WO2025242829A1

WO2025242829A1 - T cell biomarkers of response to cancer therapy

Info

Publication number: WO2025242829A1
Application number: PCT/EP2025/064196
Authority: WO
Inventors: Eliane Piaggio
Original assignee: Egle Therapeutics; Institut National de la Sante et de la Recherche Medicale INSERM; Institut Curie
Current assignee: Egle Therapeutics; Institut National de la Sante et de la Recherche Medicale INSERM; Institut Curie
Priority date: 2024-05-22
Filing date: 2025-05-22
Publication date: 2025-11-27
Anticipated expiration: 2026-11-22

Abstract

The present invention provides a method for selecting a cancer patient for a cancer treatment or for predicting or monitoring the response of a cancer patient to a cancer treatment, comprising: determining the level of effector regulatory T cells in a biological sample from the patient, wherein said level of effector regulatory T cells correlates with the responsiveness of said patient to the cancer treatment.

Description

T CELL BIOMARKERS OF RESPONSE TO CANCER THERAPY

FIELD OF THE INVENTION

The invention pertains to the field of oncology, in particular cancer therapy. The invention provides T cell biomarkers of response to cancer therapy such as immunotherapy in cancer patients.

BACKGROUND OF THE INVENTION

Immunotherapy targeting immune checkpoint molecules, in particular immune checkpoint inhibitors (ICI) such as with anti-PDl has been approved for the treatment of different types of cancers. Yet, a challenge persists to identify biomarkers of response and toxicity to cancer therapy, and to gain knowledge for the design of optimized treatments.

CD4+ T cells can be broadly classified in CD4+T conventional cells (Tconvs) that orchestrate and effect the anti-tumor immune response and in regulatory T cells (Treg), that blunt antitumor immune responses. In more details, Tregs play an important role in the homeostasis of the immune system, preventing the development of autoimmune diseases, but also, Tregs play a pivotal role in tumor immunology impacting on the response to treatment, the resistance and the clinical outcome of cancer patients.

Several studies have reported a prognostic value of Tregs in cancers with Treg levels being associated with a better or a worse outcome of cancer depending on the studies (Seminerio et al., Cancers (Basel), 2019, 11, 227; O’Callaghan et al., European Respiratory Journal, 2015, 46, 1762-1772; Kotsakis et al., Sci Rep, 2016, 6, 39247; Shang et al., Sci Rep, 2015, 5, 15179; Zhao, et al., Oncotarget. 2016, 7, 36065-36073; RA Soo et al., Oncotarget. 2018 May 15; 9(37): 24801-24820).

Multiple markers have been used to better characterize the Tregs, mainly based on their functional characteristics. The transcription factor forkhead box P3 (FoxP3), a crucial intracellular marker, induces peripheral naive T cells to become regulatory T cells with immune suppressive capacity. CD 127, the interleukin-7 (IL-7) receptor alpha, plays a vital role in T cell survival and the development of a memory phenotype and its low or no expression (CD1271ow/-) has been proposed as a marker of Tregs. With the advent of high throughput technologies, a deeper level of analyzing Tregs is possible today and has revealed a higher level of heterogeneity among Tregs.

SUMMARY OF THE INVENTION

The inventors have studied the dynamic evolution of the T cell response, and in particular the CD4+ T cell response in patients treated with anti-PDl and/or anti-CTLA-4 and they have identified novel biomarkers of the response to cancer therapy, in particular immunotherapy.

The inventors have shown that a low abundance of Effector Tregs at baseline is associated to response to anti-PD-1 treatment (Figure 2A, 2B and 2D; Figure 3A and 3B). Cancer patients who respond to anti-PDl show an increase in effector Treg among total Treg cells upon a-PDl treatment, while stable disease and non-responder patients do not (Figure 2B and 2C; Figure 3B and 3C) In contrast, total Treg percentages do not significantly change between responders and non-responders (Figure 2E). The study was performed on PBMC samples. Therefore, the level of circulating Effector Tregs is a novel biomarker of the response to cancer therapy, in particular immunotherapy in cancer patients. The biomarker can be easily determined by flow cytometry which can be used in routine clinical test. Therefore, the invention provides a simple method to predict the response to cancer therapy, in particular immunotherapy in cancer patients which can be performed on a blood sample.

