WO2023282749A1 - Classificateur génique pour les phénotypes immunitaires spatiaux du cancer - Google Patents

Classificateur génique pour les phénotypes immunitaires spatiaux du cancer Download PDF

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WO2023282749A1
WO2023282749A1 PCT/NL2022/050395 NL2022050395W WO2023282749A1 WO 2023282749 A1 WO2023282749 A1 WO 2023282749A1 NL 2022050395 W NL2022050395 W NL 2022050395W WO 2023282749 A1 WO2023282749 A1 WO 2023282749A1
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cell
subject
tme
solid tumor
tumor
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Johannes Wilhelmus Maria Martens
Johannes Eduard Maria Antonius Debets
Dora Martha HAMMERL
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Erasmus University Medical Center Rotterdam
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    • 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
    • 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
    • C12Q2600/118—Prognosis of disease development
    • 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

Definitions

  • the invention is in the field of molecular diagnostics of cancer, in particular the molecular typing of the tumor micro-environment (TME) as a spatial immune phenotype of a tumor involving measurement of nucleic acid biomarkers and use of a multi-gene classifier.
  • TME tumor micro-environment
  • the invention provides methods for typing the TME of a solid tumor as being T cell -inflamed or non-T cell-inflamed, preferably as being either T cell-inflamed, T cell- ignored or T cell-excluded, involving a multi-gene classifier, and methods of predicting disease prognosis or clinical outcome of ICI therapy of patients suffering from cancer based on the predictive nucleic acid biomarkers disclosed herein.
  • TNBC Triple negative breast cancer
  • BC breast cancer
  • TILs tumor-infiltrating lymphocytes
  • ICI immune checkpoint inhibition
  • TNBC are variable, and do not exceed 24% when administered as mono- therapy (Kwa et al., Cancer, 124(10):2086-2103 (2016)).
  • Clinical benefit has been observed for first-line treatment with programmed cell death -ligand 1 (PD-L1) inhibitors (e.g. the PD-L1 blocking antibody atezolizumab) in combination with nanoparticle albumin-bound (nab)-paclitaxel, which has been approved by the EMA and FDA for PD-Ll-positive metastatic TNBC.
  • PD-L1 inhibitors e.g. the PD-L1 blocking antibody atezolizumab
  • nanoparticle albumin-bound (nab)-paclitaxel which has been approved by the EMA and FDA for PD-Ll-positive metastatic TNBC.
  • TMB tumor mutational burden
  • TIL abundance in TNBC. While a high TMB has been associated with response to ICI-based therapies in melanoma, lung cancer, and colorectal cancer (Marra et al., BMC Med., 17(1): 1-9 (2019)), no significant association between TMB and ICI response has been found for TNBC (Samstein et al., Nat Genet., 51(2):202-206 (2019); Voorwerk et al., Nat Med., doi: 10.1038/s41591-019-0432-4 (2019); Molinero et al., J Immunother Cancer., 7(1): 1-9 (2019)).
  • TILs are frequently present in primary TNBC and correlate with good prognosis as well as response to neoadjuvant chemotherapy and ICI in the metastatic setting (Hammerl et al., Semin Cancer Biol. 52:178-188 (2016); Voorwerk et al., Nat Med., doi: 10.1038/s41591-019-0432-4 (2019); Hammerl et al., Clin Cancer Res., doi: 10.1158/1078-0432. CCR- 19-0285 (2019); Loi et al., Ann Oncol.,
  • TILs predict overall survival (OS) to anti-PDl as a monotherapy independent of PD-L1 expression (Relationship Between Tumor-Infiltrating Lymphocytes and Outcomes in the KEYNOTE- 119 Study of Pembrolizumab Versus Chemotherapy for Previously Treated, Metastatic Triple-Negative Breast Cancer. 2019). Emerging evidence now suggests that next to abundance of TILs, also the cellular composition and activation state of TILs contribute to clinical outcome.
  • tissue- resident memory CD8+ T (Trm) cells provides more prognostic information when compared to CD8+ T cells (Savas et al., Nat Med., doi:10.1038/s41591- 018-0078-7 (2016)), and hallmarks of an ongoing immune response, such as clonal T cell expansion, correlate to anti-PDl response (Voorwerk et al., Nat Med.., doi:10.1038/s41591-019-0432-4 (2019)).
  • TILs have prognostic value in TNBC (Keren et al., Cell, 174(6):1373-1387.el9 (2016); Gruosso et al., J Clin Invest., 129(4):1785-1800 (2019)).
  • inflamed also referred to as “hot”; characterized by the presence of intratumoral lymphocytes
  • excluded also referred to as “altered”; lymphocytes are restricted to the invasive margin
  • deert also referred to as “desert”; characterized by lack of lymphocytes
  • biomarkers that predict spatial localization of CD8+ T cells in primary and secondary tumors, and which are prognostic.
  • biomarkers that allow for the stratification of patients with solid tumors such as TNBC into groups that are likely to respond to ICI therapy or other immune therapies and groups that will likely not respond to ICI therapy or other immune therapies and which consequently may benefit from combinatorial approaches or a different therapy.
  • the inventors have surprisingly established that the well-known spatial immune phenotypes (i) ‘ inflamed’ (ii) ‘excluded’ and (iii) ‘ignored’ can be accurately assigned by using a gene classifier. They have furthermore found that the gene classifier can directly be used to assess disease prognosis and provides a prognostic factor for improved patient survival in TNBC and other solid tumor types, and that the gene classifier can directly predict response to ICI therapy such as anti-PDl or anti-PD-Ll treatment in metastatic TNBC and other solid tumors such as melanoma. Furthermore, the inventors have identified genes, the expression of which are as such associated with prognosis of solid tumors such as TNBC.
  • anti-cancer immunotherapy such as immune checkpoint inhibitor therapy, preferably a PD-1 or PD-L1 inhibitor
  • anti-cancer immunotherapy can be employed in relation to all three immune phenotypes with the proviso that: (i) for treatment of subjects having a tumor with the ignored phenotype the immunotherapeutic agent is to administered in combination with a WNT inhibitor and/or an inhibitor of M2 macrophages; (ii) for treatment of subjects having a tumor with the excluded phenotype the immunotherapeutic agent is to be administered in combination with a TGF ⁇ inhibitor and/or a VEGF inhibitor; and (iii) for treatment of subjects having a tumor with the inflamed phenotype the immunotherapeutic agent is to be administered as a single agent or in combination with another immunotherapeutic agent, an epigenetic drug and/or an inhibitor of M2 macrophages. See Example 1, Figures 10 and 13.
  • the invention provides in a first aspect a method for typing a tumor micro-environment (TME) of a solid tumor as being of the immune phenotype T cell-inflamed, T cell-excluded or T cell-ignored, comprising the steps of: - providing a test sample of a solid tumor comprising a TME from a subject; measuring in said test sample the gene expression level for (i) at least one gene selected from group 1 consisting of IGHG1, NKG7, IL2RG, IL7R, CCL18, PVRIG, PLAC8, CCL5, SIRPG,
  • TME tumor micro-environment
  • the method of the invention is a method for typing a TME of a solid tumor as being of the immune phenotype T cell-inflamed, T cell-excluded or T cell-ignored; wherein said reference gene expression level comprises the gene expression level of said at least one gene in at least one reference sample of each of said three immune phenotypes, and wherein said inflam oefd said solid tumor is typed as being T cell-inflamed, T cell-excluded or T cell-ignored on the basis of the comparison of said measured gene expression level and said reference.
  • said gene expression level is measured for at least 2 genes selected from each of group 1, group 2, and group 3.
  • said gene expression level is measured for at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15 genes selected from group 1, and at least 3, 4, 5, 6, 7, 8, 9,
  • said gene expression level is measured for IGHGl, NKG7, IL2RG, IL7R, CCL18, PVRIG, PLAC8, SIRPG, COROIA, LCK, TRBC1, GZMB, CCL5, CXCL13, WARS, COL5A1, SPON1, CAMK2N1, FAP, SPOCK1, COL1A1, SCGB2A1, AKR1C2, CPE, SCGB2A2, TCN1, TPSAB1, MMP2, PERP, THBS2, ASPN, COLlOAl, TUFT1, GREM1, CEACAM6, ENTPD3, IGFBP5, PBX1, CXADR, GPRC5A, SDCl and CALML5.
  • the sohd tumor is selected from the group formed by BRCA: breast carcinoma such as triple negative breast cancer (TNBC); CESC: cervical squamous cell carcinoma and endocervical adenocarcinoma; HNSCC: head and neck squamous cell carcinoma; KICH: chromophobe renal cell carcinoma; BLCA: bladder urothelial carcinoma, and SKCM: skin cutaneous melanoma.
  • BRCA breast carcinoma such as triple negative breast cancer (TNBC)
  • CESC cervical squamous cell carcinoma and endocervical adenocarcinoma
  • HNSCC head and neck squamous cell carcinoma
  • KICH chromophobe renal cell carcinoma
  • BLCA bladder urothelial carcinoma
  • SKCM skin cutaneous melanoma.
  • the sohd tumor is a triple negative breast cancer (TNBC).
  • TNBC triple negative breast cancer
  • the sohd tumor is a primary tumor, recurrent tumor or a secondary (metastasized) tumor.
  • measuring the gene expression level is performed by qPCR, microarray analysis or next- generation sequencing (NGS).
  • said method for typing is a method for predicting the prognosis of a subject with a solid tumor, and wherein, when said TME of a solid tumor from said subject is typed as T cell-inflamed said subject has a favorable prognosis, and wherein, when said TME of a solid tumor from said subject is typed as being T cell-excluded or T cell-ignored, said subject has an unfavorable prognosis.
  • the present invention provides a method for assigning a subject having a solid tumor comprising a TME to an immunotherapy-responsive or immunotherapy-unresponsive group, such as an ICI-responsive or ICI-unresponsive group, said method comprising the steps of: performing a method for typing according to the invention as described above; and assigning said subject to the immunotherapy- responsive group when said inflam oefd a solid tumor from said subject is typed as T cell - inflamed or assigning said subject to the immunotherapy- unresponsive group when said inflam oefd a solid tumor from said subject is typed as T cell-excluded or T cell-ignored.
  • the present invention provides an immunotherapeutic agent for use in the treatment of a subject having a sohd tumor comprising a TME, wherein said subject is assigned to the immunotherapy-responsive group according to a method for assigning a subject according to the invention; optionally wherein said immunotherapeutic agent is for administration in combination with an epigenetic drug and/or an inhibitor of M2 macrophages.
