Signatures for metastatic urothelial cancer

Abstract

The present invention relates to methods for determining whether a subject suffering from a metastatic urothelial cancer (mUC) will achieve a response with an Immune checkpoint inhibitors (ICI) therapy, as well as methods of treatment of the subjects depending on their response to ICI therapy.

Description

SIGNATURES FOR METASTATIC UROTHELIAL CANCER FIELD OF INVENTION

[0001] The present invention relates to methods for determining whether a subject suffering from a metastatic urothelial cancer (mUC) will achieve a response with an Immune checkpoint inhibitors (ICI) therapy, as well as methods of treatment of the subjects depending on their response to ICI therapy.BACKGROUND OF INVENTION

[0002] Urothelial cancers include bladder and upper tract cancers, which account for more than 200,000 deaths worldwide each year. Upon metastatic presentation, the disease is most often incurable with a median survival of less than 2 years.

[0003] Therapeutic strategies are being developed for treating patients with metastatic urothelial cancers (mUC), including the use of immune checkpoint inhibitors (ICI). Indeed, it was shown, for example, that ICI improved survival after chemotherapy as a maintenance or second-line treatment. Inhibition of PD-(L)1 is now a first-line treatment option in combination with cisplatin-based chemotherapy or with the antibody-drug conjugate enfortumab-vedotin.

[0004] However, although ICI therapy improved survival in patients with mUC, most patients experience disease progression. Options at resistance to ICI, include, for example, fibroblast growth factor (FGFR) 2 / 3 inhibitors or enfortumab-vedotin (for patients who did not receive it previously). Despite increasing options to elaborate therapeutic strategies, no reliable biomarker yet helped refine patient selection. Thus, there is a need to identify patients which may benefit from ICI therapy to refine patient selection and adapt therapeutic strategy.

[0005] The Applicants have identified biomarkers related to tumor features, immune features, and macrophage features, which could serve as valuable predictors of ICI treatment success, helping to determine whether a patient is likely to respond to ICI.SUMMARY

[0006] The present invention relates to a method for determining whether a subject suffering from a metastatic urothelial cancer (mUC) will achieve a response with an ICI therapy, said method comprising:(i) determining, in a biological sample previously obtained from the subject, at least one of the signatures below:(a) Signature relative to macrophages (Macrophage signature),(b) Signature relative to T cells (T cell signature), and / or (c) Signature relative to tumor cells (Tumor cell signature), wherein the determination of the Macrophage signature (a) comprises measuring at least one of the parameters below:al) the proportion of HES1 macrophages among total macrophages,a2) the proportion of TREM2 macrophages among total macrophages, a3) the expression levels of at least one gene selected from the group consisting of CD 163, SAMSN1, MS4A4A and VSIG4 in macrophages, and / ora4) the expression levels of at least one of the genes CLEC5A and HLA-DRA in macrophages,wherein the determination of the T cell signature (b) comprises measuring at least one of the parameters below:bl) the proportion of CD8 T cells among total T cells,b2) the proportion of CD 8 effector T cells among total T cells,b3) the proportion of CD8 exhausted T cells among total T cells,b4) the expression levels of at least one gene selected from the group consisting otPDCDl, TIGIT, LAGS, and HAVCR2 in T cells, and / orb5) the expression levels of at least one of the genes GZMA / B, in T cells,wherein the determination of the Tumor cell signature (c) comprises measuring a parameter cl) being the proportion of basal tumor cells among total tumor cells, and(ii) comparing the at least one signature obtained at step (i) with a reference signature, and(iii) predicting whether the subject will achieve a response with ICI therapy based on the correlation of the at least one signature obtained at step (i) with the reference signature.

[0007] In one embodiment, the determination of the Macrophage signature (a) comprises measuring two, three, or the four parameters al) to a4).

[0008] In one embodiment, measuring the parameter a3) comprises measuring the expression levels of the genes CD163, SAMSN1, MS4A4A and VSIG4 in macrophages.

[0009] In one embodiment, measuring the parameter a4) comprises measuring the expression levels of the genes CLEC5A and HLA-DRA in macrophages.

[0010] In one embodiment, the determination of the T cell signature (b) comprises measuring two, three, four, or the five parameters bl) to b5).

[0011] In one embodiment, measuring the parameter b4) comprises measuring the expression levels of the genes PDCD1, TIGIT, LAG3, and HAVCR2 in T cells.

[0012] In one embodiment, the method comprises the determination of two or the three signatures Macrophage signature (a), T cell signature (b) and Tumor signature (c).

[0013] In one embodiment, the method comprises the determination of the three signatures Macrophage signature (a), T cell signature (b) and Tumor signature (c), wherein the determination of the Macrophage signature (a) comprises measuring the four parameters al) to a4), and wherein the determination of the T cell signature (b) comprises measuring the five parameters bl) to b5).

[0014] In one embodiment, the biological sample previously obtained from a subject is a tumour biopsy.

[0015] In one embodiment, it is predicted that the subject will achieve a response with ICI therapy if the at least one signature obtained at step (i) is different from a reference signature obtained from a reference population.

[0016] In one embodiment, the reference signature is obtained as follows:for the Macrophage signature (a), by measuring at least one of the parameters al) to a4) in a reference population,for the T cell signature (b), by measuring at least one of the parameters bl) to b5) in a reference population, and / orfor the Tumor signature (c), by measuring the parameter cl) in a reference population.

[0017] In one embodiment, the reference population is a population of subjects suffering from mUC, treated with an ICI therapy and for which the response to ICI is already known.

[0018] In one embodiment, the ICI therapy is an anti-PD-1 inhibitor or an anti-PD-Ll inhibitor, preferably an ICI therapy selected from the group consisting of pembrolizumab, nivolumab, atezolizumab, durvalumab, and avelumab.DEFINITIONS

[0019] In the present invention, the following terms have the following meanings:

[0020] “Mammal” refers to any mammal, including humans, non-human primates, domestic and farm animals, and zoo, sports, or pet animals, such as dogs, cats, cattle, horses, sheep, pigs, goats, rabbits, monkeys, etc. Preferably, the mammal is a primate, more preferably a human.

[0021] ‘ ‘Parameter’ ’ refers to a variable whose measure is indicative of a signature as described herein.

[0022] “Signature”, as used herein, refers to a panel of one or more parameter(s), which allows to predict the likelihood of a subject suffering from a mUC to respond to an ICI therapy or the risk of a subject suffering from a mUC to have or develop resistance to an ICI therapy.

[0023] “Subject” refers to a mammal, preferably a human. A subject may be a “patient”, i.e., a warm-blooded animal, more preferably a human, who / which is awaiting the receipt of, or is receiving medical care or was / is / will be the object of a medical procedure, or is monitored for the development of a disease.

[0024] “Therapeutically efficient amount” refers to the level or the amount of the active agent, in particular an immune checkpoint inhibitor (ICI), which is aimed at, without causing significant negative or adverse side effects to the target, (1) delaying or preventing the onset of cancer; (2) slowing down or stopping the progression, aggravation, or deterioration of one or more symptoms of cancer; (3) bringing about amelioration of the symptoms of cancer; (4) reducing the severity or incidence of cancer; or (5) curing cancer. A therapeutically effective amount may be administered prior to the onset of cancer, for a prophylactic or preventive action. Alternatively, or additionally, the therapeutically effective amount may be administered after the onset of cancer for a therapeutic action. In one embodiment, a therapeutically effective amount of the pharmaceutical composition is an amount that is effective in reducing at least one symptom of cancer.

[0025] “Treating” or “treatment” or “alleviation” refers to both therapeutic treatment and prophylactic or preventative measures, wherein the object is to prevent or slow down (lessen) cancer. Those in need of treatment include those already with cancer, as well as those prone to develop cancer or those in whom cancer is to be prevented. An individual is successfully “treated” for cancer, if, after receiving a therapeutic amount of a therapy (e.g. ICI therapy), the individual shows observable and / or measurable reduction in or absence of one or more of the symptoms associated with cancer; reduced morbidity and mortality, and improvement in quality of life issues. The above parameters for assessing successful treatment and improvement of cancer are readily measurable by routine procedures familiar to physician or authorized personnel.DETAILED DESCRIPTION

[0026] This invention relates to a method for determining whether a subject suffering from a metastatic urothelial cancer (mUC) will achieve a response with an ICI therapy, said method comprising:(i) determining, in a biological sample previously obtained from the subject, at least one of the signatures below:(a) Signature relative to macrophages (Macrophage signature),(b) Signature relative to T cells (T cell signature), and / or(c) Signature relative to tumor cells (Tumor cell signature), wherein the determination of the Macrophage signature (a) comprises measuring at least one of the parameters below:al) the proportion of HES1 macrophages among total macrophages,a2) the proportion of TREM2 macrophages among total macrophages,a3) the expression levels of at least one immunosuppressive gene involved in M2 macrophage polarization, in particular at least one gene selected from the group comprising or consisting of CD 163, SAMSN1, MS4A4A and VSIG4, in macrophages, and / ora4) the expression levels of at least one gene involved in Ml macrophage polarization, in particular at least one of the genes CLEC5A and HLA-DRA, in macrophages, wherein the determination of the T cell signature (b) comprises measuring at least one of the parameters below:bl) the proportion of CD8 T cells among total T cells,b2) the proportion of CD8 effector T cells among total T cells,b3) the proportion of CD8 exhausted T cells among total T cells,b4) the expression levels of at least one immune checkpoint gene, in particular at least one gene selected from the group comprising or consisting of PDCD1, T1G1T, LAG3, and HAVCR2, in T cells, and / orb5) the expression levels of at least one marker gene of effector CD8 T cells, in particular at least one of the genes GZMA / B, in T cells,wherein the determination of the Tumor cell signature (c) comprises measuring a parameter cl) being the proportion of basal tumor cells among total tumor cells, and (ii) comparing the at least one signature obtained at step (i) with a reference signature, and(iii) predicting whether the subject will achieve a response with ICI therapy based on the correlation of the at least one signature obtained at step (i) with the reference signature.

