our approach successfully generated 1 ,855 high-plex tissue images from both biopsies and excisions using FFPE samples collected prospectively as part of a randomized trial.
  • Fig. 2 To precisely characterize cell phenotypes in situ, we segmented single cells using deep learning and derived proteomic profiles. For cell phenotyping, we separated epithelial (cancer cells) and TME cells using several methods (Fig. 2) and, taking cell morphology as the standard, selected the best performing. To discover salient cell phenotypes, we clustered single cells, limiting the proteins used for clustering to those relevant to epithelial or TME cells. Cell clustering resulted in 17 epithelial and 20 TME phenotypes. Epithelial cells were distinguished by markers of lineage, activation state and immunoregulation.
  • TME cells cytotoxic and helper T cells separated according to expression of PD-1 and TCF1 (encoded by TCF7) which identifies stem-like T-cells.
  • Treg regulatory T
  • CD8+ GZMB+ T activated cytotoxic T cells with high granzyme B expression
  • APCs CD11c+ antigen presenting cells
  • B cells plasma cells, macrophages, dendritic cells, neutrophils, endothelial cells and three fibroblast phenotypes.
  • PD-L1 status assessed by centralized pathology review we confirmed that PD-L1 expression by IMC was greater in PD-L1 -positive tumours.
  • the cell phenotypes most enriched among PD-L1 -positive tumours were characterized by the highest expression of PD-L1 .
  • All lymphoid cell phenotypes were also positively associated with PD-L1 status at baseline.
  • Stromal infiltrating lymphocytes were also positively correlated with all lymphoid cells and PD-L1+ APCs.
  • the luminal androgen receptor subtype for example, was characterized by the highest proportion of epithelial cells positive for androgen receptor (AR+ LAR), and the mesenchymal tumour subtype contained the greatest proportion of all three stromal cell phenotypes.
  • pCR pathological complete response
  • FDR false discovery rate
  • T cells in contact with cancer cells were functionally distinct from other T cells.
  • contact with a cancer cell was associated with higher expression of key activation markers (TOX and PD-1 for cytotoxic T cells; TOX and 0X40 for T helper cells; Fig. 7a), and that T cells contacting cancer cells were much more likely to be proliferating (Fig. 7b).
  • proliferating CD8+ TCF1 + T cells showed significantly higher levels of all key activation markers (TOX, PD-1 , GZMB, ICOS, Helios) and were more often in contact with cancer cells and with MHCII+ cells. Pre-treatment proliferative fractions therefore enrich for cells in distinct activation states and identify phenotypes that predict ICB response.
  • CKhi cells suggestive of discrete phenotypic state transitions.
  • foci of CD15+ cancer cells surrounded by CD15+ leukocytes, implicating heterotypic interactions as possible drivers of state transition.
  • proliferation was largely preserved on-treatment, proliferation itself was reduced and proliferating cell fractions were not predictive of response.
  • markers of outcome on-treatment were distinct from those in treatment-naive tumours.
  • Response to immunotherapy was characterized by accumulation of CD8+ GZMB+ T cells, whereas heterotypic CD79a+ Plasma cell interactions and CD15+ cancer cells marked resistant tumours.
  • TNBC triple negative breast
  • TNBC TNBC status was assessed in a central laboratory of pathology to characterize estrogen and progesterone receptor expression, erbB2 status and PD-L1 expression.
  • the primary endpoint of the study is event free survival defined as the time from randomization to the first date of disease progression while on primary therapy or disease recurrence (local, regional, distant, invasive contralateral breast, other invasive cancers) after surgery or death due to any cause, including unknown causes.
  • secondary endpoint there are safety and response assessed at surgery to compare rates of pathologic complete response (pCR; ypTO/Tis ypNO).
  • EFS EFS-L1 expression and higher sTILs at baseline were prognostic for better EFS, but not predictive of atezolizumab benefit.
  • Table 1 Multi-variate analysis of hazard ratio analysis of EFS. A HR less than 1 indicated a positive association with EFS. A HR greater than 1 indicated a negative association with EFS (i.e., a positive association with disease progression).
  • Example 1 demonstrates that that the density of interaction between selected activated immune cells and epithelial cancer cells at different locations in the tumour specimen is strongly associated with the probability of achieving pCR with the addition of the ICI.
  • the key aspect of the observation is that the prediction is restricted to immunotherapy treatment, while the same predictors are not linked to the probability of pCR with chemotherapy only.
  • Example 1 It was then decided to test several biomarkers that were identified in Example 1 as being associated with pCR following atezolizumab treatment.
  • the four biomarker profiles identified as having the highest statistical predictive power for predicting pCR in atezolizumab were assessed to determine if there was an association with EFS.
  • the four profiles were:
  • CD8 + and GZMBT + i.e., dual expression of CD8 and GZMBT
  • MHC I and ll + and Ki67 + i.e., triple expression of MHC I and II and Ki67
  • CD8 + TCF1 + and Ki67 + i.e., triple expression of CD8, TCF1 , and Ki67
  • a cell would be considered to meet the fourth profile above if it expressed detectable levels of CD8, TCF1 , and Ki67.
  • Neoadjuvant durvalumab improves survival in early triple-negative breast cancer independent of pathological complete response.
  • le 3 Hazard ratio analysis of LOW and HIGH quantiles of biomarker profiles MHC I and H + and Ki67 + and CD8 + TCF1 + and Ki67 + in relation to ociation with EFS.
  • a HR less than 1 indicated a positive association with EFS.
  • a HR greater than 1 indicated a negative association with EFS (i.e., a itive association with disease progression).