The biomarker quality of effector Treg frequency was further confirmed by single-cell transcriptomic analysis performed on Treg-enriched PBMCs from the same patient cohort. Using scRNA-seq, effector Tregs were identified based on gene expression of activation and suppressive markers (e.g., TNFRSF9, CTLA4, BATF, TNFRSF18) (Figure 4). As with the flow cytometry data, responders showed significantly lower baseline frequencies of effector Tregs compared to stable or progressive disease patients (Figure 5), and a significant increase in effector Treg frequency post-treatment was observed only in responders (Figure 6). These transcriptomic results confirm and reinforce the use of circulating effector Tregs as a robust and clinically relevant biomarker of response to cancer therapy, in particular immunotherapy in cancer patients.

One aspect of the invention relates to a method for selecting a cancer patient for a cancer treatment or for predicting or monitoring the response of a cancer patient to a cancer treatment, comprising: determining the level of effector regulatory T cells in a biological sample from the patient, wherein said level of effector regulatory T cells correlates with the responsiveness of said patient to the cancer treatment.

In some embodiments of the method according to the invention, the biological sample is a blood sample, preferably whole-blood or PBMC fraction thereof.

In some embodiments of the method according to the invention, the biological sample is obtained at baseline, before initiation of the cancer treatment.

In some embodiments of the method according to the invention, the biological sample is obtained during the cancer treatment.

In some embodiments of the method according to the invention, said effector regulatory T cells are CD3+CD4+CD127-CD45RA-CD25hi cells or CD3+CD4+CD127- CD45RO+CD25hi cells.

In some embodiments of the method according to the invention, said level is the percentage of effector regulatory T cells among the population of regulatory T cells in said biological sample. In some particular embodiments, the method according to the invention comprises determining the percentage of CD3+CD4+CD127-CD45RA-CD25hi cells or CD3+CD4+CD127-CD45RO+CD25hi cells within the population of CD3+CD4+CD127- CD25+ cells in said biological sample.

In some alternative or additional embodiments, the level of effector regulatory T cells in the biological sample is determined based on the expression of one or more transcriptomic markers characteristic of effector regulatory T cells. In particular embodiments, the effector regulatory T cells are identified as regulatory T cells expressing high levels of TNFRSF9, CTLA4, BATF, and/or, in particular and, TNFRSF18. The level of effector regulatory T cells may thus be determined by measuring the expression level of at least one, or a combination, and in particular of all, of these genes in regulatory T cells, such as CD3 CD4 CD I 27 CD25 cells, obtained from the biological sample.

Accordingly, in some embodiments of the method according to the invention, said effector regulatory T cells are: - CD3+CD4+CD127-CD45RA-CD25hi cells or CD3+CD4+CD127-CD45RO+CD25hi cells; and/or

- regulatory T cells expressing a gene signature comprising at least one gene selected from TNFRSF9, CTLA4, BATF, and TNFRSF18, and in particular all the genes TNFRSF9, CTLA4, BATF, and TNFRSF18.

In some particular embodiments, the method according to the invention comprises determining:

- the percentage of CD3+CD4+CD127-CD45RA-CD25hi cells or CD3+CD4+CD127- CD45RO+CD25hi cells within the population of CD3+CD4+CD127-CD25+ cells in said biological sample and/or

- the expression level of at least one gene, and in particular of all genes, selected from TNFRSF9, CTLA4, BATF, and TNFRSF18 in said biological sample.

In some embodiments, the method according to the invention, further comprises comparing the determined level of effector regulatory T cells with a reference level. In some particular embodiments of the method according to the invention, said reference level is a percentage of 13.5% of effector regulatory T cells among the population of total regulatory T cells.

In some embodiments, the method according to the invention is for predicting the response of a cancer patient to a cancer treatment, wherein the responsiveness of said patient to the cancer treatment is inversely proportional to the level of effector regulatory T cells in a biological sample obtained at baseline. In some particular embodiments of the method according to the invention, a level of effector regulatory T cells at baseline lower than the reference is predictive of the responsiveness of said patient to a cancer treatment and/or a level of effector regulatory T cells at baseline higher than the reference level is predictive of the inefficacy or lower efficacy of a cancer treatment in the cancer patient.