[0027] The method as described herein may consist of the steps (i), (ii) and (iii) as described herein.

[0028] The method as described herein may comprise determining one signature among Macrophage signature (a), T cell signature (b) and Tumor cell signature (c). The method as described herein may comprise determining Macrophage signature (a). The method as described herein may comprise determining T cell signature (b). The method as described herein may comprise determining Tumor cell signature (c).

[0029] The method as described herein may comprise determining two signatures among Macrophage signature (a), T cell signature (b) and Tumor cell signature (c). The method as described herein may comprise determining Macrophage signature (a) and T cell signature (b). The method as described herein may comprise determining Macrophage signature (a) and Tumor cell signature (c). The method as described herein may comprise determining T cell signature (b) and Tumor cell signature (c).

[0030] The method as described herein may comprise determining the three signatures Macrophage signature (a), T cell signature (b) and Tumor cell signature (c).

[0031] In other words, the method as described herein may comprise a step (i) of determining one signature among Macrophage signature (a), T cell signature (b) and Tumor cell signature (c), a step (i) of determining two signatures among Macrophage signature (a), T cell signature (b) and Tumor cell signature (c), or a step (i) of determining the three signatures Macrophage signature (a), T cell signature (b) and Tumor cell signature (c).

[0032] The determination of the Macrophage signature (a) may comprise or consist of measuring one parameter among the parameters al) to a4).

[0033] In particular, the determination of the Macrophage signature (a) may comprise or consist of measuring the parameter al). Thus, the method as described herein may comprise a step (i) of determining at least one signature, the at least one signature being the Macrophage signature (a), wherein the determination of the Macrophage signature (a) comprises or consists of measuring the parameter al). In particular, the method as described herein may comprise a step (i) of determining the Macrophage signature (a), wherein the determination of the Macrophage signature (a) comprises or consists of measuring the parameter al).

[0034] The determination of the Macrophage signature (a) may comprise or consist of measuring the parameter a2). The determination of the Macrophage signature (a) may comprise or consist of measuring the parameter a3). The determination of the Macrophage signature (a) may comprise or consist of measuring the parameter a4).

[0035] The determination of the Macrophage signature (a) may comprise or consist of measuring two parameters among the parameters al) to a4). In particular, the determination of the Macrophage signature (a) may comprise or consist of measuring the parameters: al) and a2); al) and a3); al) and a4); a2) and a3); a2) and a4); or a3) and a4).

[0036] The determination of the Macrophage signature (a) may comprise or consist of measuring three parameters among the parameters al) to a4). In particular, the determination of the Macrophage signature (a) may comprise or consist of measuring parameters al), a2) and a3); al), a2) and a4); al), a3) and a4); or a2), a3) and a4).

[0037] The determination of the Macrophage signature (a) may comprise or consist of measuring the four parameters al) to a4).

[0038] According to the invention, the determination of the Macrophage signature (a) may comprise or consist of measuring:the parameter al) being the proportion of HES1 macrophages among total macrophages, and / orthe parameter a2) being the proportion of TREM2 macrophages among total macrophages.

[0039] Markers for identifying macrophages and subtypes thereof are well known by the skilled artisan.

[0040] For example, the macrophages may be identified as HES1 macrophages by using specific marker genes, and in particular, the following marker genes: SEPPI, SLC40A1, PLTP, F13A1, FOLR2, RNASE1, CCL18, LGMN, EGRI, MAF, DAB2, C1QA, C1QC, STAB1, PDK4, A2M, Cl QB, SLCO2B1, JUN, NR4A2, LYVE1, HSPA1B, FOSB, FUCA1, SDC3, HSPA1A, ARL4C, GPR34, DNAJB1, CCE4, CXCL12, IGF1, EIERB5, ATF3, CD163, MS4A7, MS4A4A, CD209, VCAM1, MS4A6A, RGS1, HM0X1, MRC1, HES1, ZNF331, CPM, EIP A, CCE13, APOE, and HSPH1.

[0041] For example, the macrophages may be identified as TREM2 macrophages by using specific marker genes, and in particular, the following marker genes: SPP1, APOE, APOCI, GPNMB, CTSD, CHI3L1, FABP5, CHIT1, TREM2, ACP5, CTSL, CSTB, CTSB, CD9, LIPA, LGMN, MMP12, CCL18, FN1, PLD3, GM2A, MMP9, PLA2G7, LGALS3, RNASE1, CTSZ, NUPR1, TIMP3, FTL, SDC2, CYP27A1, CD63, CD68, FPL, PSAP, VAT1, CD81, MGLL, SDS, LAMP1, CAPG, OTOA, ANXA2, GCHFR, CALR, HAMP, CD59, GRN, CCL2, and FAM20C.

[0042] According to the invention, the determination of the Macrophage signature (a) may comprise or consist of measuring the parameter a3) being the expression levels of at least one immunosuppressive gene involved in M2 macrophage polarization, in particular at least one gene selected from the group comprising or consisting of CD 163, SAMSN1, MS4A4A and VSIG4. in macrophages.

[0043] As used herein, the expression “immunosuppressive gene involved in M2 macrophage polarization” refers to immunosuppressive genes associated with macrophages polarization into M2 macrophages.

[0044] Measuring the parameter a3) may comprise or consist of measuring the expression levels of one, two, three or the four gene(s) selected from the group comprisingor consisting of CD 163, SAMSN1, MS4A4A and VS1G4. In particular, measuring the parameter a3) may comprise or consist of measuring the expression levels of the four genes CD 163, SAMSN1, MS4A4A and VS1G4.

[0045] According to the invention, the determination of the Macrophage signature (a) may comprise or consist of measuring the parameter a4) being the expression levels of at least one gene involved in Ml macrophage polarization, in particular at least one of the genes CLEC5A and HLA-DRA, in macrophages.

[0046] As used herein, the expression “gene involved in Ml macrophage polarization” refers to genes associated with macrophages polarization into Ml macrophages.

[0047] Measuring the parameter a4) may comprise or consist of measuring the expression levels of one or the two genes CLEC5A and HEA-DRA.

[0048] The determination of the T cell signature (b) may comprise or consist of measuring one parameter among the parameters bl) to b5).

[0049] The determination of the T cell signature (b) may comprise or consist of measuring the parameter bl). The determination of the T cell signature (b) may comprise or consist of measuring the parameter b2). The determination of the T cell signature (b) may comprise or consist of measuring the parameter b3). The determination of the T cell signature (b) may comprise or consist of measuring the parameter b4). The determination of the T cell signature (b) may comprise or consist of measuring the parameter b5).

[0050] The determination of the T cell signature (b) may comprise or consist of measuring two parameters among the parameters bl) to b5). In particular, the determination of the T cell signature (b) may comprise or consist of measuring the parameters: bl) and b2); bl) and b3); bl) and b4); bl) and b5); b2) and b3); b2) and b4); b2) and b5); b3) and b4); b3) and b5); or b4) and b5).

[0051] The determination of the T cell signature (b) may comprise or consist of measuring three parameters among the parameters bl) to b5). In particular, the determination of the T cell signature (b) may comprise or consist of measuring the parameters: bl), b2) and b3); bl), b2) and b4); bl), b2) andb5); bl), b3) and b4); bl), b3)and b5); bl), b4) and b5), b2), b3) and b4) ; b2), b3) and b5); b2), b4) and b5) or b3), b4) and b5).

[0052] The determination of the T cell signature (b) may comprise or consist of measuring four parameters among the parameters bl) to b5). In particular, the determination of the T cell signature (b) may comprise or consist of measuring the parameters: bl), b2), b3) and b4); bl), b2), b3) and b5); bl), b2), b4) and b5); b2), b3), b4) and b5); or bl), b3), b4) and b5).

[0053] The determination of the T cell signature (b) may comprise or consist of measuring the five parameters bl) to b5).