  • the invention provides an immunotherapeutic agent for use in the treatment of a subject having a solid tumor comprising a TME, wherein said subject is assigned to the immunotherapy- unresponsive group according to a method for assigning according to the invention; and wherein said TME of said solid tumor is typed as being T cell-ignored; and wherein said immunotherapeutic agent is for administration in combination with a WNT inhibitor or an inhibitor of M2 macrophages such as colony stimulating factor 1 receptor (CSF1) inhibitor.
  • a WNT inhibitor or an inhibitor of M2 macrophages such as colony stimulating factor 1 receptor (CSF1) inhibitor.
  • the present invention provides an immunotherapeutic agent for use in the treatment of a subject having a sohd tumor comprising a TME, wherein said subject is assigned to the immunotherapy-unresponsive group according to a method for assigning a subject according to the invention; and wherein said TME of said solid tumor is typed as being T cell-excluded; and wherein said immunotherapeutic agent is for administration in combination with a TGF ⁇ inhibitor and/or a VEGF inhibitor.
  • the immunotherapeutic agent is an immune checkpoint inhibitor (I Cl), preferably a PD-1 or PD-L1 inhibitor.
  • I Cl immune checkpoint inhibitor
  • the invention provides a method for treating a subject having a solid tumor comprising a TME, comprising the steps of: - performing a method for assigning according to the invention; - administering a therapeutically effective amount of an immunotherapeutic agent, optionally in combination with an epigenetic drug and/or an inhibitor of M2 macrophages, to said subject if the subject is assigned to the immunotherapy-responsive group.
  • the invention provides a method for treating a subject having a solid tumor comprising a TME, comprising the steps of: - performing a method for assigning according to the invention; - administering a therapeutically effective amount of an immunotherapeutic agent in combination with a WNT inhibitor or an inhibitor of M2 macrophages if said subject is assigned to the immunotherapy-unresponsive group and if said TME of said solid tumor is typed as being T cell-ignored.
  • the invention provides a method for treating a subject having a solid tumor comprising a TME, comprising the steps of: - performing a method for assigning according to the invention; - administering a therapeutically effective amount of an immunotherapeutic agent in combination with a TGF ⁇ inhibitor and/or a VEGF inhibitor if said subject is assigned to the immunotherapy-unresponsive group and if said TME of said solid tumor is typed as being T cell-excluded.
  • the immunotherapeutic agent is an immune checkpoint inhibitor (I Cl), preferably a PD-1 or PD-L1 inhibitor.
  • the present invention provides a method for predicting a prognosis for a subject with a solid tumor comprising a TME, said method comprising the steps of: - measuring in a sample of a solid tumor comprising a TME from a subject a gene expression level for (i) at least one gene selected from group 1 consisting of IGHGl, NKG7, IL2RG, IL7R, CCL18, PVRIG, PLAC8, CCL5, SIRPG, COROlA, LCK, TRBCl, GZMB, CXCL13, and WARS; and (ii) at least one gene selected from group 2 consisting of COL5A1, SPON1, CAMK2N1, FAP, SPOCK1, COL1A1, SCGB2A1, AKR1C2, CPE, SCGB2A2, TCN1, TPSAB1, and MMP2; and/or (iii) at least one gene selected from group 3 consisting of PERP, THBS2, ASPN, COL10A
  • the invention also provides a method for measuring or determining a gene expression level in a test sample of the TME of a solid tumor of a subject, comprising the steps of providing a test sample of the TME of a solid tumor of a subject; and measuring in said test sample the gene expression level for (i) at least one gene selected from group 1 consisting of IGHG1, NKG7, IL2RG, IL7R, CCL18, PVRIG, PLAC8, CCL5, SIRPG, CORO 1 A, LCK, TRBC1, GZMB, CXCL13, and WARS; and (ii) at least one gene selected from group 2 consisting of COL5A1, SPON1, CAMK2N1, FAP, SPOCK1, COL1A1, SCGB2A1, AKR1C2, CPE, SCGB2A2, TCN1, TPSABl, andMMP2; and (iii) at least one gene selected from group 3 consisting of PERP, THBS2, ASPN, COLlOAl,
  • said step of providing a test sample and/or said step of measuring is as disclosed in relation to a method for typing of the invention.
  • the invention also provides a method of treating a subject having a solid tumor comprising a classified TME, comprising the step of: - administering a therapeutically effective amount of an immunotherapeutic agent to a subject having a solid tumor comprising a classified TME; wherein said subject is assigned to the immunotherapy-responsive group according to a method for assigning according to the invention and wherein said TME of said solid tumor is classified as being T cell-inflamed; and wherein said immunotherapeutic agent is an ICI or other type of immune therapy, preferably as indicated herein.
  • a classified inflam aesd defined in embodiments of the present invention refers to a inflam cleadssified by methods of this invention.
  • the present invention in providing methods for the stratification of patients with solid tumors, such as TNBC, into groups that are likely to respond to ICI therapy or other immune therapies and groups that will likely not respond to ICI therapy or other immune therapies, now provides for companion diagnostic methods to aid in selecting or excluding patients or patient groups for treatment with immunotherapeutic agents and which patients or patient groups may benefit from combinatorial approaches or an altogether different therapy.
  • the invention thereto provides a method of treating a subject having a sohd tumor comprising a classified TME, comprising the step of: - administering a therapeutically effective amount of an immunotherapeutic agent to a subject having a solid tumor comprising a classified TME; wherein said subject is assigned to the immunotherapy-unresponsive group according to a method for assigning according to the invention and wherein said classified TME of said solid tumor (preferably TNBC) is typed as being T cell -ignored; and wherein said immunotherapeutic agent is for administration in combination with an WNT inhibitor or with a colony stimulating factor 1 receptor (CSF 1) inhibitor.
  • TNBC colony stimulating factor 1 receptor
  • the invention also provides a method of treating a subject having a solid tumor comprising a classified TME, comprising the step of administering a therapeutically effective amount of an immunotherapeutic agent to a subject having a solid tumor comprising a classified TME; wherein said subject is assigned to the immunotherapy-unresponsive group according to a method for assigning according to the invention and wherein said classified inflam oefd said solid tumor (preferably TNBC) is typed as being T cell-excluded; and wherein said immunotherapeutic agent is for administration in combination with a TGF ⁇ inhibitor or a VEGF inhibitor.
  • the invention also provides a use of an immunotherapeutic agent in the manufacture of a medicament for treatment of a subject having a sohd tumor comprising a classified TME; wherein said subject is assigned to the immunotherapy-responsive group according to a method for assigning according to the invention and wherein said classified TME of said solid tumor is typed as being T cell-inflamed; and wherein said immunotherapeutic agent is an ICI or other type of immune therapy.
  • the invention also provides a use of an immunotherapeutic agent in the manufacture of a medicament for treatment of a subject having a sohd tumor comprising a classified TME; wherein said subject is assigned to the immunotherapy-unresponsive group according to a method for assigning according to the invention and wherein said classified TME of said solid tumor is typed as being T cell-ignored; and wherein said immunotherapeutic agent is for administration in combination with an WNT inhibitor or with a CSF 1 inhibitor.
  • the invention also provides the use of an immunotherapeutic agent (e.g. an ICI or other type of immune therapy) in the manufacture of a medicament for treatment of a subject having a solid tumor comprising a classified TME; wherein said subject is assigned to the immunotherapy- unresponsive group according to a method for assigning according to the invention and wherein said classified TME of said solid tumor is typed as being T cell-excluded; and wherein said immunotherapeutic agent is for administration in combination with a TGF ⁇ inhibitor or a VEGF inhibitor.
  • an immunotherapeutic agent e.g. an ICI or other type of immune therapy
  • FIG. 1 Study Design. Different steps and types of analyses regarding spatial immune phenotypes. Colors of boxes reflect the cohorts used for each step (for details and clinical characteristics of cohorts see M&M section and Table 2). For cohort A and F spatial phenotypes were identified using IHC of CD8+ T cells on whole slides and for cohort B-E spatial phenotypes were assigned using the gene-classifier.
  • FIG. 1 Workflow for digital image analysis of immune stainings.
  • A Whole slide images of CD8+ T cell IHC with border and center stamps (regions of interest, red and yellow, respectively) with close-up (20x magnification) of one border stamp (top), separation of tissue (red) and empty space (blue) (middle) and identification of CD8-positive (red) and negative (blue) cells (bottom). Yellow fine indicates outer tumor margin.
  • B Image analysis for multiplex IF of immune effector panel at border and center of an inflamed TNBC; from left to right: multicolour IF image, tissue segmentation (red: tumor; green: stroma; orange: empty space, yellow line: outer tumor margin); cell segmentation; and individually phenotyped cells (Inform software). Stamp size: 670x502 pm 2 ; resolution: 2 pixels/pm 2 ; pixel size: 0.5x0.5 pm 2 .
  • FIG. 1 Immune effector cells according to spatial phenotypes in TNBC. Boxplots show cell densities (in cell number/mm 2 ) following staining and analysis using immune effector panel.
  • HR Hazard Ratios
  • Cl 95% confidence intervals
  • FIG. 4 Performance and clinical validation of gene classifier.
  • Gene classifier (as described in Results section and Figure 7 A) was tested for: A. Correct assignment of spatial immune phenotypes in primary TNBC; B. Correct assignment of spatial immune phenotypes in TNBC lymph node metastases; C. Prediction of response to anti-PDl treatment. Abbreviations: PPV: positive predictive value, NPV: negative predictive value.
  • FIG. 1 Spatial phenotypes in non-TNBC cancers.
  • A. Forest plots show HRs and CIs of individual classifier genes (Cohort B, not corrected for multiple testing).
  • B. Stacked bar-graphs show frequencies of spatial phenotypes in different breast cancer subtypes (left, Cohort B) and various cancer types (right, Cohort E); below right panel ORRs are listed for ICI treatments of respective cancer types.
  • Boxplots display average expression of gene-sets for the excluded as well as inflamed phenotype in responding and non-responding melanoma patients following ICI treatment (left, Hugo Cohort; right, Riaz Cohort). Significant differences are: **, p ⁇ 0.01; *, p ⁇ 0.05; NS, n>0.5.
  • PAAD pancreatic adenocarcinoma
  • BRCA breast carcinoma
  • PRAD prostate adenocarcinoma
  • BLCA bladder urothelial carcinoma
  • HNSC head and neck squamous cell carcinoma
  • CO AD colon adenocarcinoma
  • LUAD lung adenocarcinoma
  • CESC cervical squamous cell carcinoma and endocervical adenocarcinoma
  • KICH chromophobe renal cell carcinoma
  • SKCM skin cutaneous melanoma.