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Immunology (AREA)
  • Chemical & Material Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Medicinal Chemistry (AREA)
  • Hematology (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Urology & Nephrology (AREA)
  • Cell Biology (AREA)
  • Biomedical Technology (AREA)
  • Biochemistry (AREA)
  • Microbiology (AREA)
  • Organic Chemistry (AREA)
  • Analytical Chemistry (AREA)
  • Pharmacology & Pharmacy (AREA)
  • Pathology (AREA)
  • Veterinary Medicine (AREA)
  • General Physics & Mathematics (AREA)
  • Public Health (AREA)
  • Physics & Mathematics (AREA)
  • Food Science & Technology (AREA)
  • Animal Behavior & Ethology (AREA)
  • Biotechnology (AREA)
  • Oncology (AREA)
  • Genetics & Genomics (AREA)
  • Mycology (AREA)
  • Biophysics (AREA)
  • Hospice & Palliative Care (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Epidemiology (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • General Chemical & Material Sciences (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Zoology (AREA)
  • Tropical Medicine & Parasitology (AREA)
  • Virology (AREA)
  • Medicines That Contain Protein Lipid Enzymes And Other Medicines (AREA)

Abstract

La présente invention concerne des méthodes permettant de prédire si un sujet humain atteint d'un cancer répondra au traitement par un inhibiteur de point de contrôle immunitaire. La méthode consiste à mesurer la proportion de cellules T CD8+TCF1+ de cellules T et/ou de cellules MHCI+MHCII+ qui comprennent un niveau d'expression détectable d'un marqueur de prolifération dans un échantillon prélevé chez le sujet, afin d'obtenir un taux de cellules T CD8+TCF1+ et/ou des cellules MHCI+MHCII+ du sujet, et de prédire si le sujet répondra ou non à un traitement par inhibiteur de point de contrôle immunitaire sur la base du profil de fraction proliférative du sujet. L'invention concerne également des méthodes de traitement du cancer comprenant l'administration d'un inhibiteur de point de contrôle immunitaire à un sujet qui a été déterminé comme répondant à l'inhibiteur de point de contrôle immunitaire sur la base d'un profil de fraction proliférative des cellules T CD8+TCF1+ et/ou des cellules MHCI+MHCII+ du sujet.
PCT/EP2024/079371 2023-10-19 2024-10-17 Biomarqueurs pour prédiction de réponse à l'immunothérapie Pending WO2025083140A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
IT102023000021825 2023-10-19
IT202300021825 2023-10-19

Publications (1)

Publication Number Publication Date
WO2025083140A1 true WO2025083140A1 (fr) 2025-04-24

Family

ID=89308092

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2024/079371 Pending WO2025083140A1 (fr) 2023-10-19 2024-10-17 Biomarqueurs pour prédiction de réponse à l'immunothérapie

Country Status (1)

Country Link
WO (1) WO2025083140A1 (fr)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018209324A2 (fr) * 2017-05-11 2018-11-15 The Broad Institute, Inc. Procédés et compositions d'utilisation de sous-types de lymphocytes infiltrant les tumeurs cd8 + et leurs signatures géniques
WO2022066945A1 (fr) * 2020-09-23 2022-03-31 The Trustees Of Columbia University In The City Of New York Augmentation métabolique pour favoriser et améliorer la réponse immunitaire par repopulation de lymphocytes t tcf1+

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018209324A2 (fr) * 2017-05-11 2018-11-15 The Broad Institute, Inc. Procédés et compositions d'utilisation de sous-types de lymphocytes infiltrant les tumeurs cd8 + et leurs signatures géniques
WO2022066945A1 (fr) * 2020-09-23 2022-03-31 The Trustees Of Columbia University In The City Of New York Augmentation métabolique pour favoriser et améliorer la réponse immunitaire par repopulation de lymphocytes t tcf1+