[0054] According to the invention, the determination of the T cell signature (b) may comprise or consist of measuring:the parameter bl) being the proportion of CD8 T cells among total T cells, and / or the parameter b2) being the proportion of CD8 effector T cells among total T cells, and / or,the parameter b3) being the proportion of CD8 exhausted T cells among total T cells.

[0055] Markers for identifying CD8 T cells and subtypes thereof are well known by the skilled artisan.

[0056] For example, the T cells may be identified as CD8 T cells by using specific marker genes, and in particular, the following marker genes: CD8A, CD8B, GZMB, PRF1, KLRD1, NKG7, IFNG, CCL4, and EOMES.

[0057] For example, the T cells may be identified as CD8 effector T cells by using specific marker genes, and in particular, the following marker genes: FAS, FASEG, CD44, CD69, CD38, NKG7, KLRB1, KLRD1, KLRF1, KLRG1, KLRK1, FCGR3A, CX3CR1, CD300A, FGFBP2, ID2, IDS, PRDM1, RUNX3, TBX21, ZEB2, BATE, IRF4, NR4A1, NR4A2, NR4A3, PBX3, ZNF683, HOPX, FOS, FOSB, JUN, JUNB, JUND, STAT1, STAT2, STAT5A, STAT6, STAT4, EOMES, GZMA, GZMB, GZMH, GZMK, GNLY, PRF1,IFNG, TNF, SERPINBI, SERP1NB6, SERPINB9, CTSA, CTSB, CTSC, CTSD, CTSW, CST3, CST7, CSTB, LAMP1, LAMPS, and CAPN2.

[0058] For example, the T cells may be identified as CD8 exhausted T cells by using specific marker genes, and in particular, the following marker genes: PDCD1, LAYN, HAVCR2, LAGS, CD244, CTLA4, LILRB1, TIGIT, TOX, VSIR, BTLA, ENTPD1, CD160, and LA1R1.

[0059] According to the invention, the determination of the T cell signature (b) may comprise or consist of measuring the parameter b4) being the expression levels of at least one immune checkpoint gene, in particular at least one gene selected from the group comprising or consisting of PDCD1, TIGIT, LAGS, and HAVCR2, in T cells.

[0060] As used herein, the expression “immune checkpoint gene” refers to genes encoding immune checkpoint proteins.

[0061] Measuring parameter b4) may comprise or consist of measuring the expression levels of one, two, three or the four gene(s) selected from the group comprising or consisting of PDCD1, TIGIT, LAGS, and HAVCR2. In particular, measuring parameter b4) may comprise or consist of measuring the expression levels of the four genes PDCD1, TIGIT, LAGS, and HAVCR2.

[0062] According to the invention, the determination of the T cell signature (b) may comprise or consist of measuring the parameter b5) being the expression levels of at least one marker gene of effector CD8 T cells, in particular at least one of the genes GZMA / B, in T cells.

[0063] Measuring parameter b5) may comprise or consist of measuring the expression levels of the gene GZMA in T cells. Measuring parameter b5) may comprise or consist of measuring the expression levels of the gene GZMB in T cells. Measuring parameter b5) may comprise or consist of measuring the expression levels of the genes GZMA / B in T cells.

[0064] According to the invention, the determination of the Tumor cell signature (c) comprises measuring the parameter cl) being the proportion of basal tumor cells amongtotal tumor cells. In particular, the determination of the Tumor cell signature (c) may consist of measuring the parameter cl) being the proportion of basal tumor cells among total tumor cells.

[0065] Means for identifying basal tumor cells are known in the art.

[0066] The method as described herein may comprise the determination of the Tumor signature (c), and at least one of the Macrophage signature (a) and T cell signature (b), wherein:the determination of the Macrophage signature (a) comprises measuring at least one of the four parameters al) to a4), and / orthe determination of the T cell signature (b) comprises measuring at least one of the five parameters bl) to b5).

[0067] The method as described herein may comprise the determination of the three signatures Macrophage signature (a), T cell signature (b) and Tumor signature (c), wherein:the determination of the Macrophage signature (a) comprises measuring at least one of the four parameters al) to a4),the determination of the T cell signature (b) comprises measuring at least one of the five parameters bl) to b5), andthe determination of the Tumor signature (c) comprises measuring the parameter cl).

[0068] The method as described herein may comprise the determination of the three signatures Macrophage signature (a), T cell signature (b) and Tumor signature (c), wherein:the determination of the Macrophage signature (a) comprises or consists of measuring two of the four parameters al) to a4),the determination of the T cell signature (b) comprises or consists of measuring two of the five parameters bl) to b5), andthe determination of the Tumor signature (c) comprises or consists of measuring the parameter cl).

[0069] The method as described herein may comprise the determination of the three signatures Macrophage signature (a), T cell signature (b) and Tumor signature (c), wherein:the determination of the Macrophage signature (a) comprises or consists of measuring the four parameters al) to a4),the determination of the T cell signature (b) comprises or consists of measuring the five parameters bl) to b5), andthe determination of the Tumor signature (c) comprises or consists of measuring the parameter cl).

[0070] This method as described herein is based on the determination of at least one signature in a biological sample previously obtained from the subject, and thus may be considered as an in vitro method.

[0071] The sample may be a tumor biopsy. In particular, the sample may be any tumor biopsy from a subject suffering from a mUC (z.e. tumor from any localization).

[0072] Thus, the sample may be, for example, a tumor biopsy from lung, lymph node, soft tissue, liver or bone.

[0073] According to the invention, the method as described herein is for determining whether a subject suffering from a mUC will achieve a response with an ICI therapy.

[0074] In particular, the method as described herein may be used to predict the response of a subject suffering from a mUC to ICI therapy, and to optimize the treatment of the subject. It will be understood to the skilled artisan in the art that, if the subject is predicted to respond to ICI therapy, the ICI therapy may be favored over other lines of treatments. On the reverse, if the subject is predicted not to respond to ICI therapy, another line of therapy (z.e. other than ICI therapy) may be favored.

[0075] As used herein, the term “ICI” refers to molecules, often antibodies, that block the interactions between inhibitory receptors (IRs) expressed on T cells and their ligands.

[0076] Examples of ICI include, without being limited to, inhibitors of the cell surface receptor PD-1 (programmed cell death protein 1), also known as CD279 (clusterdifferentiation 279); inhibitors of the ligand PD-L1 (programmed death-ligand 1), also known as CD274 (cluster of differentiation 274) or B7-H1 (B7 homolog 1); inhibitors of the cell surface receptor CTLA4 or CTLA-4 (cytotoxic T-lymphocyte-associated protein 4), also known as CD 152 (cluster of differentiation 152); inhibitors of LAG-3 (lymphocyte-activation gene 3), also known as CD223 (cluster differentiation 223); inhibitors of TIM-3 (T-cell immunoglobulin and mucin-domain containing-3), also known as HAVCR2 (hepatitis A virus cellular receptor 2) or CD366 (cluster differentiation 366); inhibitors of TIGIT (T cell immunoreceptor with Ig and ITIM domains), also known as VSIG9 (V-Set And Immunoglobulin Domain-Containing Protein 9) or VSTM3 (V-Set And Transmembrane Domain-Containing Protein 3); inhibitors of BTLA (B and T lymphocyte attenuator), also known as CD272 (cluster differentiation 272); inhibitors of CEACAM-1 (carcinoembryonic antigen-related cell adhesion molecule 1) also known as CD66a (cluster differentiation 66a).

[0077] In particular, the ICI may be an anti-PD-1 inhibitor or an anti-PD-Ll inhibitor.

[0078] Examples of inhibitors of PD-1 include, without limitation, pembrolizumab and nivolumab. Examples of inhibitors of PD-L1 include, without limitation, atezolizumab, durvalumab and avelumab.

[0079] The ICI may be selected from the group comprising or consisting of pembrolizumab, nivolumab, atezolizumab, durvalumab and avelumab.

[0080] The ICI may be selected from the group comprising or consisting of avelumab, durvalumab and pembrolizumab.

[0081] The ICI may be pembrolizumab. The ICI may be nivolumab. The ICI may be atezolizumab. The ICI may be durvalumab. The ICI may be avelumab.

[0082] Thus, the method as described herein may be used to predict whether a subject suffering from a mUC will achieve a response with an ICI therapy as described above, in particular, an anti-PD-1 inhibitor or an anti-PD-Ll inhibitor, and more particularly, an ICI therapy selected from the group comprising or consisting of pembrolizumab, nivolumab, atezolizumab, durvalumab and avelumab.

[0083] The method as described herein may comprise measuring the expression levels of at least one gene as described herein.

[0084] As used herein, the term “expression” may refer alternatively to expression at the transcription level (i.e. expression of the RNA) or expression at the translation level (i.e. expression of the protein encoded by the gene) of the genes as described herein.

[0085] Methods for determining the expression levels of genes are well-known from the skilled artisan, and include, without limitation, determining the transcriptome or the proteome associated with the genes as described herein.