  • ORR References for ORR: 1: Henriksen et al., Can Treat Review, 2019; 2: Kwa et al., Cancer, 2018; 3: Fay et al., Ann Transl Med, 2019; 4: Arlington et al., Annals Oncol, 2019; 5: Kamatham et al., Cur Col Can Rep, 2019; 6: Kim et al., Invest Clin Urol, 2018; 7: Regzedmaa et al., Oncotargets Ther, 2019; 8: Liu et al., Frontiers Pharmacol, 2019; 9: Flynn et al., Ther Adv Med Oncol, 2019.
  • FIG. 6 Spatial immune contexture is prognostic in TNBC.
  • A B. Representative whole slide images of CD8+ T cell spatial phenotypes with percentage of patients per phenotype (A) and corresponding Kaplan-Meier curves for metastasis-free survival (MFS), disease-free survival (DFS) and overall survival (OS) (B, p-values show log-rank test for trend; time is displayed in months).
  • C Representative multiplex IF images of immune effector cells at the tumor border and center of each spatial phenotype.
  • D Circle plots show mean and SD of immune cell densities (cells/mm 2 ) at border and center.
  • E E.
  • Histograms show mean distances in pm between CD8+ T cells and other cell types (x-axis) versus their respective densities (cells/mm 2 , y-axis).
  • F. Boxplots show total number of tertiary lymphoid structures (TLS, identified by consecutive stainings of CD20+ B cells (top) and CD4+ T cells (bottom), see black squares in images). Significant differences are: ***, p ⁇ 0.001; **, p ⁇ 0.01; *, p ⁇ 0.05, NS, p>0.5.
  • Figure 7. Gene classifier assigns spatial phenotypes of CD8+ T cells and stratifies metastasized TNBC patients according to ICI response.
  • Heatmap shows median expression of classifier genes per spatial phenotype in the discovery set (red: high expression, blue: low expression; Cohort Al).
  • B. Forest plots show HRs and CIs of classifier gene-sets (Cohort B).
  • D. Forest plots show Odds Ratios (OR) for response to anti-PD-1 treatment of classifier gene-sets (Cohort D, TONIC trial).
  • E. Boxplots display average expression of classifier gene-sets in responding (CR+PR+SD > 24 weeks) and non-responding (PD) patients (Cohort D).
  • ROC curves predict clinical response (PR+CR+SD) with areas under the curve (AUC) and CIs for gene sets of excluded-, inflamed- or a combination of the two phenotypes (average expression of respective gene sets was used) (first 3 panels), or for standardly used predictive markers, such as frequency of stromal TILs and PDL1 positivity of immune cells (Cohort D) (last 2 panels).
  • Figure 8 Predictive value of spatial immune phenotype gene classifier versus public classifiers.
  • ROC displays area under the curve for predicting anti-PDl response (CR+PR+SD) using the extended signature.
  • FIG. 9 Genomic features of spatial phenotypes.
  • the following parameters were tested for differential presence in spatial phenotypes (determined by the gene-classifier) in TNBC: A. BRCA status (proportion).
  • H TCR repertoire skewness (based on the Gini-Simpson index).
  • I Total number of different TCR-Vbeta reads. For all above parameters Cohort B was used, spatial phenotypes were assigned according to classifier. Significant differences are: ***, p0.001; **, p ⁇ 0.01;
  • FIG. 10 Spatial phenotypes interrogated for immune determinants and evasive pathways.
  • A. Heatmap shows scaled average frequencies of immune cell populations based on Cibersort deconvolution (red: high, blue: low, immune cell populations with significant differences among spatial phenotypes are indicated in bold); corresponding boxplots show immune cell populations with differential abundances among spatial phenotypes.
  • B. Heatmap shows scaled average expression of gene-sets related to T cell evasion (differential gene-sets are indicated in bold).
  • C Volcano plot of differential gene expressions between excluded and inflamed (upper), and ignored and inflamed phenotypes (lower); top DE genes related to T cell evasion are shown in bold.
  • FIG. 11 Spatial immune phenotypes are characterized by distinct T cell evasive mechanisms.
  • Boxplots show numbers of high endothelial venules (HEV, identified via MECA-79 staining, black arrow) and MHC-II expression of tumor cells (no distinction between border and centre, pink arrow: tumor cells; yellow arrow: adjacent normal breast lobules; green arrow: immune cells).
  • E. Neutrophil densities at border and centre and representative image is shown.
  • F. Boxplots show numbers of different T cell markers stained on consecutive slides, and representative images are shown. Significant differences are: ***, p0.001; **, p ⁇ 0.01; *, p ⁇ 0.05, NS, p>0.05.
  • FIG. 12 Spatial phenotypes in metastasized TNBC according to distant sites and induction treatment.
  • A. Stacked bar graphs show frequencies of spatial phenotypes assigned via gene classifier in different metastatic lesions (number indicates total number of lesions).
  • C Frequencies of spatial phenotypes from paired pre- and post- induction treatments (number indicates a change to inflamed phenotype).
  • FIG. 13 Illustration of immune contextures per spatial phenotype in relation to paths of T cell evasion as well as response to ICI. Distinctive and dominant pathways (in bold) per phenotype. When phenotypes are targeted in an immune phenotype-specific manner (in boxes), this would sensitize TNBC to ICI (see Discussion section for details).
  • Figure 14 Standardly used predictive markers of ICI response in patients with metastatic TNBC.
  • A. Boxplots show fraction of PDL1 -positive immune cells (upper plot) and fraction of stromal TIL (sTIL) per response group (all Cohort D).
  • HR prognostic value
  • OR 95% Cl and p-value
  • Stacked bar graphs show frequencies of spatial phenotypes stratified by sTIL>5%; table shows spatial phenotypes and sTIL in multivariable models according to prognostic value (HR, Cl and p-value) as well as predictive value (OR, Cl and p value).
  • Figure 15 Accuracy of the gene classifier.
  • This Figure shows on the y-axis the accuracy of the gene classifier in predicting the spatial immune phenotypes when randomly chosen genes from each group mentioned in Table 1 are included in the classifier.
  • the Figure shows that, starting with one random gene per group and increasing, accuracy improves. Already at 5 genes per group, an accuracy of >60% is attained; at 10 genes per group, an accuracy of >70% is attained.
  • cancer refers to a disease characterized by dysregulated cell proliferation and/or growth.
  • the term comprises benign and mahgnant cancerous diseases, such as tumors, and may refer to an invasive or non-invasive cancer.
  • the term comprises all types of cancers, including carcinomas, sarcomas, lymphomas, germ cell tumors, and blastomas.
  • the term cancer relates to solid tumor.
  • solid tumor examples include stomach cancer, breast cancer, lung cancer, colorectal cancer, liver cancer, gallbladder cancer, pancreatic cancer, thyroid cancer, prostate cancer, ovarian cancer, uterine cervical cancer, bladder cancer, sarcoma, glioma, mesothelioma, colorectal tumors, hepatic tumors, and head and neck tumors, with preference for breast cancer, lung cancer, colorectal cancer, stomach cancer, prostate cancer, and liver cancer.
  • the term cancer relates to breast cancer.
  • cancer relates to invasive breast cancer.
  • Invasive breast cancer may spread from the breast through the blood and lymph system to other parts of the body.
  • cancer relates to triple-negative breast cancer (TNBC), which does not express the genes for HER2neu (ERBB2), estrogen receptor (ER), and progesterone receptor (PR).
  • TNBC triple-negative breast cancer
  • ERBB2neu HER2neu
  • ER estrogen receptor
  • PR progesterone receptor
  • spatial phenotype and “spatial immune phenotype” refer to the three-type model for the immune phenotype based on the distribution of immune cells in the TME including (a) T cell -inflamed, (b) T cell-excluded, and (c) T cell-ignored also known as T cell desert (Gruosso et al. 2019 J Clin Invest. 129(4):1785-1800; Galon et al. 2006 Science 336:61- 64; Galon et al. 2019 Nature Reviews Drug Discovery 18: 197-218; Chen & Mellman 2017 Nature 541(7637):321-330).
  • the three main spatial phenotypes are associated with different clinical outcome in TNBC as well as other cancer types.
  • the three immune phenotypes can essentially be identified based on spatial distribution of tumor-infiltrating CD8 + T cells as histologically (IHC) discernable in whole tissue sections (e.g. at the border and center of a solid tumor as displayed in Figures 2 A and 6 A and schematically shown in Figure 13) from tumors of different individuals.
  • T cell -inflamed is characterized by CD8 + T cells being evenly distributed across border and center (e.g.
  • T cell-excluded is characterized by CD8 + T cells being predominantly located at the tumor border, not the center (e.g. >10 times more CD8+ T cells at the border compared to center);
  • T cell-ignored is characterized by negligible presence of CD8 + T cells neither at border nor center (hardly any CD8 + T cells present at the border and center).
  • the above quantitative distribution of CD8 + T cells may be assessed by IHC, preferably by digital image analysis of IHC-stained tissue sections.
  • the number of CD8 + T cells per mm 2 in different compartments may be applied as follows: inflamed, >200 cells/mm 2 at border and ratio between border and center ⁇ 10; excluded, >200 cells/mm 2 at border and ratio between border and center >10; ignored ⁇ 150 cells/mm 2 at border and center.
  • gene classifier “gene expression classifier”, and “multi-gene biomarker” are used interchangeably herein to refer to a gene signature or molecular indicator that can discriminate between different spatial immune phenotypes of a solid tumor type based on differential gene expression between these phenotypes.
  • the gene classifier discriminates between inflamed, excluded and ignored phenotypes based on differentially expressed genes in a test sample compared to a reference, the reference preferably being the averaged expression of all classifier genes in a set comprising representative tumor samples of each of the three spatial phenotypes of the same cancer.
  • the ranking indicated in Table 1 is used.
  • the phenotype of an unknown tumor e.g.
  • test sample is assigned to one of the three classes based on highest Spearman rank-correlations between the test sample and (ranked) expressions of classifier genes in the three spatial phenotypes.
  • the reference set may include tumor samples of which the spatial immune phenotype is determined by classical immune histochemical (IHC) methods, preferably based on differential presence and spatial distribution of CD8 + T cells in the reference samples, which may either be scored manually or through digital image analysis as explained in the Experimental section. Differential gene expression analysis in these reference samples for one or more of the genes of the classifier indicated herein may provide suitable reference data with which test data of unknown samples of the same cancer can be correlated.