In some embodiments, the method according to the invention is for predicting or monitoring the response of a cancer patient to a cancer treatment, wherein the responsiveness of said patient to the cancer treatment is proportional to the level of effector regulatory T cells in a biological sample obtained during cancer. In some particular embodiments of the method according to the invention, a level of effector regulatory T cells during cancer treatment higher than the reference is predictive of the responsiveness of said patient to a cancer treatment and/or a level of effector regulatory T cells lower than the reference level is predictive of the inefficacy or lower efficacy of a cancer treatment in the cancer patient.

In some embodiments, the method according to the invention is for monitoring the response of a cancer patient to a cancer treatment, wherein an increase of the level of effector regulatory T cells between the level of effector regulatory T cells at baseline and after cancer treatment in the cancer patient is predictive of the responsiveness of said patient to a cancer treatment and/or a decrease or unchanged level of effector regulatory T cells between the level of effector regulatory T cells at baseline and after cancer treatment is predictive of the inefficacy or lower efficacy of a cancer treatment in the cancer patient.

In some embodiments of the method according to the invention, the cancer is selected from the group consisting of: lung cancer; neuroendocrine cancer; breast cancer; kidney cancer; prostate cancer; colon or colorectal cancer; ovarian cancer; uterus cancer, such as endometrial or cervical cancer; liver cancer such as hepatocellular carcinoma; skin cancer such as melanoma; brain cancer; thyroid cancer; adrenal gland cancer; gastric cancer; esophageal cancer; pancreatic cancer; muscle cancer; head and neck cancer; hypopharynx cancer; bladder cancer.

In some embodiments of the method according to the invention, the cancer treatment is radiotherapy, chemotherapy, immunotherapy or a combination thereof. In some particular embodiments, the cancer treatment is immunotherapy with an immune checkpoint inhibitor alone or in combination therapies with radiotherapy, chemotherapy, cancer vaccine, and/or other checkpoint inhibitors. In some particular embodiments, the immune checkpoint inhibitor(s) are selected from the group consisting of PD-1 inhibitors, PD-L1 inhibitors, Lag- 3 inhibitors, Tim-3 inhibitors, TIGIT inhibitors, B TLA inhibitors, V-domain Ig suppressor of T-cell activation (VISTA) inhibitors, CTLA-4 inhibitors and IDO inhibitors.

In some embodiments, the method according to the invention further comprises classifying the cancer patient into responder and non-responder to cancer treatment based on the level of effector regulatory T cells determined in the biological sample from the patient. DETAILED DESCRIPTION OF THE INVENTION

The invention provides T cell biomarkers of response to cancer treatment such as immunotherapy in cancer patients, in particular circulatory T cell biomarkers of response to cancer treatment such as immunotherapy. The invention also provides a method for selecting a cancer patient for a cancer treatment or for predicting or monitoring the response of a cancer patient to a cancer treatment, which comprises detecting at least one T cell biomarker according to the present disclosure in a biological sample from the cancer patient. A reliable prediction of the response to a cancer treatment such as immunotherapy is extremely valuable for optimizing the therapeutic strategy for each cancer patient.

Definitions

As used herein, T cells refer to CD3+ cells.

As used herein, “regulatory T cells”, “T regulatory cells”, “Tregs”, “Treg” or “Treg cells” refer to CD4+ Foxp3+ T cells; CD3+CD4+Foxp3+ CD25high cells; CD4+ CD25high T cells; CD4+, CD25+ and CD127- T cells or CD4+, CD25high and CD127- T cells.

As used herein, “effector regulatory T cells”, “effector” or “eff ’ Treg(s) or Treg cells (EffTreg) refer to a subpopulation of regulatory T cells characterized by a distinct gene expression signature as disclosed for example in WO 2024/052433. Effector Tregs refer in particular to CD3+CD4+CD127-CD45RA-CD25hi T cells; CD3+CD4+CD127-CD45RO+CD25hi T cells.