[0086] The expression of the genes as described herein may be assessed at the RNA level.

[0087] Methods for assessing the expression of a gene at a transcription level are well known in the prior art. Examples of such methods include, but are not limited to, RT-PCR, RT-qPCR, Northern Blot, hybridization techniques such as, for example, use of microarrays, and combination thereof including but not limited to, hybridization of amplicons obtained by RT-PCR, sequencing such as, for example, next- generation DNA sequencing (NGS) or RNA-seq (also known as “Whole Transcriptome Shotgun Sequencing”) and the like, and spatial transcriptomics.

[0088] In particular, the expression of the genes may be assessed by RNA-seq, including bulk RNA-sequencing or single cell RNA-sequencing. In particular, the expression of the genes may be assessed by spatial transcriptomics.

[0089] The expression of the genes as described herein may be assessed at the protein level.

[0090] In vitro methods for determining the expression of a gene at a translation level in a sample are well-known in the art. Examples of such methods include, but are not limited to, immunohistochemistry, Multiplex methods (Luminex), Western blot, enzyme-linked immunosorbent assay (ELISA), sandwich ELISA, fluorescent-linked immunosorbent assay (FLISA), enzyme immunoassay (EIA), radioimmunoassay (RIA), flow cytometry (FACS) and the like.

[0091] According to the invention, the method as described herein comprises a step (ii) of comparing the at least one signature obtained at step (i) with a reference signature.

[0092] It will be understood to the skilled artisan in the art that, when the method comprises the determination of only one signature (z.e. one signature among Macrophage signature (a), T cell signature (b) and Tumor cell signature (c)), the reference signature may be the determination of the same signature in a reference population. For example, if the method comprises the determination of Macrophage signature (a) only, the reference signature may be the determination of Macrophage signature (a) in a reference population.

[0093] Similarly, when the method comprises the determination of two signatures (z.e. two signatures among Macrophage signature (a), T cell signature (b) and Tumor cell signature (c)), the reference signature may be the determination of the same two signatures in a reference population. For example, if the method comprises the determination of Macrophage signature (a) and T cell signature (b), the reference signature may be the determination of Macrophage signature (a) and T cell signature (b) in a reference population.

[0094] Similarly, when the method comprises the determination of the three signatures Macrophage signature (a), T cell signature (b) and Tumor cell signature (c), the reference signature may be the determination of the same three signatures in a reference population.

[0095] In particular, the reference signature for each signature may be obtained as follows:for the Macrophage signature (a), by measuring at least one of the parameters al) to a4) in a reference population,for the T cell signature (b), by measuring at least one of the parameters bl) to b5) in a reference population, and / orfor the Tumor signature (c), by measuring the parameter cl) in a reference population.

[0096] It will be understood to the skilled artisan in the art that, since each signature may be determined by measuring one or several parameter(s), the reference signature may be obtained by measuring the same parameter(s) in a reference population.

[0097] For each parameter of each signature, a reference value may be obtained by measuring said parameter in a reference population. Thus, the reference signature may comprise one or several reference value(s), depending on the number of parameters measured for determining a given signature (and depending on the number of signatures of the method).

[0098] For example, if the method comprises the determination of Macrophage signature (a) by measuring the parameters al) and a3) and the determination of T cell signature (b) by measuring the parameter b3) and b5), the reference signature may be obtained by measuring the parameters al), a3), b3) and b5) in a reference population, and may comprise a reference value for each of parameters al), a3), b3) and b5).

[0099] Thus, the method as described herein may comprise a step (i) of determining at least one signature, the at least one signature being the Macrophage signature (a), wherein the determination of the Macrophage signature (a) comprises or consists of measuring the parameter al), and a step (ii) of comparing the at least one signature obtained at step (i) with a reference signature, the reference signature being obtained by measuring the parameter al) in a reference population.

[0100] In particular, the method as described herein may comprise a step (i) of determining the Macrophage signature (a), wherein the determination of the Macrophage signature (a) comprises or consists of measuring the parameter al), and a step (ii) of comparing the Macrophage signature (a) obtained at step (i) with a reference signature, the reference signature being obtained by measuring the parameter al) in a reference population.

[0101] The reference population may comprise at least 5, at least 10, at least 50, at least 100, or at least 200 subjects.

[0102] The reference population may be a population of subjects suffering from mUC, treated with an ICI therapy and for which the response to ICI is already known.

[0103] The reference population may be a population of subjects suffering from mUC and which respond to ICI. The reference population may be a population of subjects suffering from mUC and which do not respond to ICI. The reference population may be a population of subjects suffering from mUC and may comprise subjects which do respond to ICI and subjects which do not respond to ICI.

[0104] It may be considered that a subject responds to the ICI if the administration of said ICI induces a positive response, i.e. a clinical benefit.

[0105] As used herein, a clinical benefit is a favorable effect on a meaningful aspect of how a subject feels (e.g., symptom relief), functions (e.g., improved mobility) or survives as a result of the treatment. Clinical benefit may be measured as an improvement or delay in the progression of cancer.

[0106] According to the invention, the method as described herein comprises a step (iii) of predicting whether the subject will achieve a response with ICI therapy based on the correlation of the at least one signature obtained at step (i) with the reference signature.

[0107] The step (iii) may comprise a step of comparing the at least one signature obtained at step (i) with a reference signature obtained from a population of reference, and in particular, a population of subjects suffering from mUC, treated with an ICI therapy, and for which the response to ICI is already known.

[0108] It may be predicted that the subject will achieve a response with ICI therapy if the at least one signature obtained at step (i) is different from a reference signature obtained from a reference population as described herein, in particular a population of subjects suffering from mUC, treated with an ICI therapy and for which the response to ICI is already known.

[0109] It may be predicted that the subject will achieve a response with ICI therapy if the value of a given parameter measured in said subject is different from the value of the same parameter measured in a reference population.

[0110] It may be predicted that the subject will not achieve a response with ICI therapy if at least one of the parameters al), a2) and a3) of the Macrophage signature (a) (i.e. one, two or three of these parameters) is increased (or that the subject will achieve a response with ICI therapy if at least one of these parameters is decreased), preferably as compared to the same parameter(s) determined in a reference population.

[0111] Thus, the method as described herein may comprise a step (i) of determining at least one signature, the at least one signature being the Macrophage signature (a), wherein the determination of the Macrophage signature (a) comprises or consists of measuring the parameter al), a step (ii) of comparing the at least one signature obtained at step (i) with at least one reference signature, the at least one reference signature being obtained by measuring the parameter al) in a reference population, and a step (iii) of predicting that the subject will not achieve a response with ICI therapy if at least the parameter al) obtained at step (i) is increased as compared to the same parameter determined in a reference population.

[0112] In particular, the method as described herein may comprise a step (i) of determining the Macrophage signature (a), wherein the determination of the Macrophage signature (a) comprises or consists of measuring the parameter al), a step (ii) of comparing the Macrophage signature (a) obtained at step (i) with a reference signature, the reference signature being obtained by measuring the parameter al) in a reference population, and a step (iii) of predicting that the subject will not achieve a response with ICI therapy if the parameter al) obtained at step (i) is increased as compared to the same parameter determined in a reference population.

[0113] It may be predicted that the subject will not achieve a response with ICI therapy if the parameter a4) of the Macrophage signature (a) is decreased (or that the subject will achieve a response with ICI therapy if said parameter is increased), preferably as compared to the same parameter(s) determined in a reference population.

[0114] It may be predicted that the subject will not achieve a response with ICI therapy if at least one (i.e. one, two, three, four or five) parameter of the T cell signature (b), in particular at least one parameter selected among the parameters bl), b2), b3), b4) and b5),is increased (or that the subject will achieve a response with ICI therapy if at least one of these parameters is decreased), preferably as compared to the same parameter(s) determined in a reference population.

[0115] It may be predicted that the subject will not achieve a response with ICI therapy if the parameter cl) of the Tumor cell signature (c) is decreased (or that the subject will achieve a response with ICI therapy if said parameter is increased), preferably as compared to the same parameter(s) determined in a reference population.

[0116] The subject suffering from mUC may be diagnosed with mUC.

[0117] Means for diagnosing mUC include, without limitation, cystoscopy examination.

[0118] The subject may have been or may be under cancer therapy. In particular, the subject may have been or may be under a cancer therapy which is not ICI therapy. Examples of therapies other than ICI therapy are described herein.