  • IHC classical immune histochemical
  • the genes IGHGl, NKG7, IL2RG, IL7R, CCL18, PVRIG, PLAC8, CCL5, SIRPG, COROIA, LCK, TRBC1, GZMB, CXCL13, and WARS are overexpressed (upregulated) in the phenotype “inflamed” relative to the other two phenotypes;
  • the genes COL5A1, SPON1, CAMK2N1, FAP, SPOCK1, COL1A1, SCGB2A1, AKR1C2, CPE, SCGB2A2, TCN1, TPSABl, and MMP2 are overexpressed (upregulated) in the phenotype “ignored” relative to the other two phenotypes, and the genes PERP, THBS2, ASPN, COL10A1, TUFT1, GREM1, CEACAM6, ENTPD3, IGFBP5, PBX1, CXADR, GPRC5A, SDCl and CALML5 are over
  • the relative gene expression level of a set of minimally 3 genes comprising at least 1 gene that is characteristic for each phenotype is preferably compared or correlated to the relative gene expression levels of the same set of genes from reference samples (Table 1).
  • Table 1 The above is applicable for typing test samples and distinguishing between the three immune phenotypes.
  • the relative gene expression of a set of minimally 2 genes comprising at least 1 gene that is characteristic for each phenotype is preferably compared or correlated to the relative gene expression levels of the same set of genes from reference samples.
  • the reference sample preferably comprises a set of reference samples, and, depending on the level of distinction required, said set preferably includes at least 1 sample of each of the 2 different immune phenotypes inflamed and non-inflamed, or said set preferably includes at least one sample of each of the 3 different immune phenotypes inflamed, excluded, and ignored.
  • the gene classifier allows discrimination between the spatial immune phenotypes “inflamed” and “non-inflamed” (wherein “non-inflamed” is either excluded or ignored). In addition, the gene classifier allows further discrimination of the spatial immune phenotype “non-inflamed” into the spatial immune phenotypes “excluded” and “ignored”. The gene classifier, in one preferred embodiment, allows discrimination between the spatial immune phenotypes “inflamed” and “excluded” and “ignored” of a specified cancer.
  • the analytical steps to distinguish between the various spatial immune phenotypes is herein also referred to as “typing”, which comprises differential gene expression analysis between a test tumor sample and a reference sample (preferably a set of reference samples as indicated above) and ranking the gene expression data on the basis of the gene classifier to thereby identify the spatial immune phenotype that matches the reference phenotype, preferably using the gene classifier as displayed in Table 1, and preferably using microarray gene expression analysis or RNAseq.
  • the term "typing” as used herein includes any method of analysing the gene expression level of one or more nucleic acid molecules to be analysed (e.g. the "test” or target nucleic acid).
  • typing in aspects of the invention includes methods for analysing gene expression in a test tissue relative to reference tissues or a reference dataset.
  • Methods of the invention thus include methods of determining the gene expression of a test sample and comparing the expression data with reference data.
  • Analysing the gene expression level in aspects of the present invention involves determining the expression level of multiple genes from the multi-gene classifier as described herein, and in particular as indicated in Table 1.
  • the term "multiple” as used herein means 2 or more (such as 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42 or more).
  • typing may include the analysis of 1 or more (such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15) genes upregulated in the phenotype “inflamed”, together with the analysis of 1 or more (such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) genes upregulated in the phenotypes “excluded” and “ignored” (together also indicated as “non-inflamed”) as described herein.
  • 1 or more such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15
  • typing may comprise the analysis of 1 or more (such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15) genes upregulated in the phenotype “inflamed”, further comprising the analysis of 1 or more (such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14) genes upregulated in the phenotype “excluded”, and further comprising the analysis of 1 or more (such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13) genes upregulated in the phenotype “ignored”.
  • 1 or more such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15
  • Typing in methods of the present invention is based on determining the presence of differential gene expression in a solid tumor sample by measuring the quantity of a gene product (RNA or protein, preferably mRNA) for at least two, preferably at least three genes of the multi-gene classifier described herein, preferably at least one gene that is upregulated in each phenotype.
  • RNA or protein preferably mRNA
  • “differential gene expression” is considered to be present when there is at least an about 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9 or 2.0-fold, preferably at least about four-fold, more preferably at least about six-fold, most preferably at least about ten-fold difference between the expression of a given gene in a test sample and the expression in the reference.
  • Table 1 includes both gene names and/or reference accession numbers, such as ENTREZ ID (GenelDs), and Affymetrix Probeset IDs. These identifiers may be used to retrieve publicly- available annotated DNA, mRNA or protein sequences from sources such as the NCBI website, which may be found at the following uniform resource locator (URL): http://www.ncbi.nlm.nih.gov.
  • the identifiers given in the tables herein pertain to NCBI-GenBank Flat File Release 244.0 incorporating data processed by the INSDC databases as of Tuesday June 22, 2021.
  • Gene identifiers indicated herein include reference to those gene sequences in their entirety. The information associated with these identifiers, including reference sequences and their associated annotations, are all incorporated by reference.
  • the test sample used in methods of this invention is a tumor sample, preferably a whole tissue section or biopsy of a solid tumor.
  • biopsy refers to a sample of tissue (e.g. tumor tissue) that is removed from a subject for the purpose of determining, for example, if the sample contains cancerous tissue or for use in analysis using methods of the present invention.
  • the test sample used in methods of this invention is preferably a tumor sample comprising the TME, preferably wherein the sample includes tumor cells, resident stromal cells, such as fibroblasts, myofibroblasts, and neuroendocrine cells, and immune cells.
  • the sampling procedure therefore preferably allows for the provision of, or provides, a mixed cell sample or heterogeneous cell sample of the tumor tissue.
  • a sohd tumor sample is a needle biopsy sample.
  • the test nucleic acid is preferably RNA (e.g. mRNA) derived or isolated from the test sample.
  • RNA e.g. mRNA
  • cDNA may be generated using the RNA as template of a reverse transcriptase reaction.
  • the quantity of RNA may inter alia be determined by RNAseq or microarray analysis.
  • tumor micro-environment refers to the status of interaction between tumor cells, resident stromal cells, and immune cells, in particular the T cell, preferably CD8+ T cell, presence and spatial distribution in the tumor and/or its surroundings.
  • the spatial immune phenotype of a solid tumor is a feature of the TME and represents the distinct T cell evasive mechanisms active in that environment.
  • Immunotherapeutic agent refers to any compound, biologic or cell used for the treatment of disease by activating or suppressing the immune system.
  • Immunotherapeutic agents include immunomodulators (such as thalidomide, lenalidomide, pomalidomide and/or imiquimod; cytokines like IFN-a; and other peptides) and immune checkpoint inhibitors (ICIs, such as anti-PDl antibodies: e.g. nivolumab, pembrolizumab, and cemiplimab; anti-PD-Ll antibodies: e.g. atezolizumab, darmatizumab; anti-CTLA4 antibodies: e.g.
  • anti-LAG3 antibodies e.g. relatlimab
  • stimulatory antibodies directed against CD40 or other costimulatory receptors such as APX005111
  • vaccines anti-cancer monoclonal antibodies for targeted therapy (such as Herceptin and bevacizumab), chimeric antigen receptor (CAR) T cells and TCR-T cells, as well as oncolytic viruses.
  • CAR chimeric antigen receptor
  • Chemotherapeutic agents include proteasome inhibitors such as carfilzomib, oprozomib, bortezomib, and ixazomib; tyrosine kinase inhibitors such as imatinib, lapatinib, acalabrutinib, afatinib, alectinib, avapritinib, axitinib, bosutinib, cabozantinib, crizotinib, dacomitinib, dasatinib, entrectinib, erlotinib, gilteritinib, ibrutinib, midostaurin, neratinib, nilotinib, pacritinib, pazopanib, pexidartinib, ponatinib, quizartinib, regorafenib, midostaurine, sorafenib, das
  • terapéuticaally effective amount includes reference to a quantity of a specified agent sufficient to achieve a desired effect in a subject being treated with that agent.
  • a therapeutically effective amount of an agent is an amount sufficient to inhibit or treat the disease or condition without causing a substantial cytotoxic effect in the subject.
  • the therapeutically effective amount of an agent will be dependent on the subject being treated, the severity of the affliction, and the manner of administration of the therapeutic agent. It is within the knowledge and capabilities of the skilled practitioner to determine therapeutically effective dosing regimens.
  • administering refers to the physical introduction of an agent or therapeutic compound to subject as disclosed herein, using any of the various methods and delivery systems known to those skilled in the art.
  • the skilled person is aware of suitable methods for administration and dosage forms.
  • Preferred route of administration for protein-based agents such as antibodies is by parenteral administration, including intravenous, intramuscular, subcutaneous, intraperitoneal, spinal or other parenteral routes of administration, executed inter aha by injection or infusion in the form of a solution.
  • Administering can be performed, for example, once, a plurality of times, and/or over one or more extended periods of time.
  • level when used in the context of measuring and comparing a gene expression level in a sample can refer to absolute or to relative quantification. Absolute quantification may be accomplished by inclusion of known concentration(s) of one or more control analytes and referencing the detected level of the target nucleic acid with the known control analytes (e.g., through generation of a standard curve). Alternatively, relative quantification can be accomplished by comparison of detected levels or amounts between two or more different target nucleic acids to provide a relative quantification of each of the two or more different nucleic acids, e.g., relative to each other. In addition, a relative quantitation may be ascertained using a control, or reference, value (or profile) of gene expression levels obtained from a control sample or in the form of a gene classifier.
  • typing refers to differentiating between, or stratification of, subjects according to their TME status, more specifically spatial immune phenotype status.
  • the typing is based on a comparison of (i) the measured gene expression level of at least one gene selected from each group as listed in Table 1 with (ii) a reference gene expression level for said at least one gene selected from each group as listed in Table 1.
  • the typing can be based on comparison of the measured gene expression levels with reference gene expression levels provided in the form of a classifier such as a trained algorithm designed to distinguish said spatial immune phenotypes on the basis of gene expression levels of said at least one gene of each group as listed in Table 1.
  • the reference is preferably composed of the expression level value in sohd tumor tissue samples with known spatial organization of CD8+ T cells. More preferably, the reference is the average gene expression of at least a subset of the genes in the classifier set forth in Table 1, such as the average expression value for 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
  • the reference can be provided in the form of a gene classifier that is a trained algorithm.