The term “marker” as used herein means “molecular marker” or “molecular signature” and refers to a specific gene or gene product (RNA or protein). A marker is in particular a cell marker that may be a cell-surface or intracellular marker. A marker includes any one of the markers disclosed herein such as any marker disclosed in the examples or figures of the present disclosure. In some embodiments, effector regulatory T cells are further defined by the expression of transcriptomic markers such as TNFRSF9 (CD137), CTLA4, BATF, and/or, in particular and, TNFRSF18 (GITR). These markers may be used alone or in combination, and are in particular all used, to identify or quantify effector Tregs, particularly in transcriptomic analyses such as bulk or single-cell RNA sequencing, RT-qPCR, or other gene expression profiling techniques. As used herein, « gene signature » or « gene expression signature » refers to a single or combined group of genes in a cell with a uniquely characteristic pattern of gene expression or peak accessibility specific for a unique characteristic feature of the cell such as for example cell differentiation, tissue-imprinting, migration, tissue-residency, and others.

As used herein, “biomarker” refers to a distinctive biological or biologically derived indicator of a process, event or condition. Biomarker includes “molecular marker”, “gene signature” and “cell marker”. “Cell marker” refers to a distinct cell type (or cell population), in particular a subtype (or subpopulation) of T cells, in particular Effector Tregs as disclosed herein.

As used herein, “expression” of a marker in a cell refers to a detectable level of the marker in the cell irrespective of its expression level, “high expression”, “high expression level” “overexpression” of a marker in a cell refers to a high level of expression of the marker in the cell. The level of expression of markers in cells can be measured by standard quantitative or semi-quantitative techniques that are well-known in the art, and for example disclosed herein. Expression of a marker refers to gene and/or protein expression of the marker, in particular gene and protein expression of the marker.

The different types and subtypes of T cells, in particular CD4+ T cells, as disclosed herein can be analyzed and sorted by routine techniques known in the art such as flow cytometry assisted cell sorting or magnetic cell separation, using appropriate antibodies as disclosed in the examples. Flow cytometry such as flow cytometry assisted cell sorting can be used to determine the level of the different types and subtypes of T cells (immunophenotyping), in particular Effector Treg cells, as disclosed herein. Flow cytometry can also use to determine the expression levels of cell-surface and intracellular markers in the T cells according to the present disclosure.

Biological samples include direct samples and processed samples. Processed samples have been treated by standard methods, used to prepare a biological sample for analysis. In particular, processed samples include samples that have been treated by standard methods used for the preparation of cells or tissue for immunological or immune-histological analysis, or the isolation and purification of nucleic acids or proteins for analysis, such as those described in the Examples. As used herein, the term “cancer” refers to any member of a class of diseases or disorders characterized by uncontrolled division of cells and the ability of these cells to invade other tissues, either by direct growth into adjacent tissue through invasion or by implantation into distant sites by metastasis. Metastasis is defined as the stage in which cancer cells are transported through the bloodstream or lymphatic system. The term cancer according to the present invention also comprises cancer metastases and relapse of cancer. Cancers are classified by the type of cell that the tumor resembles and, therefore, the tissue presumed to be the origin of the tumor. For example, carcinomas are malignant tumors derived from epithelial cells. This group represents the most common cancers, including the common forms of breast, prostate, lung, and colon cancer. Lymphomas and leukemias include malignant tumors derived from blood and bone marrow cells. Sarcomas are malignant tumors derived from connective tissue or mesenchymal cells. Mesotheliomas are tumors derived from the mesothelial cells lining the peritoneum and the pleura. Gliomas are tumors derived from glia, the most common type of brain cell. Germinomas are tumors derived from germ cells, normally found in the testicle and ovary. Choriocarcinomas are malignant tumors derived from the placenta. As used herein, “cancer” refers to any cancer type including solid and liquid tumors.

A "patient" refers to a subject affected by cancer, i.e., a cancer patient. As used herein, the term "patient" refers to human subjects or non-human subjects such as non-human mammals. Preferably, a patient according to the invention is a human.