Non-Patent Citations (22)

* Cited by examiner, † Cited by third party
Title
BERG, S. ET AL.: "ilastik: interactive machine learning for (bio)image analysis.", NAT. METHODS, vol. 16, 2019, pages 1226 - 1232, XP036929815, DOI: 10.1038/s41592-019-0582-9
BIANCHINI ET AL.: "Treatment landscape of triple-negative breast cancer - expanded options, evolving needs", NAT REVCLIN ONCOL, vol. 19, no. 2, 2022, pages 91 - 113, XP037675445, DOI: 10.1038/s41571-021-00565-2
CHEVRIER, S. ET AL.: "Compensation of signal spillover in suspension and imaging mass cytometry", CELL SYSTEMS, vol. 6, 2018, pages 612 - 620
CORTAZAR, P. ET AL.: "Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis", THE LANCET, vol. 384, no. 9938, 2014, pages 164 - 172
DOBIN, A. ET AL.: "STAR: ultrafast universal RNA-seq aligner", BIOINFORMATICS, vol. 29, 2013, pages 15 - 21, XP055500895, DOI: 10.1093/bioinformatics/bts635
GIANNI ET AL.: "Pathologic complete response (pCR) to neoadjuvant treatment with or without atezolizumab in triple-negative, early high-risk and locally advanced breast cancer: NeoTRIP Michelangelo randomized study", ANNALS ONCOL., vol. 33, 2022, pages 534 - 543
GREENWALD, N. F. ET AL.: "Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning", NAT. BIOTECHNOL., 2021, Retrieved from the Internet <URL:https://doi.org/10.1038/s41587-021-01094-0>
KRATOCHVIL, M. ET AL.: "GigaSOM.jl: high-performance clustering and visualization of huge cytometry datasets", GIGASCIENCE, vol. 9, 2020, pages 127
LEVINE, J. H. ET AL.: "Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis", CELL, vol. 162, 2015, pages 184 - 197, XP055403730, DOI: 10.1016/j.cell.2015.05.047
LOIBL ET AL.: "A randomised phase II study investigating durvalumab in addition to an anthracycline taxane-based neoadjuvant therapy in early triple-negative breast cancer: clinical results and biomarker analysis of GeparNuevo study", ANN ONCOL., vol. 30, no. 8, 1 August 2019 (2019-08-01), pages 1279 - 1288
LOIBL ET AL.: "Neoadjuvant durvalumab improves survival in early triple-negative breast cancer independent of pathological complete response", ANN ONCOL., vol. 33, no. 11, November 2022 (2022-11-01), pages 1149 - 1158
MITTENDORF ET AL.: "Neoadjuvant atezolizumab in combination with sequential nab-paclitaxel and anthracycline-based chemotherapy versus placebo and chemotherapy in patients with early-stage triple-negative breast cancer (IMpassion031): a randomised, double-blind, phase 3 trial", LANCET, vol. 396, no. 10257, 10 October 2020 (2020-10-10), pages 1090 - 1100, XP086286144, DOI: 10.1016/S0140-6736(20)31953-X
PROWELL T.M.R PAZDUR: "Pathological Complete Response and Accelerated Drug Approval in Early Breast Cancer", NEW ENGLAND JOURNAL OF MEDICINE, vol. 366, no. 26, 2012, pages 2438 - 2441
RIZZO ET AL.: "KEYNOTE-522, IMpassion031 and GeparNUEVO: changing the paradigm of neoadjuvant immune checkpoint inhibitors in early triple-negative breast cancer", FUTURE ONCOL., vol. 18, no. 18, June 2022 (2022-06-01), pages 2301 - 2309
SADE-FELDMAN MOSHE ET AL: "Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma", CELL, ELSEVIER, AMSTERDAM NL, vol. 175, no. 4, 1 November 2018 (2018-11-01), pages 998, XP085522260, ISSN: 0092-8674, DOI: 10.1016/J.CELL.2018.10.038 *
SAMBROOK, J.RUSSEL, D.W.: "Molecular Cloning, A Laboratory Manual", 2001, COLD SPRING HARBOR LABORATORY PRESS
SCHMID ET AL.: "Event-free Survival with Pembrolizumab in Early Triple-Negative Breast Cancer", N ENGL J MED., vol. 386, no. 6, 10 February 2022 (2022-02-10), pages 556 - 567
SCHMID ET AL.: "Pembrolizumab for Early Triple-Negative Breast Cancer", N ENGL J MED., vol. 382, no. 9, 27 February 2020 (2020-02-27), pages 810 - 821, XP093159692, DOI: 10.1056/NEJMoa1910549
SIDDIQUI IMRAN ET AL: "Intratumoral Tcf1+PD-1+CD8+ T Cells with Stem-like Properties Promote Tumor Control in Response to Vaccination and Checkpoint Blockade Immunotherapy", IMMUNITY, vol. 50, no. 1, 15 January 2019 (2019-01-15), AMSTERDAM, NL, pages 195 - 211, XP055924034, ISSN: 1074-7613, DOI: 10.1016/j.immuni.2018.12.021 *
TRAPNELL, C. ET AL.: "Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks", NAT. PROTOC., vol. 7, 2012, pages 562 - 578, XP055175849, DOI: 10.1038/nprot.2012.016
XIAO QIAN WANG: "Spatial predictors of immunotherapy response in triple-negative breast cancer", vol. 621, no. 7980, 28 September 2023 (2023-09-28), pages 868 - 876, XP093157185, ISSN: 0028-0836, Retrieved from the Internet <URL:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10533410/pdf/41586_2023_Article_6498.pdf> DOI: 10.1038/s41586-023-06498-3 *
ZANOTELLI, V. R.BODENMILLER, B., IMCSEGMENTATIONPIPELINE: A PIXEL CLASSIFICATION BASED MULTIPLEXED IMAGE SEGMENTATION PIPELINE, 2017, Retrieved from the Internet <URL:https://github.com/BodenmillerGroup/lmcSegmentationPipeline>