  • subject can be used interchangeably with the term “patient” or “individual”, and includes reference to a mammal, preferably a human, who is suffering from a solid tumor that comprises a TME. Where reference is made to subjects having a sohd tumor that comprises a TME, also included is reference to subjects in which said tumor is completely or partially resected and wherein said solid tumor may recur or metastasize or wherein said solid tumor has already recurred or metastasized. In other words, “having a sohd tumor that comprises a TME” does not exclude situations in which the solid tumor has already been resected, for instance in order to perform a method for typing of the invention.
  • combination includes reference to administration of at least two therapeutic agents together at the same time (either in the same pharmaceutical composition or in separate compositions), separately of each other at the same time or separately of each other staggered in time. Simultaneous, separate and sequential administration of the therapeutic agents disclosed herein is for instance envisaged.
  • WNT inhibitor includes reference to agents that inhibit the Wnt/6-catenin signaling pathway in cancer.
  • WNT inhibitors include, but are not limited to, IWR-1, IWP-2, Pyrvinium pamoate, Salinomycin (Procoxacin), CWP232228, LP-922056, Teplinovivint, Wnt-C59 (C59), NCB-0846, Salinomycin sodium salt, Adavivint, FH535, Wogonin, CCT251545, iCRT3, PNU-74654, Echinacoside, ETC- 159, KYA1797K, EMT inhibitor- 1, Prinaberel (ERB-041), MSAB, FIDAS-5, Triptonide (NSC 165677), IWP-4, KY-05009, SKI II, IQ 1, Gigantol, Ginkgetin, iCRT 14 , SSTC3, Prodigiosin (Prodigiosine), Specnu).
  • CSF1R inhibitor includes reference to agents that inhibit colony stimulating factor 1 receptor signaling.
  • CSF 1R inhibitors include, but are not limited to, CSFlR-IN-1, CSFlR-IN-2 (compound 5), AZD7507, ARRY-382, c-Fms-IN-8 (compound 4a), BLZ945, c- Fms-IN-10, PRN1371, OSI-930, Pexidartinib hydrochloride (PLX-3397 hydrochloride), Chiauranib (CS2164), Pexidartinib (PLX-3397), AC710, AC710 Mesylate, Sulfatinib (HMPL-012), AZ304, and CHMFL-ABL/KIT-155 (compound 34).
  • TGF ⁇ inhibitor includes reference to agents that inhibit transforming growth factor-beta signaling.
  • TGF ⁇ inhibitors include, but are not limited to, Galunisertib (LY2157299), Asiaticoside, BIO-013077-01, BIBF0775, EMT inhibitor-1, SJ000291942, LY2 109761, BMS453 (BMS- 189453), SB-431542, LSKL Inhibitor of Thrombospondin (TSP-1), A 77-01, GW788388, EW-7195, Disitertide (P144), 10,11-Dehydrocurvularin, A 83-01 sodium salt, LDN-212854, LY3200882, Oxymatrine, Isoviolanthin, SB-505124, Pirfenidone (AMR69), Halofuginone (RU-19110), SB-505124 hydrochloride, A 83-01, Halofuginone (RU-19110), TAK1/MAP4K2 inhibitor 1, De
  • VEGF inhibitor includes reference to agents that inhibit vascular endothelial growth factor signahng.
  • VEGF inhibitors include, but are not limited to, AEE 788, AG 879, AP 24534, Axitinib, BMS 605541, DMH4, GSK 1363089, Ki 8751, Nintedanib, RAF 265, Sorafenib, SU 4312, SU 5402, SU 5416, SU 6668, Sunitinib malate, Toceranib, Vatalanib succinate, XL 184, ZM 306416 hydrochloride, and ZM 323881 hydrochloride.
  • inhibitor of M2 macrophages includes such therapeutic compounds as Cyclooxygenase-2 (COX-2) inhibitor, including etodolac, as well as all-trans retinoic acid (ATRA) and MEL-dKLA (melittin- d(KLAKLAK)2 hybrid peptide).
  • COX-2 Cyclooxygenase-2
  • ATRA all-trans retinoic acid
  • MEL-dKLA melittin- d(KLAKLAK)2 hybrid peptide
  • epigenetic drug includes reference to agents that release epigenetic brakes of expression of genes and consequently enhance innate immune pathways, such as type I IFN pathway and chemo-attractant pathways.
  • Epigenetic drugs include, but are not limited to, inhibitors of DNA methyl transferases (DNMTi), such as 5-azacytidine and 5-aza-2-deoxycytidine; inhibitors of histone deacetylase (HDACi), such as, valproate, FK-228, SAHA, PDX-101; as well as inhibitors of histone methyl transferases (HMTi), e.g. inhibitors of EZH2, such as Tazemetostat.
  • DNMTi DNA methyl transferases
  • HDACi histone deacetylase
  • HMTi histone deacetylase
  • EZH2 histone methyl transferases
  • the present invention provides methods for typing the TME of a solid tumor. Methods of the present invention can be used to identify three main spatial immune phenotypes in TNBC as well as other cancer types through molecular analysis of gene expression.
  • the present invention provides methods for distinguishing or detecting the spatial immune phenotypes inflamed, ignored, and excluded, of a solid tumor. Due to the association of these phenotypes with clinical outcome in cancer, the present invention provides methods for predicting the prognosis of a cancer patient, as well as methods for predicting the response of a cancer patient to ICI therapy. Compositions and kits useful in carrying out such methods are also provided.
  • TBE tumor micro-environment
  • the present invention provides methods for typing, which allow for determining the spatial immune phenotype of a solid tumor tissue, where the methods generally involve the steps of determining an expression level of a product of a gene set forth in Table 1 in a sohd tumor tissue sample obtained from a patient, generating an expression level value, classifying the solid tumor tissue sample as a solid tumor with (i) an inflamed, (ii) an ignored, or (iii) an excluded spatial immune phenotype by comparing the expression level values to a reference.
  • the reference can be composed of the expression level values of the different phenotypes either according to samples with known spatial organization of CD8+ T cells or according to an established training set of samples.
  • the reference is the average gene expression of at least a subset of the genes in the classifier set forth in Table 1, such as the average expression values for at least 2, 3, 4, 5, 6, 7, 8,
  • the reference may be composed of the expression level value of at least one gene of the phenotype inflamed and at least one gene of the phenotype non-inflamed when typing a test sample as belonging to either one of these phenotypes, or the reference may be composed of the expression level value of at least one gene of the phenotype inflamed, at least one gene of the phenotype ignored, and least one gene of the phenotype excluded when typing a test sample as belonging to either one of these three phenotypes.
  • the expression values of an unknown sample are preferably correlated/compared to the expression values of a reference samples of each corresponding phenotype.
  • Methods of the present invention now enable to predict the prognosis of a subject with a solid tumor or to predict a patient’s response to therapeutic intervention such as ICI therapy.
  • the present methods for typing contemplate determining the gene expression value of at least one gene from each spatial immune phenotype group as listed in Table 1, of which the expression is upregulated.
  • genes of which the differential expression correlate to the phenotype “inflamed” are, in order of descending rank, selected from IGHG1, PBX1, WARS, IL7R, CCL5, COROlA, CXCL13, LCK, CCL18, TRBC1, PLAC8, GZMB, NKG7, IL2RG, SIRPG, and PVRIG.
  • genes of which the differential expression correlate to the phenotype “ignored” are, in order of descending rank, selected from COL1A1, COL5A1, MMP2, FAP, SPON1, CPE, SPOCK1, CAMK2N1, AKR1C2, SCGB2A2, TPSABl, TCN1 and SCGB2A1.
  • genes of which the differential expressions correlate to the phenotype “excluded” are, in order of descending rank, selected from IGFBP5, THBS2, SDCl, PBX1, PERP, CXADR, COL10A1, GPRC5A, CALML5, ASPN, TUFT1, CEACAM6, and ENTPD3.
  • the gene expression level is measured for at least 5, at least 6, at least 7, or at least 8 genes of each of spatial immune phenotype groups 1, 2 and 3.
  • Figure 15 shows that an accuracy of more than 60% is achieved when the gene expression level is measured for at least 5 random genes from each of spatial immune phenotype groups 1, 2 and 3 as depicted in Table 1.
  • the methods of the present invention comprise the step of measuring the expression level of at least one gene upregulated in each of the phenotypes according to the sub-listing in Table 1 (e.g. at least 1 gene upregulated in inflamed, at least 1 gene upregulated in ignored, and at least 1 gene upregulated in excluded).
  • at least 3 genes are therefore measured, including at least 1 gene upregulated in each phenotype.
  • upregulated in each phenotype preferably means upregulated relative to the gene expression in the other 2 phenotypes.
  • the expression level of at least 6 genes is measured, including at least 2 genes upregulated in each phenotype.
  • the expression level of at least 9 genes is measured, including at least 3 genes upregulated in each phenotype. Even more preferably, the expression level of at least 12 genes is measured, including at least 4 genes upregulated in each phenotype. Still more preferably, the expression level of at least 15, 18, 21, 24, 27, 30, 33, 36, 39, 42 genes is measured, including at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 genes upregulated in the classifier.
  • the methods of the present invention comprise the step of measuring the expression level of (i) at least a first gene selected from the group consisting of IGHGl, NKG7, IL2RG, IL7R, CCL18, PVRIG, PLAC8, CCL5, SIRPG, CORO 1 A, LCK, TRBC1, GZMB, CXCL13, and WARS; and
  • THBS2 THBS2, ASPN, COLlOAl, TUFT1, GREM1, CEACAM6, ENTPD3, IGFBP5, PBX1, CXADR, GPRC5A, SDCl and CALML5.
  • a difference (e.g., an increase, a decrease) in gene expression can be determined by comparison of the level of expression of one or more genes in a sample from a subject to that of a suitable control or reference.
  • suitable controls include, for instance, a non-neoplastic tissue sample (e.g. a nonneoplastic tissue sample from the same subject from which the cancer sample has been obtained), a sample of non-cancerous cells, non-metastatic cancer cells, non- mahgnant (benign) cells or the like, or a suitable known or determined reference standard.
  • the reference can be a typical, normal or normalized range of levels, or a particular level, of expression of a protein or RNA (e.g. an expression standard).
  • the standards can comprise, for example, a zero gene expression level, the gene expression level in a standard cell line, the average level of gene expression previously obtained for a population of normal human controls, or the average level of gene expression over the set of expression products measured, such as the average level of all classifier genes measured.
  • assignment of a test sample to one of three immune phenotypes is based on rank-correlations between expressions of classifier genes in the test sample and ranked expressions of classifier genes per spatial phenotype in the classifier, more preferably highest Spearman rank-correlations between the test sample and classifier.