“Treating cancer" (i.e. cancer treatment or cancer therapy) includes, without limitation, reducing the number of cancer cells or the size of a tumor in the patient, reducing progression of a cancer to a more aggressive form (i.e. maintaining the cancer in a form that is susceptible to a therapeutic agent), reducing proliferation of cancer cells or reducing the speed of tumor growth, killing of cancer cells, reducing metastasis of cancer cells or reducing the likelihood of recurrence of a cancer in a subject. Treating a subject as used herein refers to any type of treatment that imparts a benefit to a subject afflicted with cancer or at risk of developing cancer or facing a cancer recurrence. Treatment includes improvement in the condition of the subject (e.g., in one or more symptoms), delay in the progression of the disease, delay in the onset of symptoms, slowing the progression of symptoms and others. “a”, “an”, and “the” include plural referents, unless the context clearly indicates otherwise. As such, the term “a” (or “an”), “one or more” or “at least one” can be used interchangeably herein; unless specified otherwise, “or” means “and/or”.

Biomarkers

The present invention provides T cell biomarkers of response to cancer treatment such as immunotherapy in cancer patients. One aspect of the invention relates to the amount (or level or abundance) of effector Tregs, in particular the amount of circulatory Tregs as a biomarker of the response to cancer treatment such as immunotherapy. The present invention encompasses the in vitro use of the biomarker for selecting a cancer patient for a cancer treatment such as immunotherapy or for predicting or monitoring the response of a cancer patient to a cancer treatment such as immunotherapy.

Prediction and monitoring of response to cancer therapy

One aspect of the invention relates to a method for selecting a cancer patient for a cancer treatment such as immunotherapy or for predicting or monitoring the response of a cancer patient to a cancer treatment such as immunotherapy, wherein the method comprises: determining the level of effector regulatory T cells (effector Tregs) in a biological sample from the patient, wherein said level of effector regulatory T cells correlates with the responsiveness of said patient to the cancer treatment such as immunotherapy.

The method according to the invention allows to predict at baseline and during the course of treatment if a cancer patient is likely or not to respond to a cancer treatment. This method thus allows to stratify and monitor the cancer patients to decide an optimized therapeutic strategy for each patient.

The level of effector Tregs is measured in a biological sample obtained from the cancer patient, preferably a blood sample such as whole-blood or a PBMC fraction thereof. The biological sample may be obtained at baseline, before initiation of the cancer treatment, or may be obtained during the cancer treatment.

In some embodiments, said effector regulatory T cells are CD3+CD4+CD127-CD45RA- CD25hi T cells; CD3+CD4+CD127-CD45RO+CD25hi T cells. The level of effector Tregs in the patient’s sample may be determined directly, by measuring the level of effector Treg cells in the patient’s sample, or indirectly, by measuring the level of expression of a gene signature of effector Treg cells. In particular embodiments, said gene signature comprises the expression of one or more, and in particular of all, of the following genes: TNFRSF9, CTLA4, BATF, and TNFRSF18.

In some embodiments, the level of effector Tregs is determined directly, by measuring the level of effector Treg cells in the patient’s sample. The level of effector Treg cells in the patient’s sample may be measured by routine techniques known in the art such as Flow cytometry, in particular flow cytometry assisted cell sorting using appropriate antibodies as disclosed in the examples. Alternatively, or additionally, the level of effector Tregs may be determined indirectly, by measuring the expression of a gene signature associated with effector Treg cells, such as the expression of TNFRSF9, CTLA4, BATF, and/or, and in particular and, TNFRSF18, for example by RT-qPCR, bulk or single-cell RNA sequencing. Suitable gene signatures are described in the examples.

In some embodiments, the level is a relative level or proportion, or percentage, or frequency. In particular, the level of effector Tegs is determined relatively to the total regulatory T cells present in the biological sample. The relative level effector Tegs may be expressed as a percent of effector Tregs among all Tregs present in the biological sample. Accordingly, in some particular embodiments of the method according to the invention, said level is the percentage of effector regulatory T cells among the population of total regulatory T cells in said biological sample. In some more particular embodiments, the method according to the invention comprises determining the percentage of CD3+CD4+CD127-CD45RA-CD25hi cells or CD3+CD4+CD127-CD45RO+CD25hi cells within the population of CD3+CD4+CD127- CD25+ cells in said biological sample.