Similar Documents

Publication Publication Date Title
Wang et al. Spatial predictors of immunotherapy response in triple-negative breast cancer
US20220390451A1 (en) Single cell genomic profiling of circulating tumor cells (ctcs) in metastatic disease to characterize disease heterogeneity
Balic et al. Circulating tumor cells: from bench to bedside
Mete et al. Diagnostic and prognostic biomarkers of adrenal cortical carcinoma
Flood‐Knapik et al. Clinical, histopathological and immunohistochemical characterization of canine indolent lymphoma
Charafe‐Jauffret et al. Immunophenotypic analysis of inflammatory breast cancers: identification of an ‘inflammatory signature’
McDaniel et al. Phenotypic diversity of circulating tumour cells in patients with metastatic castration‐resistant prostate cancer
Cornejo et al. Theranostic and molecular classification of breast cancer
EP3100047B1 (fr) Diagnostics de cellules tumorales circulantes pour biomarqueurs du cancer de la prostate
EP3022561B1 (fr) Procédé de pronostic de la durée de survie d&#39;un patient souffrant d&#39;un cancer solide
EP3631445A1 (fr) Méthodes de détermination de thérapies sur la base d&#39;une caractérisation cellulaire unique de cellules tumorales circulantes (ctc) dans une maladie métastatique
Rao et al. Transgelin: A potentially useful diagnostic marker differentially expressed in triple-negative and non–triple-negative breast cancers
Huvila et al. Combined ASRGL1 and p53 immunohistochemistry as an independent predictor of survival in endometrioid endometrial carcinoma
CN114787374A (zh) 基于对治疗的分子反应的治疗方法
US20050221398A1 (en) Protein expression profiling and breast cancer prognosis
Kock Am Brink et al. Intratumoral heterogeneity affects tumor regression and Ki67 proliferation index in perioperatively treated gastric carcinoma
Eskelinen et al. The role of nucleolar organiser regions as prognostic factors in breast cancer
Magi-Galluzzi et al. The 17-gene genomic prostate score assay predicts outcome after radical prostatectomy independent of PTEN status
Jia et al. Accumulation of p53 is prognostic for aromatase inhibitor resistance in early-stage postmenopausal patients with ER-positive breast cancer
WO2025083140A1 (fr) Biomarqueurs pour prédiction de réponse à l&#39;immunothérapie
Yang et al. Significance of tumor satellite variables in reflecting the epithelial-mesenchymal transition of tongue cancer
EP3140656B1 (fr) Compositions et procédés de biopsie de liquides permettant le diagnostic de mélanome
Soomro Discordance of biomarker status in recurrent breast cancer
Cohen et al. Expression of P53, P27 and KI‐67 in colorectal cancer patients of various ethnic origins: Clinical and tissue microarray based analysis
Pascual et al. Prevalence and associated malignancy of Bethesda category III cytologies of thyroid nodules assigned to the “cytological atypia” or “architectural atypia” groups

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 24789923

Country of ref document: EP

Kind code of ref document: A1