  • rank correlation between expression data of classifier genes in a test sample and the spatial immune phenotype classifier described herein may be accomplished by different statistical methods, including Spearman rank correlation, Weighted Rank Correlation, Kendall rank correlation, Hoeffding’s D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, Pearson., etc.
  • correlation methods may also be used to assign a test sample to one of three immune phenotypes based on correlation with the classifier as proposed herein, including the use of Distance Covariance; Wilcoxon's, hnear regression or non-linear regression models.
  • the spatial-phenotype-classifier was not only predictive and apphcable in TNBC, but also in other solid tumor types. Hence, the classifier of the present invention may be used to assess the prognostic and predictive value in a pan-cancer setting. Spatial phenotypes were significantly prognostic not only in invasive breast cancer BRCA (all subtypes, including ER+), but also in bladder cancer (BLCA), skin cutaneous melanoma (SKCM), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), head and neck squamous cell carcinoma (HNSC) and kidney cancer (KICH), but despite similar trends not in prostate (PRAD), pancreatic (PAAD), lung (LUAD) or colon cancer (COAD).
  • BLCA bladder cancer
  • SKCM skin cutaneous melanoma
  • CESC cervical squamous cell carcinoma and endocervical adenocarcinoma
  • HNSC head and neck squamous
  • a gene-expression classifier was developed that allows assignment of spatial immune phenotypes based on RNA expressions, thereby enabling assessment of prognostic and predictive values of the spatial immune phenotypes without the need for CD8 + T cell stainings.
  • the spatial-phenotype-classifier significantly outperformed other, publicly available gene-classifiers that are recognized for capturing lymphocyte activity and location, and for predicting anti-PD 1 response in melanoma, such as IFNg-response, T cell-exclusion and TLS signatures (Figure 8).
  • Methods for predicting the prognosis of a subject with a solid tumor The present invention provides methods for predicting the prognosis of a subject with a solid tumor, using either a method for typing of the invention or a method for predicting a prognosis of the invention.
  • the methods generally involve determining an expression level of a gene product of a gene set forth in Table 1 in a solid tumor tissue sample obtained from a patient, generating an expression level value, classifying the solid tumor tissue sample as a solid tumor with (i) an inflamed, (ii) an ignored, or (iii) an excluded spatial immune phenotype by comparing the expression level value to a reference, and predicting the prognosis of said patient on the basis of the spatial immune phenotype of said solid tumor.
  • MFS distal metastasis-free survival
  • DFS disease-free survival
  • OS overall survival
  • Both the excluded and ignored phenotypes are significantly associated with poor metastasis-free survival (MFS).
  • MFS metastasis-free survival
  • the inflamed phenotype is significantly associated with better MFS.
  • the excluded and ignored phenotypes were associated with poor prognosis, whereas the inflamed phenotype is associated with good prognosis (e.g. Figures 5 and 7).
  • Tumors with an inflamed phenotype had the best prognosis (10-year OS: 80%), excluded phenotypes intermediate (10-year OS), and ignored phenotypes the worst prognosis (10-year OS: 40%). Prolonged survival of excluded versus ignored phenotypes was statistically significant for OS.
  • Methods for predicting a patient’s response to ICI therapy The present disclosure provides methods for predicting a patient’s response to ICI therapy using a method for typing or method for assigning of the invention.
  • the methods generally involve determining an expression level of a product of a gene set forth in Table 1 in a solid tumor tissue sample obtained from a patient, generating an expression level value, classifying the solid tumor tissue sample as a solid tumor with (i) an inflamed, (ii) an ignored, or (iii) an excluded spatial immune phenotype by comparing the expression level value to a reference, and predicting a patient’s response to ICI therapy on the basis of the spatial immune phenotype of said solid tumor.
  • the present inventors have found that excluded or ignored phenotypes classified as such by using the methods of the present invention respond poorly to ICI, while the inflamed phenotype responds well to ICI.
  • the present disclosure provides new and combined methods for treating a subject with a solid tumor, which may optionally be resected, recurrent or metastasized.
  • the methods generally involve determining an expression level of a product of a gene set forth in Table 1 in a solid tumor tissue sample obtained from a patient, generating an expression level value, classifying the solid tumor tissue sample as a solid tumor with (i) an inflamed, (ii) an ignored, or (iii) an excluded spatial immune phenotype by comparing the expression level value to a reference, and administering to said patient a therapeutically effective amount of an immune checkpoint inhibitor and/or other therapies on the basis of the spatial immune phenotype of said solid tumor.
  • ICI useful in aspects of this invention include, but are not limited to PD-1 inhibitors, PD-L1 inhibitors, CTLA-4 inhibitors, LAG3 inhibitors, as well as stimulatory antibodies directed against CD40 or other costimulatory receptors, or other ICI, and combinations thereof.
  • PD-1 inhibitors useful in aspects of this invention include, but are not limited to, Pembrolizumab, Nivolumab, Cemiplimab, JTX-4014, Spartalizumab, Camrelizumab, Sintilimab, Tislelizumab, Toripalimab, Dostarlimab, INCMGA00012 (MGA012), AMP-224 and AMP-514.
  • PD-L1 inhibitors useful in aspects of this invention include, but are not limited to Atezolizumab, Avelumab, Durvalumab, KN035, CK-301, AUNP12, CA-170, and BMS-986189.
  • CTLA-4 inhibitors useful in aspects of this invention include, but are not limited to Ipilimumab.
  • LAG3 inhibitors useful in aspects of this invention include, but are not limited to, relatlimab.
  • Stimulatory antibodies directed against CD40 or other costimulatory receptors useful in aspects of this invention include, but are not limited to APX005111). Combinations of any of the above are also foreseen.
  • ICI therapy (including ICI monotherapy) will be most effective in subjects having a solid tumor with an inflamed spatial immune phenotype.
  • Subjects having a solid tumor with an excluded or ignored spatial immune phenotype are less likely to respond to ICI monotherapy, with TME-mediated T cell evasion (e.g. determined by resident stromal components) being strongest in the excluded phenotype, intermediate in the ignored phenotype, and weakest in the inflamed phenotype.
  • TME-mediated T cell evasion e.g. determined by resident stromal components
  • the immune-response-mediated T cell evasion e.g.
  • ICI monotherapy In the case of an inflamed phenotype, ICI monotherapy is proposed. In case ICI monotherapy is not effective in patients having a tumor with an inflamed phenotype, a combination of multiple ICIs may provide a suitable treatment. Alternatively, or in combination therewith, therapeutic priming prior to ICI treatment using epigenetic drugs (including, but not limited to 5-azacytidine and valproate) and/or inhibitors of M2 macrophages (including, but not limited to CSF1R inhibitors, e.g.
  • epigenetic drugs including, but not limited to 5-azacytidine and valproate
  • inhibitors of M2 macrophages including, but not limited to CSF1R inhibitors, e.g.
  • pexidartinib may enhance the type I IFN and chemo-attractant pathways, and counteract adaptive immune responses that have occurred in these types of tumors, respectively, and as such further boost the numbers of intra-tumoral T cells that are prone to ICI-directed re-activation.
  • therapeutic priming prior to ICI treatment is proposed using inhibitors of TGF ⁇ (including, but not limited to the bifunctional anti-PDL-1 mAb/TGFh trap M7824), and inhibitors of VEGF receptor kinases (including, but not limited to cediranib), which are to enhance migration of CD8+ T cells from the tumor margin towards the tumor center and to enhance the activation of intra-tumoral CD8+ T cells.
  • therapeutic priming prior to ICI treatment is proposed using blockers of the WNT pathway (including, but not limited to WNT974) and/or drugs that target M2 macrophages (including, but not limited to CSF1R inhibitors, e.g. pexidartinib), which are to enhance infiltration of CD8+ T cells into the tumor, and the activation of intra- tumoral CD8+ T cells.
  • blockers of the WNT pathway including, but not limited to WNT974
  • drugs that target M2 macrophages including, but not limited to CSF1R inhibitors, e.g. pexidartinib
  • treatment regimens in aspects of this invention may include treatment with immunotherapeutic agents in combination with chemotherapeutic agents and/or radiation therapy (including external beam radiation therapy, high dose rate brachytherapy, (targeted-) radionuclide therapy, and hyperthermia), and/or surgery including surgical resection of a tumor.
  • chemotherapeutic agents and/or radiation therapy including external beam radiation therapy, high dose rate brachytherapy, (targeted-) radionuclide therapy, and hyperthermia
  • surgery including surgical resection of a tumor.
  • Embodiment 1 A method for typing a tumor micro-environment (TME) of a solid tumor, comprising the steps of:
  • TME of said solid tumor of said subject is T cell -inflamed or non-T cell-inflamed on the basis of the comparison of said measured gene expression level and said reference.
  • Embodiment 2 The method according to embodiment 1, wherein said method comprises measuring the gene expression level for:
  • Embodiment 3 The method according to embodiment 2, wherein said method is a method for typing a TME of a solid tumor as being of the immune phenotype T cell-inflamed, T cell-excluded or T cell-ignored; wherein said reference comprises the gene expression level of said at least one gene in at least one reference sample of each of said three immune phenotypes, and wherein said TME of said solid tumor is typed as being T cell-inflamed, T cell-excluded or T cell-ignored on the basis of the comparison of said measured gene expression level and said reference.
  • Embodiment 4 The method according to any one of the preceding embodiments, wherein said gene expression level is measured for at least 2 genes selected from each of group 1, group 2, and/or group 3.
  • Embodiment 5 The method according to any one of the preceding embodiments, wherein said gene expression level is measured for at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15 genes selected from group 1, and at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13, genes selected from group 2, and at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14 genes selected from group 3.
  • Embodiment 6 The method according to any one of the preceding embodiments, wherein said gene expression level is measured for IGHGl, NKG7, IL2RG, IL7R, CCL18, PVRIG, PLAC8, SIRPG, CORO 1 A, LCK, TRBC1, GZMB, CCL5, CXCL13, WARS, COL5A1, SPON1, CAMK2N1, FAP, SPOCK1, COL1A1, SCGB2A1, AKR1C2, CPE, SCGB2A2, TCN1, TPSABl, MMP2, PERP, THBS2, ASPN, COL10A1, TUFT1, GREM1, CEACAM6, ENTPD3, IGFBP5, PBX1, CXADR, GPRC5A, SDCl and CALML5.