[0119] The present invention also relates to a method for identifying a subject being at risk of having or developing resistance to ICI therapy, wherein said subject suffers from a mUC, said method comprising:(i) determining, in a biological sample previously obtained from the subject, at least one of the signatures below:(a) Signature relative to macrophages (Macrophage signature), (b) Signature relative to T cells (T cell signature), and / or(c) Signature relative to tumor cells (Tumor cell signature), wherein the determination of the Macrophage signature (a) comprises measuring at least one of the parameters below:al) the proportion of HES1 macrophages among total macrophages,a2) the proportion of TREM2 macrophages among total macrophages, a3) the expression levels of at least one immunosuppressive gene involved in M2 macrophage polarization, in particular at least one gene selected from the group consisting of CD 163, SAMSN1, MS4A4A and VSIG4. in macrophages, and / or a4) the expression levels of at least one of the genes CLEC5A and HLA-DRA in macrophages,wherein the determination of the T cell signature (b) comprises measuring at least one of the parameters below:bl) the proportion of CD8 T cells among total T cells,b2) the proportion of CD 8 effector T cells among total T cells,b3) the proportion of CD8 exhausted T cells among total T cells,b4) the expression levels of at least one gene selected from the group consisting otPDCDl, TIGIT, LAGS, and HAVCR2 in T cells, and / orb5) the expression levels of at least one marker gene of effector CD8 T cells, in particular at least one of the genes GZMA / B, in T cells,wherein the determination of the Tumor cell signature (c) comprises measuring a step cl) being the proportion of basal tumor cells among total tumor cells, and(ii) comparing the at least one signature obtained at step (i) with a reference signature, and(iii) identifying the subject as being at risk of having or developing resistance to ICI therapy, based on the correlation of the at least one signature obtained at step (i) with the reference signature.

[0120] The embodiments which have been detailed hereinabove in the frame of the method for determining whether a subject suffering from a mUC will achieve a response with an ICI therapy apply mutatis mutandis to the method for identifying a subject being at risk of having or developing resistance to ICI therapy.

[0121] According to the invention, the method as described herein comprises a step (iii) of identifying that the subject is at risk of having or developing resistance to ICI therapy based on the correlation of the at least one signature obtained at step (i) with the reference signature.

[0122] It may be concluded that the subject is at risk of having or developing resistance to ICI therapy if the at least one signature obtained at step (i) is different from a reference signature obtained from a reference population as described herein, in particular a population of subjects suffering from mUC and for which the response to ICI is already known.

[0123] It may be predicted that the subject is at risk of having or developing resistance to ICI therapy if the value of a given parameter measured in said subject is different from the value of the same parameter measured in a reference population.

[0124] It may be predicted that the subject is at risk of having or developing resistance to ICI therapy if at least of the parameters al), a2) and a3) of the Macrophage signature (a) (i.e. one, two or three of these parameters) is increased (or that the subject is not at risk of having or developing resistance to ICI therapy if at least one of these parameters is decreased), preferably as compared to the same parameter(s) determined in a reference population.

[0125] Thus, the method as described herein may comprise a step (i) of determining at least one signature, the at least one signature being the Macrophage signature (a), wherein the determination of the Macrophage signature (a) comprises or consists of measuring the parameter al), a step (ii) of comparing the at least one signature obtained at step (i) with at least one reference signature, the at least one reference signature being obtained by measuring the parameter al) in a reference population, and a step (iii) of identifying the subject as being at risk of having or developing resistance to ICI therapy if at least the parameter al) obtained at step (i) is increased as compared to the same parameter determined in a reference population.

[0126] In particular, the method as described herein may comprise a step (i) of determining the Macrophage signature (a), wherein the determination of the Macrophage signature (a) comprises or consists of measuring the parameter al), a step (ii) of comparing the Macrophage signature (a) obtained at step (i) with a reference signature, the reference signature being obtained by measuring the parameter al) in a reference population, and a step (iii) of identifying the subject as being at risk of having or developing resistance to ICI therapy if the parameter al) obtained at step (i) is increased as compared to the same parameter determined in a reference population.

[0127] It may be predicted that the subject is at risk of having or developing resistance to ICI therapy if the parameter a4) of the Macrophage signature (a) is decreased (or thatthe subject will achieve a response with ICI therapy if said parameter is increased), preferably as compared to the same parameter(s) determined in a reference population

[0128] It may be predicted that the subject is at risk of having or developing resistance to ICI therapy if at least one (i.e. one, two, three, four or five) parameter of the T cell signature (b), in particular at least one parameter selected among the parameters bl), b2), b3), b4) and b5), is increased (or that the subject is not at risk of having or developing resistance to ICI therapy if at least one of these parameters is decreased), preferably as compared to the same parameter(s) determined in a reference population.

[0129] It may be predicted that the subject is at risk of having or developing resistance to ICI therapy if the parameter cl) of the Tumor cell signature (c) is decreased (or that the subject is not at risk of having or developing resistance to ICI therapy if said parameter is increased), preferably as compared to the same parameter(s) determined in a reference population.

[0130] The present invention also relates to a method for treating a subject suffering from a mUC, said method comprising:predicting whether the subject will achieve a response with ICI therapy or identifying whether the subject is at risk of having or developing resistance to ICI therapy with a method as described herein, andtreating said subject.

[0131] The method as described may be used to treat a subj ect with an adapted treatment, depending on the prediction of response of the subject to ICI therapy or the risk of having or developing resistance to ICI therapy.

[0132] The method as described herein may comprise the steps of:predicting whether the subject will achieve a response with ICI therapy by:(i) determining, in a biological sample previously obtained from the subject, at least one of the signatures below:(a) Signature relative to macrophages (Macrophage signature), (b) Signature relative to T cells (T cell signature), and / or(c) Signature relative to tumor cells (Tumor cell signature),wherein the determination of the Macrophage signature (a) comprises measuring at least one of the parameters below:al) the proportion of HES1 macrophages among total macrophages,a2) the proportion of TREM2 macrophages among total macrophages,a3) the expression levels of at least one immunosuppressive gene involved in M2 macrophage polarization, in particular at least one gene selected from the group comprising or consisting of CD 163, SAMSN1, MS4A4A and VSIG4, in macrophages, and / ora4) the expression levels of at least one gene involved in Ml macrophage polarization, in particular at least one of the genes CLEC5A and HLA-DRA, in macrophages, wherein the determination of the T cell signature (b) comprises measuring at least one of the parameters below:bl) the proportion of CD8 T cells among total T cells,b2) the proportion of CD8 effector T cells among total T cells,b3) the proportion of CD8 exhausted T cells among total T cells,b4) the expression levels of at least one immune checkpoint gene, in particular at least one gene selected from the group comprising or consisting of PDCD1, T1G1T, LAG3, and HAVCR2, in T cells, and / orb5) the expression levels of at least one marker gene of effector CD8 T cells, in particular at least one of the genes GZMA / B, in T cells,wherein the determination of the Tumor cell signature (c) comprises measuring a parameter cl) being the proportion of basal tumor cells among total tumor cells, and (ii) comparing the at least one signature obtained at step (i) with a reference signature, and(iii) predicting whether the subject will achieve a response with ICI therapy based on the correlation of the at least one signature obtained at step (i) with the reference signature, andtreating the subject with an adapted treatment depending on the prediction of response of the subject to ICI therapy.

[0133] The method as described herein may comprise the steps of:identifying whether the subject is at risk of having or developing resistance to ICI therapy by:(i) determining, in a biological sample previously obtained from the subject, at least one of the signatures below:(a) Signature relative to macrophages (Macrophage signature), (b) Signature relative to T cells (T cell signature), and / or(c) Signature relative to tumor cells (Tumor cell signature), wherein the determination of the Macrophage signature (a) comprises measuring at least one of the parameters below:al) the proportion of HES1 macrophages among total macrophages,a2) the proportion of TREM2 macrophages among total macrophages, a3) the expression levels of at least one immunosuppressive gene involved in M2 macrophage polarization, in particular at least one gene selected from the group consisting of CD 163, SAMSN1, MS4A4A and VSIG4 in macrophages, and / or a4) the expression levels of at least one of the genes CLEC5A and HLA-DRA in macrophages,wherein the determination of the T cell signature (b) comprises measuring at least one of the parameters below:bl) the proportion of CD8 T cells among total T cells,b2) the proportion of CD 8 effector T cells among total T cells,b3) the proportion of CD8 exhausted T cells among total T cells,b4) the expression levels of at least one gene selected from the group consisting otPDCDl, TIGIT, LAGS, and HAVCR2 in T cells, and / orb5) the expression levels of at least one of the genes GZMA / B in T cells, wherein the determination of the Tumor cell signature (c) comprises measuring a step cl) being the proportion of basal tumor cells among total tumor cells, and(ii) comparing the at least one signature obtained at step (i) with a reference signature, and(iii) identifying whether the subject is at risk of having or developing resistance to ICI therapy, based on the correlation of the at least one signature obtained at step (i) with the reference signature, andtreating the subject with an adapted treatment depending on the risk of the subject for having or developing resistance to ICI therapy.

[0134] The embodiments which have been detailed hereinabove in the frame of the method for determining whether a subject suffering from a mUC will achieve a response with an ICI therapy apply mutatis mutandis to the method of treatment.

[0135] The embodiments which have been detailed hereinabove in the frame of the method for identifying a subject being at risk of having or developing resistance to ICI therapy, wherein said subject suffers from a mUC apply mutatis mutandis to the method of treatment.