According to the method of the invention, the level of effector Tregs in a patient’s sample may be determined by comparison with a reference. The reference may be a reference sample comprising known levels of effector Tregs; mRNA or protein expressed by signature genes. In particular embodiments, such signature genes include TNFRSF9, CTLA4, BATF, and TNFRSF18. The reference may consist of threshold values or expression ranges established from control or baseline samples, and may be used for relative quantification of effector Treg- associated gene expression in the patient’s sample. Alternatively, the reference is a predetermined value. The predetermined value may be a threshold value or a range. A reference value refers to a value established by statistical analysis of values obtained from representative panels of individuals. The reference value may for example be obtained by measuring effector Treg levels, in samples from a panel of cancer patients responsive to cancer treatment(responders) and a panel of cancer patients non- responsive or poorly responsive to cancer treatment (non-responders or poor responders) as disclosed in the present examples. A cut-off value that can discriminate favorable and unfavorable prognosis of cancer is then determined. The panel of cancer patients may be of the same type of cancer as the tested patient or not.

In some embodiments, the method according to the invention, further comprises: comparing the determined level of effector regulatory T cells with a reference level. In some particular embodiments of the method according to the invention, said reference level is a percentage of 13.5% of effector regulatory T cells among the population of total regulatory T cells.

In some embodiments, the method according to the invention is for predicting or monitoring the response of a cancer patient to a cancer treatment, wherein the responsiveness of said patient to the cancer treatment is proportional to the level of effector regulatory T cells in a biological sample obtained during cancer treatment. In some particular embodiments of the method according to the invention, a level of effector regulatory T cells during cancer treatment higher than the reference is predictive of the responsiveness of said patient to a cancer treatment and/or a level of effector regulatory T cells lower than the reference level is predictive of the inefficacy or lower efficacy of a cancer treatment in the cancer patient.

The method of the invention is useful to predict the response to a cancer treatment at baseline and during treatment and thereby adapt cancer treatment at initial staging and during the course of a cancer treatment. The method of the invention is also useful to monitor the response of a cancer patient to a cancer treatment and adjust cancer treatment during the course of cancer treatment.

As used herein, the term “cancer” refers to any cancer that may affect any one of the following tissues or organs: breast; liver; kidney; heart, mediastinum, pleura; floor of mouth; lip; salivary glands; tongue; gums; oral cavity; palate; tonsil; larynx; trachea; bronchus, lung; pharynx, hypopharynx, oropharynx, nasopharynx; esophagus; digestive organs such as stomach, intrahepatic bile ducts, biliary tract, pancreas, small intestine, colon; rectum; urinary organs such as bladder, gallbladder, ureter; rectosigmoid junction; anus, anal canal; skin; bone; joints, articular cartilage of limbs; eye and adnexa; brain; peripheral nerves, autonomic nervous system; spinal cord, cranial nerves, meninges; and various parts of the central nervous system; connective, subcutaneous and other soft tissues; retroperitoneum, peritoneum; adrenal gland; thyroid gland; endocrine glands and related structures; female genital organs such as ovary, uterus, cervix uteri; corpus uteri, vagina, vulva; male genital organs such as penis, testis and prostate gland; hematopoietic and reticuloendothelial systems; blood; lymph nodes; thymus.