  • Embodiment 7 The method according to any one of the preceding embodiments, wherein the solid tumor is selected from the group formed by BRCA: breast carcinoma such as triple negative breast cancer (TNBC); CESC: cervical squamous cell carcinoma and endocervical adenocarcinoma; HNSCC: head and neck squamous cell carcinoma; KICH: chromophobe renal cell carcinoma; BLCA: bladder urothelial carcinoma, and SKCM: skin cutaneous melanoma.
  • BRCA breast carcinoma such as triple negative breast cancer (TNBC)
  • CESC cervical squamous cell carcinoma and endocervical adenocarcinoma
  • HNSCC head and neck squamous cell carcinoma
  • KICH chromophobe renal cell carcinoma
  • BLCA bladder urothelial carcinoma
  • SKCM skin cutaneous melanoma.
  • Embodiment 8 The method according to embodiment 7, wherein the solid tumor is a triple negative breast cancer (TNBC).
  • TNBC triple negative breast cancer
  • Embodiment 9 The method according to embodiment 7 or embodiment 8, wherein the solid tumor is a primary tumor, recurrent tumor or a secondary (metastasized) tumor.
  • Embodiment 10 The method according to any one of the preceding embodiments, wherein measuring the gene expression level is performed by qPCR, microarray analysis or next- generation sequencing (NGS).
  • NGS next- generation sequencing
  • Embodiment 11 The method according to any one of the previous embodiments, wherein said method for typing is a method for predicting the prognosis of a subject with a solid tumor, and wherein, when said TME of a sohd tumor from said subject is typed as T cell-inflamed said subject has a favorable prognosis, and wherein, when said TME of a solid tumor from said subject is typed as non-T cell-inflamed, preferably typed as being T cell- excluded or T cell-ignored, said subject has an unfavorable prognosis.
  • Embodiment 12 A method for assigning a subject having a solid tumor comprising a TME to an immunotherapy-responsive or immunotherapy- unresponsive group, such as ICI, said method comprising the steps of:
  • Embodiment 13 An immunotherapeutic agent, such as ICI, for use in the treatment of a subject having a solid tumor comprising a TME, wherein said subject is assigned to the immunotherapy-responsive group according to a method for assigning according to embodiment 12.
  • an immunotherapeutic agent such as ICI, for use in the treatment of a subject having a solid tumor comprising a TME, wherein said subject is assigned to the immunotherapy-responsive group according to a method for assigning according to embodiment 12.
  • Embodiment 14 An immunotherapeutic agent for use in the treatment of a subject having a solid tumor comprising a TME, wherein said subject is assigned to the immunotherapy-unresponsive group according to a method for assigning according to embodiment 12 and wherein said inflam oefd said solid tumor is typed as being T cell-ignored; and wherein said immunotherapeutic agent is for administration in combination with a WNT inhibitor or with an inhibitor of M2 macrophages, such as colony stimulating factor 1 receptor (CSF 1) inhibitor.
  • a WNT inhibitor or with an inhibitor of M2 macrophages, such as colony stimulating factor 1 receptor (CSF 1) inhibitor.
  • CSF 1 receptor colony stimulating factor 1 receptor
  • Embodiment 15 An immunotherapeutic agent for use in the treatment of a subject having a solid tumor comprising a TME, wherein said subject is assigned to the immunotherapy-unresponsive group according to a method for assigning according to embodiment 12 and wherein said inflam oefd said solid tumor is typed as being T cell-excluded; and wherein said immunotherapeutic agent is for administration in combination with a TGF ⁇ inhibitor and/or VEGF inhibitor.
  • Embodiment 16 The immunotherapeutic agent for use according to any one of embodiments 13-15, wherein said immunotherapeutic agent is an immune checkpoint inhibitor (ICI).
  • ICI immune checkpoint inhibitor
  • Embodiment 17 A method for predicting a prognosis for a subject with a solid tumor comprising a TME, said method comprising the steps of: - measuring in a sample of a solid tumor comprising a TME from a subject a gene expression level for:
  • Example 1 Spatial immune phenotypes predict response to anti- PD1 treatment and capture distinct paths of T cell evasion in solid tumors such as triple negative breast cancer.
  • Cohort A Node-negative, primary TNBC from patients who did not receive adjuvant treatment.
  • Stromal TILs were scored independently, according to an accepted international standard from the International Immuno-Oncology Biomarker Working Group (available via the World Wide Web: http://www.tilsinbreastcancer.org for all guidelines on TIL assessment in solid tumors).
  • PD-L1 stainings (PD-L1 IHC 22C3 pharmDx assay (Agilent Dako)) were assessed and the percentage of positive tumor-infiltrating immune cells was scored.
  • Table 2 and Figure 1 show clinical details and application of these cohorts.
  • BLCA bladder cancer
  • SKCM skin-cutaneous melanoma
  • LUAD lung adenocarcinoma
  • HNSC head and neck squamous-cell carcinoma
  • PRAD prostate adenocarcinoma
  • PAAD pancreatic adenocarcinoma
  • COAD colorectal adenocarcinoma
  • CESC cervical squamous cell carcinoma and endocervical adenocarcinoma
  • KICH chromophobe renal cell carcinoma.
  • IHC was performed on TNBC whole tissue sections (FFPE) comprising different histological subtypes, which were assigned by pathologists. IHC stainings were performed following heat-induced antigen retrieval for 20 min at 95°C. After cooling to RT, staining was visualized by the anti-mouse EnVision+® System-HRP (DAB) (DakoCytomation).
  • DAB anti-mouse EnVision+® System-HRP
  • CD8 C8/C144B, Sanio, 1:100, pH 9
  • CD3 PSl, Sigma, 1:25, pH 6
  • CD4 4B12, DAKO, 1:80, pH 9
  • CD 137 BBK-2, Santa Cruz, 1:80, pH 6
  • CD278 SP98, Thermo Fisher, 1:50, pH 9
  • CD66b 80H3, BIO-RAD, 1:100, pH 9
  • MECA-79 Cl 11-6, Santa Cruz, 1:50, pH 9
  • MHC-II LN3, Thermo Fisher, 1:50, pH 9
  • Multiplexed IF was performed using OPAL reagents (Akoya Biosciences) on whole shdes (using a randomly selected subset of cohort A with comparable fractions of all spatial phenotypes).
  • stainings included multiple cycles of: antigen retrieval (15 min boiling in antigen retrieval buffer, pH 6 or pH 9 depending on primary antibodies) followed by cooling, blocking, and consecutive staining with primary antibodies, HRP -polymer and Opal fluorophores; cycles were repeated until all markers were stained. Finally, nuclei were stained with DAPI.
  • CD56 (MRQ-42, Sanbio, 1:500) - OPAL620; 2. CD3 (SP7, Sigma, 1:350) - OPAL520; 3. CD20 (L26, Sanbio, 1:1000) - OPAL650; 4. CD8 (C8/144b, Sanbio, 1:250) - OPAL570; 5. CD68 (KP-1, Sanbio, 1:250) - OPAL540; 6. Cytokeratin-Pan (AE1/AE3, Thermofisher, 1:200) - OPAL690; 7. DAPI. Spatial phenotype panel (number indicates position of primary antibody):
  • IHC was scored for the frequency of CD8+ T cells at the border and in the center (illustrated in Figure 2A).
  • the border region includes the invasive margin, and covers ⁇ 50% tumoral area (tumor cells and stroma) and ⁇ 50% peritumoral area (no or only isolated tumor cells, particularly in case of ILC subtypes), whereas the center region includes non-necrotic regions, and covers tumor and stroma.
  • LN lymph node
  • Spatial phenotype of CD8+ T cells was determined using whole slide scans (Hamamatsu slide scanner) at lx magnification and using at least 8 regions of interest at 20x magnification in border and center.
  • Scoring criteria were as follows: inflamed: almost equal frequencies of CD8+ T cells at the border and center; excluded: >10 times more CD8+ T cells at the border compared to center; and ignored: hardly any CD8+ T cells present at the border and center. All immune markers stained on consecutive slides were scored at 20x magnification (at border and center) and reported as percentage of positive cells (of total nuclei). Tertiary lymphoid structures (TLS) were identified as dense clusters of CD4+ T cells and CD20+ B cells on consecutive slides, whereas High endothelial venules (HEV) were identified as vessels that were MECA-79 positive (frequently found in TLS), and both TLS and HEV were reported as total number per tumor.
  • TLS Tertiary lymphoid structures
  • HEV High endothelial venules
  • stamps regions of interest; stamp size: 670x502 mm 2 ; resolution: 2 pixels/mm 2 ; pixel size: 0.5x0.5 mm 2 ) were set in non-necrotic areas at the tumor border (containing 50% peritumoral region) and center (both comprising tumor as well as stroma compartments, illustrated in Figure 3).
  • stamps regions of interest; stamp size: 670x502 mm 2 ; resolution: 2 pixels/mm 2 ; pixel size: 0.5x0.5 mm 2
  • fewer stamps were set or tissues were excluded from analysis (in case of ⁇ 3 stamps at either border or center regions).
  • Tissue-segmentation was performed according to cytokeratin and DAPI staining; cell-segmentation and phenotyping of individual cells was performed according to individual markers and presence of DAPI using Inform software; and enumerations at border (tumor and stroma) and center regions (tumor and stroma) were summarized for all stamps per sample.
  • Spatial phenotypes were determined according to median CD8 + T cell density at border and center as follows: inflamed, >200 cells/mm 2 at border and ratio between border and center ⁇ 10; excluded, >200 cells/mm 2 at border and ratio between border and center >10; ignored ⁇ 150 cells/mm 2 at border and center. All scans fulfilled either of these 3 spatial phenotypes.
  • Collagen- 10 was identified through tissue segmentation and quantified as collagen- 10-positive tissue area. Nearest-neighbor analysis was performed in R using the PhenoptR package, to which end, the number of non-CD8 + T cells within a 10 mm radius of CD8 + T cells were calculated from the Inform -derived cell segmentation files in Phyton.
  • RNAseq data was collected from fresh frozen TNBC using 150bp paired-end with LncRNA library (Ribo-zero RNA) on Illumina HiSeq.
  • RNA was isolated from FFPE using the RecoverAll Total Nucleic Acid Isolation Kit for FFPE (Thermofisher).
  • RNA was sequenced using the FFPE sample Eukaryotic RNA-seq Library (250 ⁇ 300 bp insert strand specific library with rRNA removal) on the Illumina Novoseq6000 platform at Novogene.
  • RNAseq data (cohorts A2, C, D E (TNBC)) were aligned with GRCh38 using the STAR algorithm (version 2.4.2a) and geTMM normalized (Smid et al., BMC Bioinformatics, 19(1): 1-13 (2016)) for differential expression (DE) analyses.