[0136] The method as described herein may comprise treating the subject with the ICI therapy if the subject is predicted to respond to ICI therapy.

[0137] Thus, the method as described herein may comprise the steps of:predicting that the subject will achieve a response with ICI therapy by:(i) determining, in a biological sample previously obtained from the subject, at least one of the signatures below:(a) Signature relative to macrophages (Macrophage signature), (b) Signature relative to T cells (T cell signature), and / or(c) Signature relative to tumor cells (Tumor cell signature),wherein the determination of the Macrophage signature (a) comprises measuring at least one of the parameters below:al) the proportion of HES1 macrophages among total macrophages,a2) the proportion of TREM2+ macrophages among total macrophages,a3) the expression levels of at least one immunosuppressive gene involved in M2 macrophage polarization, in particular at least one gene selected from the group comprising or consisting of CD 163, SAMSN1, MS4A4A and VSIG4, in macrophages, and / ora4) the expression levels of at least one gene involved in Ml macrophage polarization, in particular at least one of CLEC5A and HLA-DRA, in macrophages,wherein the determination of the T cell signature (b) comprises measuring at least one of the parameters below:bl) the proportion of CD8 T cells among total T cells,b2) the proportion of CD8 effector T cells among total T cells,b3) the proportion of CD8 exhausted T cells among total T cells,b4) the expression levels of at least one immune checkpoint gene, in particular at least one gene selected from the group comprising or consisting of PDCD1, T1G1T, LAG3, and HAVCR2, in T cells, and / orb5) the expression levels of at least one marker gene of effector CD8 T cells, in particular at least one of the genes GZMA / B, in T cells,wherein the determination of the Tumor cell signature (c) comprises measuring a parameter cl) being the proportion of basal tumor cells among total tumor cells, and (ii) comparing the at least one signature obtained at step (i) with a reference signature, and(iii) predicting that the subject will achieve a response with ICI therapy based on the correlation of the at least one signature obtained at step (i) with the reference signature, andtreating the subject with ICI therapy.

[0138] The method as described herein may comprise the steps of:identifying a subject predicted to achieve a response with ICI therapy by:(i) determining, in a biological sample previously obtained from the subject, at least one of the signatures below:(a) Signature relative to macrophages (Macrophage signature), (b) Signature relative to T cells (T cell signature), and / or(c) Signature relative to tumor cells (Tumor cell signature), wherein the determination of the Macrophage signature (a) comprises measuring at least one of the parameters below:al) the proportion of HES1 macrophages among total macrophages,a2) the proportion of TREM2+ macrophages among total macrophages,a3) the expression levels of at least one immunosuppressive gene involved in M2 macrophage polarization, in particular at least one gene selected from the groupcomprising or consisting of CD 163, SAMSN1, MS4A4A and VSIG4, in macrophages, and / ora4) the expression levels of at least one gene involved in Ml macrophage polarization, in particular at least one of CLEC5A and HLA-DRA, in macrophages,wherein the determination of the T cell signature (b) comprises measuring at least one of the parameters below:bl) the proportion of CD8 T cells among total T cells,b2) the proportion of CD8 effector T cells among total T cells,b3) the proportion of CD8 exhausted T cells among total T cells,b4) the expression levels of at least one immune checkpoint gene, in particular at least one gene selected from the group comprising or consisting of PDCD1, T1G1T, LAG3, and HAVCR2, in T cells, and / orb5) the expression levels of at least one marker gene of effector CD8 T cells, in particular at least one of the genes GZMA / B, in T cells,wherein the determination of the Tumor cell signature (c) comprises measuring a parameter cl) being the proportion of basal tumor cells among total tumor cells, and (ii) comparing the at least one signature obtained at step (i) with a reference signature, and(iii) identifying a subject predicted to achieve a response with ICI therapy based on the correlation of the at least one signature obtained at step (i) with the reference signature, andtreating the subject with ICI therapy.

[0139] The method as described herein may comprise treating the subject with the ICI therapy if the subject is identified as not being at risk of having or developing resistance to ICI therapy.

[0140] Thus, the method as described herein may comprise the steps of:identifying the subject as not being at risk of having or developing resistance to ICI therapy by:(i) determining, in a biological sample previously obtained from the subject, at least one of the signatures below:(a) Signature relative to macrophages (Macrophage signature), (b) Signature relative to T cells (T cell signature), and / or(c) Signature relative to tumor cells (Tumor cell signature), wherein the determination of the Macrophage signature (a) comprises measuring at least one of the parameters below:al) the proportion of HES1 macrophages among total macrophages,a2) the proportion of TREM2 macrophages among total macrophages, a3) the expression levels of at least one immunosuppressive gene involved in M2 macrophage polarization, in particular at least one gene selected from the group consisting of CD 163, SAMSN1, MS4A4A and VSIG4 in macrophages, and / or a4) the expression levels of at least one of CLEC5A and HLA-DRA in macrophages,wherein the determination of the T cell signature (b) comprises measuring at least one of the parameters below:bl) the proportion of CD 8 T cells among total T cells,b2) the proportion of CD 8 effector T cells among total T cells,b3) the proportion of CD8 exhausted T cells among total T cells,b4) the expression levels of at least one gene selected from the group consisting otPDCDl, TIGIT, LAGS, and HAVCR2 in T cells, and / orb5) the expression levels of at least one of the genes GZMA / B in T cells, wherein the determination of the Tumor cell signature (c) comprises measuring a step cl) being the proportion of basal tumor cells among total tumor cells, and(ii) comparing the at least one signature obtained at step (i) with a reference signature, and(iii) identifying the subject as not being at risk of having or developing resistance to ICI therapy, based on the correlation of the at least one signature obtained at step (i) with the reference signature, andtreating the subject with ICI therapy.

[0141] Examples of ICI are described herein.

[0142] The subj ect may be treated with a therapeutic ally effective amount of ICI therapy .

[0143] It will be understood to the skilled artisan in the art that the subject may be treated with ICI therapy, in combination with another therapy. Examples of other therapies include, without limitation, chemotherapy, such as cisplatin-based chemotherapy, and antibody-drug conjugates, such as enfortumab-vedotin or disitamab vedotin.

[0144] The method as described herein may comprise treating the subject with a line of therapy other than ICI therapy if the subject is predicted not to respond to ICI therapy.

[0145] The method as described herein may comprise treating the subject with a line of therapy other than ICI therapy if the subject is identified as being at risk of having or developing resistance to ICI therapy.

[0146] Examples of therapies other than ICI therapy are described herein. Examples of therapies other than ICI therapy are chemotherapy, such as cisplatin-based chemotherapy, and antibody-drug conjugates, such as enfortumab-vedotin or disitamab vedotin.BRIEF DESCRIPTION OF THE DRAWINGS

[0147] Figure 1 is a combination of graphs showing outcomes according to pseudobulk tumor classification (A) and single-cell states (B).

[0148] Figure 2 is a graph showing T cell subpopulations and T cell-related genes associated with outcomes on ICI.

[0149] Figure 3 is a combination of graphs showing expression of PDCD1 (A) and GZMA (B) and response to ICI (Non-resp.: non responder to ICI, Resp.: responder to ICI).

[0150] Figure 4 is a graph showing Macrophage subpopulations and Macrophage polarization genes associated with outcomes on ICI.

[0151] Figure5 is a combination of graphs showing abundance of M2 macrophages (A) and HES1 macrophages (B) and response to ICI (Non-resp.: non responder to ICI, Resp.: responder to ICI).

[0152] Figure 6 is a combination of graphs showing the Kaplan-Meier survival curves according to abundance of M2 macrophages (A) and of HES1 macrophages (B).