The term “cancer” according to the invention comprises leukemias, seminomas, melanomas, teratomas, lymphomas, non-Hodgkin lymphoma, neuroblastomas, gliomas, adenocarcinoma, mesothelioma (including pleural mesothelioma, peritoneal mesothelioma, pericardial mesothelioma and end stage mesothelioma), rectal cancer, endometrial cancer, thyroid cancer (including papillary thyroid carcinoma (THCA), follicular thyroid carcinoma, medullary thyroid carcinoma, undifferentiated thyroid cancer, multiple endocrine neoplasia type 2A, multiple endocrine neoplasia type 2B, familial medullary thyroid cancer, pheochromocytoma and paraganglioma), skin cancer (including malignant melanoma (SKCM), basal cell carcinoma, squamous cell carcinoma, Kaposi’s sarcoma, keratoacanthoma, moles, dysplastic nevi, lipoma, angioma and dermatofibroma), nervous system cancer, brain cancer (including astrocytoma, medulloblastoma, glioma, lower grade glioma (LGG), ependymoma, germinoma (pinealoma), glioblastoma multiform, oligodendroglioma, schwannoma, retinoblastoma, congenital tumors, spinal cord neurofibroma, glioma or sarcoma), skull cancer (including osteoma, hemangioma, granuloma, xanthoma or osteitis deformans), meninges cancer (including meningioma, meningiosarcoma or gliomatosis), head and neck cancer (including head and neck squamous cell carcinoma and oral cancer (such as, e.g., buccal cavity cancer, lip cancer, tongue cancer, mouth cancer or pharynx cancer)), lymph node cancer, gastrointestinal cancer, liver cancer (including hepatoma, hepatocellular carcinoma (LIHC), cholangiocarcinoma, hepatoblastoma, angiosarcoma, hepatocellular adenoma and hemangioma), colon cancer, stomach or gastric cancer, esophageal cancer (including squamous cell carcinoma, larynx, adenocarcinoma, leiomyosarcoma or lymphoma), colorectal cancer, intestinal cancer, small bowel or small intestines cancer (such as, e.g., adenocarcinoma lymphoma, carcinoid tumors, Kaposi’s sarcoma, leiomyoma, hemangioma, lipoma, neurofibroma or fibroma), large bowel or large intestines cancer (such as, e.g., adenocarcinoma, tubular adenoma, villous adenoma, hamartoma or leiomyoma), pancreatic cancer (including ductal adenocarcinoma, insulinoma, glucagonoma, gastrinoma, carcinoid tumors or vipoma), ear, nose and throat (ENT) cancer, breast cancer (including estrogen receptor positive (ER+), HER2-enriched breast cancer, luminal cancer (ER/PR+; HER2-), luminal A breast cancer, luminal B breast cancer and triple negative (ER-, PR-, HER2-) breast cancer), cancer of the uterus (including endometrial cancer such as endometrial carcinomas, endometrial stromal sarcomas and malignant mixed Mullerian tumors, uterine sarcomas, leiomyosarcomas and gestational trophoblastic disease), ovarian cancer (including dysgerminoma, granulosa-theca cell tumors and Sertoli-Leydig cell tumors), cervical cancer, in particular cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), vaginal cancer (including squamous-cell vaginal carcinoma, vaginal adenocarcinoma, clear cell vaginal adenocarcinoma, vaginal germ cell tumors, vaginal sarcoma botryoides and vaginal melanoma), vulvar cancer (including squamous cell vulvar carcinoma, verrucous vulvar carcinoma, vulvar melanoma, basal cell vulvar carcinoma, Bartholin gland carcinoma, vulvar adenocarcinoma and erythroplasia of Queyrat), genitourinary tract cancer, kidney cancer (including Renal Cell Carcinoma (KIRC), clear renal cell carcinoma, chromophobe renal cell carcinoma, papillary renal cell carcinoma, adenocarcinoma, Wilms tumor, nephroblastoma, lymphoma or leukemia), adrenal cancer, in particular adrenocortical carcinoma (ACC), bladder cancer, urethra cancer (such as, e.g., squamous cell carcinoma, transitional cell carcinoma or adenocarcinoma), prostate cancer (such as, e.g., adenocarcinoma or sarcoma) and testis cancer (such as, e.g., seminoma, teratoma, embryonal carcinoma, teratocarcinoma, choriocarcinoma, sarcoma, interstitial cell carcinoma, fibroma, fibroadenoma, adenomatoid tumors or lipoma), lung cancer (including small cell lung carcinoma (SCLC), non-small cell lung carcinoma (NSCLC) including squamous cell lung carcinoma, lung adenocarcinoma (LUAD), and large cell lung carcinoma, bronchogenic carcinoma, alveolar carcinoma, bronchiolar carcinoma, bronchial adenoma, lung sarcoma, chondromatous hamartoma and pleural mesothelioma), sarcomas (including Askin's tumor, sarcoma botryoides, chondrosarcoma, Ewing's sarcoma, malignant hemangioendothelioma, malignant schwannoma, osteosarcoma and soft tissue sarcomas), soft tissue sarcomas (including alveolar soft part sarcoma, angiosarcoma, cystosarcoma phyllodes, dermatofibrosarcoma protuberans, desmoid tumor, desmoplastic small round cell tumor, epithelioid sarcoma, extraskeletal chondrosarcoma, extraskeletal osteosarcoma, fibrosarcoma, gastrointestinal stromal tumor (GIST), hemangiopericytoma, hemangiosarcoma, Kaposi's sarcoma, leiomyosarcoma, liposarcoma, lymphangiosarcoma, lymphosarcoma, malignant peripheral nerve sheath tumor (MPNST), neurofibrosarcoma, plexiform fibrohistiocytic tumor, rhabdomyosarcoma, synovial sarcoma and undifferentiated pleomorphic sarcoma, cardiac cancer (including sarcoma such as, e.g., angiosarcoma, fibrosarcoma, rhabdomyosarcoma or liposarcoma, myxoma, rhabdomyoma, fibroma, lipoma and teratoma), bone cancer (including osteogenic sarcoma, osteosarcoma, fibrosarcoma, malignant fibrous histiocytoma, chondrosarcoma, Ewing’s sarcoma, malignant lymphoma and reticulum cell sarcoma, multiple myeloma, malignant giant cell tumor chordoma, osteochronfroma, osteocartilaginous exostoses, benign chondroma, chondroblastoma, chondromyxoid fibroma, osteoid osteoma and giant cell tumors), hematologic and lymphoid cancer, blood cancer (including acute myeloid leukemia, chronic myeloid leukemia, acute lymphoblastic leukemia, chronic lymphocytic leukemia, myeloproliferative diseases, multiple myeloma and myelodysplasia syndrome), Hodgkin’s disease, non-Hodgkin’s lymphoma and hairy cell and lymphoid disorders, and the metastases thereof.