  • TCGA Cancer Genome Atlas
  • BRCA breast cancer
  • EB++Adjusted EB++Adjusted
  • ICI-treated melanoma patients FPKM normalized
  • TCR clonality was estimated using the MIXCR algorithm as described previously (Hammerl et al., Clin Cancer Res.,doi: 10.1158/1078-0432. CCR- 19-0285 (2019)); output was processed with tcR package in R and reported as TCR diversity (total number of TCR-Vb reads per sample) and TCR repertoire skewness (Gini-Simpson index of TCR-Vb reads per sample). Prediction of neo-antigens was performed with netCTLpan as described previously (Hammerl et al., Clin Cancer Res.,doi: 10.1158/1078-0432. CCR- 19-0285 (2019); Smid et al., Nat Commun., 7:12910 (2016)).
  • RNAseq data of independent samples with corresponding CD8+ T cell staining data were used to assign phenotypes based on highest rank-correlations with the discovery set (Al), and yielded 81% accuracy ( Figure 4A).
  • Correct assignment of unknown samples from Cohort B was verified by comparison of T cell characteristics, such as TCR-Vb repertoire diversity and numbers of intra- tumoral T cells, with those of Cohort A2 (RNAseq and CD8+ T cell stainings), and the classifier-assigned samples were found non -different compared to those from the validation set ( Figure 5A, B).
  • Predictive value of classifier gene-sets was determined by fitting ROC curves for anti-PDl response. Responders (CR, PR, SD > 24 weeks) and non-responders (PD) were separated using the pROC package in R. Excluded and inflamed gene- sets were calculated as average scores of all respective genes, and PD-L1 and sTIL scores were scored as described before 8 .
  • CD8+ T cells In addition to CD8+ T cells, we assessed the presence of other immune effector cells using multiplexed immunofluorescence (IF) imaging of 64 tumors (Figure 6C, Figure 2B).
  • CD4+ T cells and CD20+ B cells generally co-occurred with CD8+ T cells at the tumor border and center, whereas CD56+ NK cells were hardly present in TNBC ( Figure 6D, Figure 3A-D).
  • Figure 6D Figure 3A-D
  • CD4+ and CD8+ T cells did not differ between excluded and inflamed phenotypes, yet the excluded phenotype had significantly fewer intratumoral B and T cells (Figure 3A, C).
  • TLS tertiary lymphoid structures
  • a gene classifier of spatial phenotypes predicts outcome to anti-PDl treatment in TNBC patients
  • genes highly expressed in the inflamed phenotype included: WARS, CXCL13, CCL5, GZMB, TRBC1, COROlA, CCL5, CCL18, IL2RG, NKG7, IGHGl, which were all significantly associated with better MFS (HR ⁇ 1, p ⁇ 0.05) (Figure 5A).
  • ROC as a measure of predictive value of the excluded, inflamed or combined gene-sets, we observed areas under the curve (AUC) of 0.72 (CL0.52-0.89), 0.73 (Cl: 0.54-0.94) and 0.75 (Cl: 0.55-0.95), respectively.
  • AUC areas under the curve
  • PD-L1 expression on immune cells a biomarker that is currently used in the clinical setting had an AUC of 0.66 (Cl: 0.51-0.82) ( Figure 7F).
  • the AUC for sTIL, another marker considered to stratify patients was 0.67 (Cl: 0.48-0.82) ( Figure 7F).
  • Spatial phenotypes were significantly prognostic not only in invasive breast cancer BRCA (all subtypes, including ER+), but also in bladder cancer (BLCA), skin cutaneous melanoma (SKCM), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), head and neck squamous cell carcinoma (HNSC) and kidney cancer (RICH) (Cohort E, Figure 5C).
  • BLCA bladder cancer
  • SKCM skin cutaneous melanoma
  • CESC cervical squamous cell carcinoma and endocervical adenocarcinoma
  • HNSC head and neck squamous cell carcinoma
  • RICH kidney cancer
  • the inflamed phenotype showed enhanced expression of genes associated with necrosis, TNF-signaling, type-I and type-II IFN, antigen processing and presentation, T cell co-stimulation, but also co-inhibition ( Figures 10B, C), which were all inter-related (data not shown).
  • the inflamed phenotype showed high gene expression of the T cell chemo-attractants CXCL9 and CXCL10 ( Figure IOC), which according to our immune cell deconvolution and pathway analyses are derived from activated (BATF3/CLEC9A-positive) conventional DC (cDCl, Figure 10G).
  • CD 163 and T cell co-inhibition were correlated with the expression of CD8A (Figure 10H).
  • Multiplex IF demonstrated that collagen- 10 was deposited into stromal areas between tumor and immune cells at the tumor center in the excluded phenotype, ( Figure 11A, B).
  • HEV high endothelial venules
  • Figure 11D high endothelial venules
  • the spatial immuophenotype classifier acts independently of PD-L1 (data not shown) and outperfoms alternative classifiers that relate to lymphocyte activity and location (Figure 8).
  • TLS whether captured by staining (as performed in this study) or a gene signature, was neither significantly associated with survival nor anti-PDl response, irrespective of stratification per spatial immune phenotype (data not shown), indicating that further research is needed to determine the exact role of TLS in shaping anti-tumor immune responses in TNBC.
  • NGS-techniques expected to become part of systemic evaluations of tumor tissues for targetable alterations in the near future at departments of Pathology of Medical Centers, can be used towards the application of the gene classifier.
  • proportions of inflamed phenotype increase following induction treatment with cisplatin and doxorubicin ( Figure 12), suggesting that spatial phenotypes show plasticity and non-inflamed phenotypes can be primed for treatment with ICI.
  • the immune determinants that characterize these phenotypes ( Figure 10D) and their correlative relationships with actionable targets ( Figure 10E, F, G, H) provide a rationale for below-mentioned co-treatments (illustrated in Figure 13, and discussed below).
  • inhibitors of TGFb such as the bifunctional anti-PDL-1 mAb/TGFb trap M7824, and inhibitors of VEGF receptor kinases, such as cediranib, both being in clinical development for TNBC and the latter being FDA-approved for other malignancies, can potentially prime for ICI.
  • blockers of the WNT pathway such as WNT974 and/or drugs that target M2 macrophages, such as pexidartinib, a CSF1R inhibitor that depletes M2 macrophages, are of interest, and are currently being tested in TNBC.
  • the inflamed phenotype being enriched in patients responding to anti-PDl treatment, would be the phenotype of choice to start combination ICI treatment. In case ICIs are not effective, this phenotype could potentially benefit from combining multiple ICIs or priming with CSF1R inhibitors that target M2 macrophages.
  • Another mode of priming the inflamed phenotype could be reactivation of type I/II IFN and/or chemoattractant pathways, thereby re-boosting antigen presentation as well as recruitment and function of intra-tumoral CD8 T cells; to this end, an option could be the epigenetic drug decitabine that is approved for other indications and has shown promising results in preclinical studies of TNBC.
  • the above-mentioned targets are part of larger immune networks that were revealed upon integrative analyses of TNBC samples using NGS and multiplexed IF.
  • the charting of these larger networks enabled the identification of TME- and immune response-mediated paths of T cell evasion and their relationship to ICI response.
  • the excluded phenotype was characterized by CD4+, CD8+, CD20+ and CD56+ lymphocytes that were preferentially located at the tumor border at large distances from tumor cells.
  • This phenotype had high expression of collagen- 10, which is not present in normal tissues, is associated with epithelial-to-mesenchymal transition, as weU as poor survival in TNBC and various other tumor types.
  • the ignored phenotype was characterized by no or very low densities of CD8+ T cells and either showed high expression of target genes of the WNT and PPARg/RXR pathways or contained CD 163+ macrophages and CD66b+ neutrophils.
  • the inflamed phenotype was characterized by high numbers of intra-tumoral CLEC9A+ DC and lymphocytes.
  • the prognostic value of TILs was mainly attributed to T and B cells located in tumor regions, a finding that is in line with earlier observations showing that proximity to tumor cells is a pre-requisite for effective anti-tumor activity of lymphocytes.
  • the inflamed phenotype had a high TCR clonality independent of the level of neo-antigens and showed highest expression of genes associated with immunogenic cell death, type I/II IFNs and chemo-attractants (Figure 10G).
  • TILs in the inflamed phenotype over-expressed genes encoding for various immune checkpoints and only a minority of TILs expressed ICOS or 41BB (Figure 11F).
  • a large fraction of the inflamed phenotype showed genetic alterations in MHC-I (Figure 9B) and down-regulated expression of MHC-II by tumor cells ( Figure HE). All the above changes are again inter-related (data not shown) and considered part of an immune response-mediated negative-feedback loop (Figure 10H, 5th and 6th plots), and may contribute to the relatively low frequency of sustained clinical responses to ICI even in the inflamed phenotype.

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Abstract

La présente invention concerne un procédé de typage d'un micro-environnement tumoral (TME) d'une tumeur solide comme étant du phénotype immun à inflammation des lymphocytes T, à exclusion des lymphocytes T ou à ignorance des lymphocytes T, comprenant les étapes suivantes : - fourniture d'un échantillon de test d'une tumeur solide comprenant un TME prélevé sur un sujet ; - mesure dans ledit échantillon de test du niveau d'expression génique pour : (i) au moins un gène choisi dans le groupe 1 constitué par IGHG1, NKG7, IL2RG, IL7R, CCL18, PVRIG, PLAC8, CCL5, SIRPG, CORO1A, LCK, TRBC1, GZMB, CXCL13 et WARS ; et (ii) au moins un gène choisi dans le groupe 2 constitué de COL5A1, SPON1, CAMK2N1, FAP, SPOCK1, COL1A1, SCGB2A1, AKR1C2, CPE, SCGB2A2, TCN1, TPSAB1, et MMP2 ; et (iii) au moins un gène choisi dans le groupe 3 constitué de PERP, THBS2, ASPN, COL10A1, TUFT1, GREM1, CEACAM6, ENTPD3, IGFBP5, PBX1, CXADR, GPRC5A, SDC1 et CALML5 ; - comparaison des niveaux d'expression génique mesurés de l'échantillon testé à un niveau d'expression génique de référence, et - typage de la TME de ladite tumeur solide dudit sujet comme étant à inflammation des lymphocytes T, à exclusion des lymphocytes T ou à ignorance des lymphocytes T en se fondant sur la comparaison dudit niveau d'expression génique mesuré et dudit niveau d'expression génique de référence.
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