[0153] Figure 7 is a graph showing the abundance of the following functional subsets of myeloid cells, assessed by single-nucleus RNA-seq, in metastatic bladder cancers responding (n=7) or not (n=21) to immune checkpoint blockade: C1Q macrophages, HES1 macrophages, IL4I1 macrophages, other macrophages, ISG monocytes, other monocytes, Ml macrophages and M2 macrophages.EXAMPLES

[0154] The present invention is further illustrated by the following examples.Materials and Methods:-nuclei RNA

[0155] Cryopreserved cells were thawed and nucleus permeabilization was performed following recommendations from 10X Genomics. Single-nucleus 5’ gene expression libraries were generated using 10X Genomics single cell gene expression 5’ vl.l kit, following the protocol provided by the manufacturer. Nuclei were loadedon the Chromium Single Cell Controller. Libraries were sequenced on a NovaSeq 6000 to a minimum depth of 300 million read pairs (Paired-end, 28bp Read 1, 90bp Read 2).Single-nuclei RNA-seq

[0156] We used a custom workflow to process raw FASTQ fles. The UMI counts matrices were generated using STAR alignment (Dobin, A. et al. Bioinformatics. 2013 Jan 1;29(1): 15-21. doi: 10.1093 / bioinformatics / bts635) and count (STARsolo) with the human genome reference (GrCh38 / hg38) and lOx barcode whitelists. We used CellBender (Fleming, S. J. et al. Nat Methods. 2023 Sep;20(9): 1323- 1335. doi: 10.1038 / s41592-023-01943-7) to remove counts from ambient RNA molecules and random barcode swapping with recommended settings. We used Doubletcollection (Xi, N. M. &Li, J. J. STAR Protoc. 2021 Jul 28;2(3): 100699. doi: 10.1016 / j.xpro. 2021.100699.) to detect doublet cells with doubletCells, cxds, beds, hybrid, scDblFinder, Scrublet, DoubletDetection and DoubletFinder algorithms. Each cell detected as doublet by >3 methods was removed. We retained only high-quality cells (>500 genes detected and <10% of mitochondrial RNA reads). The samples were aggregated into one matrix and the cohort matrix was normalized using monocle328normalization method by size factor.Cell annotation

[0157] We used Seurat package (Hao, Y. et al. Nat Biotechnol. 2024 Feb;42(2):293-304. doi: 10.1038 / s41587-023-01767-y.) to generate UMAP (Uniform Manifold Approximation and Projection) visualization of all cells. We first performed a principal component analysis to identify the main principal components of the data set, and we projected the resulting data on a 2D UMAP. We used FindNeighbors and FindClusters to identify transcriptomic clusters, and established marker genes to annotate cell clusters as epithelial cells, endothelial cells, fibroblasts, T cells, B cells, monocytes and macrophages. We used InferCNV to compute a CNV score per cell and annotated clusters corresponding to cancer cells.Bladder cancer cell states

[0158] To explore the heterogeneity of bladder cancer cells, we conducted a PCA analysis and we projected cells on the two first components. We used previously described signatures of urothelial, basal and neuroendocrine cell differentiation30and computed the log-normalized mean expression of each set of marker genes in each cell. The proportion of basal cells in each sample was estimated as the proportion of cells with a log-normalized expression of the basal signature > 0.1. We applied consensusMIBC (Kamoun, A. et al. Eur Urol. 2020 Apr;77(4):420-433. doi: 10.1016 / j.eururo.2019.09.006.) on pseudo-bulk RNA profiles generated by pooling tumor cell profiles to predict the molecular class of each sample.Functional immune signatures

[0159] We used published data sets and signatures to characterize and quantify functional subsets of immune cells. We first defined functional signatures of interest for monocytes and macrophages (MoMac), and for T cells. For MoMac we used Seurat FindMarkers function to identify markers of functional subsets (e.g. HES1 macrophages) defined in the MoMacVerse dataset (Mulder, K. et al. Immunity. 2021 Aug 10;54(8):1883-1900.e5. doi: 10.1016 / j.immuni.2021.07.007.). For each MoMac subset, we used as markers the 50 genes with the highest fold-change compared to other subsets, within significant genes (p-value < 0.05 and fold-change > 1.5). For T cells, we used signatures from a published pan-cancer T cell atlas (Chu, Y. et al. Nat Med. 2023 Jun;29(6): 1550-1562. doi: 10.1038 / s41591-023-02371-y). Then, 2 metrics were used to quantify the activity of each functional signature:- pseudobulk expression was calculated as the sum of read counts per cell on the genes belonging to the signature, divided by the total read counts of the cell, multiplied by a scaling factor (10,000), averaged across cells from the population of interest and log-transformed- counts signature scores were calculated as the % of cells with > 1 count in >30% genes from the signature.Association with clinical response

[0160] To identify predictors of ICI response, we compared cell proportions and functional signatures between responders and non-responders. We used quasi-binomial generalized linear mixed models (GLMM) to compare cell proportions and counts signature scores between responders and non-responders, and Wilcoxon tests to compare pseudobulk expression scores between responders and non-responders. We used log-rank tests to compare the progression-free survival and overall survival between patients with low / high scores for each feature, split by the median.Longitudinal evolution upon treatment

[0161] To identify longitudinal gene expression changes in each cell type, we performed differential expression analyses between pre- and post-treatment samples using SeuratFindMarkers with ‘test.use=MAST’ and ‘latent. var=patient’. For genes of interest, e.g. genes known to be associated with ICI response, and genes significantly deregulated between pre- and post-treatment samples, we generated visualizations showing the proportion of cells expressing each gene and the mean expression level in matched pre / post pairs.of functional subsets of myeloid cells betweenand non-

[0162] We annotated myeloid cells into the functional subsets identified in the MoMacVerse meta-analysis (Mulder, K. et al. Immunity. 2021 Aug 10;54(8): 1883-1900. e5. doi: 10.1016 / j.immuni. 2021.07.007). After integrating and clustering myeloid cells from all samples, we used Seurat FindAllMarkers function to identify markers of each myeloid subset defined in the MoMacVerse data set. For each subset, we used as markers the 50 genes with the highest fold-change compared to other subsets, within significant genes (p-value < 0.05 and fold-change > 1.5). We kept genes with sufficient expression in our data set (at least 1 count in 1% of cells) and removed genes overlapping multiple signatures. Signatures comprising <5 genes were discarded. UCell scores were scaled between 0 and 1 for each signature. Cells with all scores lower than 0.1 were labeled "Unknown", while other cells were labeled according to the myeloid subset with the highest UCell score. The abundance of each myeloid subset was compared between responders and non-responders using Wilcoxon rank- sum test.Results-nuclei profiling of metastatic urothelial carcinoma

[0163] A total of 32 patients with metastatic urothelial carcinoma were included in the MATCH-R trial and treated with single-agent PD-1 or PD-L1 inhibitor (z.e. one of avelumab, durvalumab or pembrolizumab) in the metastatic setting. Median age was 71 (IQR 61-77), and most (66%) had bladder cancer as a primary tumor. Patients were treated either in the first line metastatic setting (28%), second-line (50%), or third-line and more (22%). Most common metastatic sites at immune checkpoint inhibitor initiation were lymph nodes (65%), lung (38%), bone (31%), liver (28%), peritoneal orretroperitoneal soft tissue (28%). Overall, seven (22%) patients achieved objective response (OR), which translated to prolonged benefit to ICI: with a median follow-up of 26.4 months (95% confidence interval [CI] 16.0-35.4), median progression-free survival was 24.7 months (95%CI 17.8-31.0) in patients who achieved OR versus 1.4 months (95%CI 1.0-1.6) in those who did not, and median overall survival not reached versus 4.2 months (95%CI 2.5-6.2) (data not shown).

[0164] To allow for longitudinal assessment of the tumor and its microenvironment on therapy, single-nuclei RNA sequencing was performed in matched sequential biopsies at three different time points: at baseline, 3 months after ICI initiation (for patients who achieved disease control), and at disease progression. Biopsies at baseline were performed in 31 patients (97%), including lung (23%), lymph node (19%), soft tissue (19%), liver (13%) or bone (9%) metastases. Seventeen patients (53%) had a biopsy at disease progression, while five (16%) had an interval biopsy on-therapy. Overall, 55 samples were collected generating single-nuclei transcriptomes from 235 913 cells, with a median number of 4573 cells per sample, and a median gene coverage of 2299 (data not shown).

[0165] Main immune cell populations were annotated according to marker gene expression, and tumor cells by marker gene expression and presence of copy number variations. Tumor cells were the most abundant cell subtype (data not shown).Tumor cell signature

[0166] We next searched to identify tumor cells features associated with resistance or sensibility to ICI. To do so, we explored tumor cell differentiation states.

[0167] Molecular signatures were assessed through bulk RNA sequencing data in the single-nuclei dataset. Using a pseudo bulk approach to classify samples based on aggregated single-nuclei transcriptome, it was observed a high concordance between single-nuclei and bulk tumor classifications (data not shown). Principal component analysis using basal, luminal and neuroendocrine differentiation signatures (Biton, A. et al. Cell Rep. 2014 Nov 20;9(4): 1235-45. doi: 10.1016 / j.celrep.2014.10.035) hinted at a continuum between luminal and basal phenotypes, with a unique component that wasboth strongly associated with the luminal signature and anticorrelated with the basal signature (data not shown).

[0168] Besides, it was explored whether tumor cell states were associated with specific genomic alterations including driver single nucleotide variants and high-level amplifications First, the ability to detect driver mutations with single-nuclei sequencing was assessed in 32 samples with available bulk whole exome sequencing, and a 74% detection rate was reached for somatic coding SNVs and a 83% detection rate was reached for oncogene amplifications (data not shown). It was found that oncogenic drivers were not restricted to specific tumor cell states (data not shown).

[0169] Tumour microenvironment composition was defined according to pseudo bulk tumour classification. Tumors classified as basal / squamous using the pseudo bulk classification had increased immune infiltration, but did not display a significantly different microenvironment composition regarding main immune cell populations. Despite evidence of association with heterogenous immune infiltration, the pseudo bulk classification did not predict outcomes on therapy; however, the proportion of basal tumor cells within a sample appeared a strong predictor of response to ICI (Figures 1A-1B). Indeed, as shown in Figure IB, subjects which respond to ICI exhibited a higher percentage of basal cells compared to non-responsive subjects.

[0170] To understand adaptive mechanisms of resistance to ICI related to tumor cells, gene expression changes between baseline and disease progression were assessed. Among -1800 up- and down-regulated genes at progression, genes suggested to be involved in immunotherapy response were investigated. Genes involved in antigen presentation, including HLA-A, HLA-B, HLA-C, and B2M were all significantly downregulated in tumor cells at progression as well as genes involved in interferon response including IFNGR1 and IFNGR2 (data not shown). Alterations in the JAK / STAT signaling pathway also appeared prominent with a significant repression of the activator STATL and an upregulation of the key regulator PTPN2. Significant alterations in either of these pathways at progression were observed in 10 / 16 patients with matched samples (63%). In patients who also had tissue sampling during ICI in the context of controlled disease, transient upregulation of HLA-A / B / C, B2M, IFNGR1, and STAT1 was observed(data not shown), suggestive of an immune-responsive contexture.T cells signature

[0171] T lymphocytes were the main component of the tumor microenvironment in this cohort. Seven main infiltrating T cell subpopulations were identified, including CD4 regulatory cells, CD4 follicular helpers (naive, effector, exhausted), CD8 cytotoxic (naive, effector, exhausted) (data not shown). We thus searched to identify T cells features associated with resistance or sensibility to ICI.

[0172] The abundance of overall CD8 infiltration was associated with high CD8 effector and CD8 exhausted cell infiltration; both correlated with poor outcomes on ICI (Figure 3). Accordingly, patients who did not respond to ICI had higher expression of multiple immune checkpoints (including PDCD1, TIGIT, LAG3, HAVCR2) as well as effector genes GZMA / B on T lymphocytes, suggestive of terminally differentiated infiltrating T cells (Figures 2-3). Conversely, overall CD4 lymphocyte infiltration was associated with improved outcomes.

[0173] Changes in the T cell transcriptome on therapy were heterogeneous across samples, though down-regulated genes identified two modules pertaining to T cell function (including ICOS, CD96, GZMA, GNLY) and type-I interferon response (data not shown). Five patients (31%) with sequential T cell single-nuclei data also displayed upregulation of at least one immune checkpoint at progression, including PDCD1, CTLA4. HAVCR2, LAGS and TIGIT (data not shown).

[0174] We also searched to identify macrophages features associated with resistance or sensibility to ICI.

[0175] Infiltrating macrophages were characterized based on single-cell datasets classifications that identified several pro or anti-tumoral macrophages subtypes (Mulder, K. et al. Immunity. 2021 Aug 10;54(8):1883-1900.e5. doi: 10.1016 / j.immuni.2021.07.007.) (data not shown). Primary resistance to ICI was associated with an increasein HES 1 and TREM2 macrophages that have been associated with M2-like transcriptional programs (Figures 4-5). In Figures 6A-6B, patients have been grouped into high and low abundance of HES1 macrophages and M2 macrophages, and each group is represented by a Kaplan-Meier survival curve. As shown, the curves for higher abundance dropped more rapidly, demonstrating that higher abundance of HES1 macrophages and M2 macrophages correlates with worse survival.

[0176] Immunosuppressive genes involved in M2 polarization CD] 63, SAMSN1, MS4A4A were also upregulated in macrophages in patients who did not respond to ICI (Figure 4). Most patients (81%) had evidence of M2-like macrophage polarization at progression, consisting in increased HES1 macrophage abundance, upregulation of M2-genes such as CD 163, VSIG4, and downregulation of Ml genes such as CLEC5A and HLA-DRA (data not shown).

[0177] We further compared the abundance of functional subsets of myeloid cells, assessed by single-nucleus RNA-seq, in metastatic bladder cancers responding or not to immune checkpoint blockade. Figure 7 shows that HES 1 macrophages is the population with the strongest association to response. M2 macrophages were also associated with response in this cohort, but not Ml macrophages.Single-nuclei sequencing pinpoints individual trajectories towards ICI resistance

[0178] To recapitulate the findings using more easily accessible assays, bulk RNA sequencing was used to specifically explore genes associated with outcomes through single-nuclei sequencing. The candidate genes expression using bulk RNA sequencing was highly driven by the abundance of the tumor microenvironment without discrimination of pro- or antitumor immune features (data not shown). Thus, single-nuclei sequencing uniquely allowed the identification of immune features associated with sensitivity to ICI thanks to the depiction of tumor and immune cell subpopulations transcriptomes. Individual patient assessments showed specific tumor trajectories involving one or multiple mechanisms of resistance related to tumor or immune cells (data not shown).

[0179] In conclusion, the herein data support the fact that the biomarkers identifiedherein, related to tumor features, immune features, and macrophage features, could serve as valuable predictors of ICI treatment success, helping to determine whether a patient is likely to respond to ICI.

Claims

CLAIMS1. A method for determining whether a subject suffering from a metastatic urothelial cancer (mUC) will achieve a response with an ICI therapy, said method comprising:(i) determining, in a biological sample previously obtained from the subject, at least one of the signatures below:(a) Signature relative to macrophages (Macrophage signature),(b) Signature relative to T cells (T cell signature), and / or(c) Signature relative to tumor cells (Tumor cell signature), wherein the determination of the Macrophage signature (a) comprises measuring at least one of the parameters below:al) the proportion of HES1 macrophages among total macrophages,a2) the proportion of TREM2 macrophages among total macrophages, a3) the expression levels of at least one gene selected from the group consisting of CD 163, SAMSN1, MS4A4A and VSIG4 in macrophages, and / ora4) the expression levels of at least one of the genes CLEC5A and HLA-DRA in macrophages,wherein the determination of the T cell signature (b) comprises measuring at least one of the parameters below:bl) the proportion of CD 8 T cells among total T cells,b2) the proportion of CD 8 effector T cells among total T cells,b3) the proportion of CD8 exhausted T cells among total T cells,b4) the expression levels of at least one gene selected from the group consisting otPDCDl, TIGIT, LAGS, and HAVCR2 in T cells, and / orb5) the expression levels of at least one of the genes GZMA / B, in T cells, wherein the determination of the Tumor cell signature (c) comprises measuring a parameter cl) being the proportion of basal tumor cells among total tumor cells, and(ii) comparing the at least one signature obtained at step (i) with a reference signature, and(iii) predicting whether the subject will achieve a response with ICI therapy based on the correlation of the at least one signature obtained at step (i) with the reference signature.

2. The method according to claim 1, wherein the determination of the Macrophage signature (a) comprises measuring two, three, or the four parameters al) to a4).

3. The method according to any one of claims 1 to 2, wherein measuring the parameter a3) comprises measuring the expression levels of the genes CD 163, SAMSN1, MS4A4A and VSIG4 in macrophages.

4. The method according to any one of claims 1 to 3, wherein measuring the parameter a4) comprises measuring the expression levels of the genes CLEC5A and HLA-DRA in macrophages.

5. The method according to any one of claims 1 to 4, wherein the determination of the T cell signature (b) comprises measuring two, three, four, or the five parameters bl) to b5).

6. The method according to any one of claims 1 to 5, wherein measuring the parameter b4) comprises measuring the expression levels of the genes PDCD1, T1G1T, LAG3, and HA VCR2 in T cells.

7. The method according to any one of claims 1 to 6, wherein the method comprises the determination of two or the three signatures Macrophage signature (a), T cell signature (b) and Tumor signature (c).

8. The method according to any one of claims 1 to 7, wherein the method comprises the determination of the three signatures Macrophage signature (a), T cell signature (b) and Tumor signature (c), wherein the determination of the Macrophage signature (a) comprises measuring the four parameters al) to a4), and wherein the determination of the T cell signature (b) comprises measuring the five parameters bl) to b5).

9. The method according to any one of claims 1 to 8, wherein the biological sample previously obtained from a subject is a tumour biopsy.

10. The method according to any one of claims 1 to 9, wherein it is predicted that the subject will achieve a response with ICI therapy if the at least one signature obtained at step (i) is different from a reference signature obtained from a reference population.

11. The method according to any one of claims 1 to 10, wherein the reference signature is obtained as follows:for the Macrophage signature (a), by measuring at least one of the parameters al) to a4) in a reference population,for the T cell signature (b), by measuring at least one of the parameters bl) to b5) in a reference population, and / orfor the Tumor signature (c), by measuring the parameter cl) in a reference population.

12. The method according to any one of claims 10 to 11, wherein the reference population is a population of subjects suffering from mUC, treated with an ICI therapy and for which the response to ICI is already known.

13. The method according to any one of claims 1 to 12, wherein the ICI therapy is an anti-PD-1 inhibitor or an anti-PD-Ll inhibitor, preferably an ICI therapy selected from the group consisting of pembrolizumab, nivolumab, atezolizumab, durvalumab, and avelumab.

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