In some embodiments of the method according to the invention, the cancer is selected from the group consisting of lung cancer; neuroendocrine cancer; breast cancer; kidney cancer; prostate cancer; colon or colorectal cancer; ovarian cancer; uterus cancer, such as endometrial or cervical cancer; liver cancer such as hepatocellular carcinoma; skin cancer such as melanoma; brain cancer; thyroid cancer; adrenal gland cancer; gastric cancer; esophageal cancer; pancreatic cancer; muscle cancer; head and neck cancer; hypopharynx cancer; and bladder cancer.

The treatment is any type of cancer therapy, including surgery, radiotherapy, administration of anti-cancer drug(s) and combinations thereof. The anti-cancer drug may be select from the group comprising: chemotherapy agents, immunotherapy agents, hormone therapy agents, targeted therapy agents, other anti-cancer agents and combination thereof. Chemotherapy agents include alkylating agents such as hydrazine, oxazaphosphorines, nitrogen mustards, platinum-based agents and others; antimetabolites such as purine analogs, purine antagonists, pyrimidine antagonists, antifolates, ribonucleotide reductase inhibitors and others; topoisomerase inhibitors (I and II); mitotic inhibitors such as taxanes, vinca alkaloids (vinblastine, vincristine) and others; antitumor antibiotics; radiolabeled agents such as radiolabeled antibodies; and other anti-neoplastic drugs. Immunotherapy agents include in particular immune checkpoint modulators (i.e., inhibitors and/or agonists) such as immune checkpoint inhibitors and co-stimulatory antibodies; adoptive cell therapies; monoclonal antibodies; oncolytic virus therapy; cancer vaccines; and immune system modulators. Checkpoint inhibitors include, but are not limited to, PD-1 inhibitors, PD-L1 inhibitors, Lag- 3 inhibitors, Tim-3 inhibitors, TIGIT inhibitors, B TLA inhibitors, V-domain Ig suppressor of T-cell activation (VISTA) inhibitors and CTLA-4 inhibitors, IDO inhibitors for example. Costimulatory antibodies deliver positive signals through immune-regulatory receptors including but not limited to ICOS, CD 137, CD27 OX-40 and GITR. In some embodiments, the cancer treatment is radiotherapy, chemotherapy, immunotherapy or a combination thereof wherein each type of therapy may include the combination of different agents (of the same type, i.e., radiotherapy agents, chemotherapy agents, or immunotherapy agents). In some particular embodiments, the cancer treatment is immunotherapy alone or in combination therapies with radiotherapy and/or chemotherapy. In some particular embodiments, immunotherapy is cancer vaccine and/or immune checkpoint inhibitors. In some particular embodiments, the cancer treatment is immunotherapy with an immune checkpoint inhibitor (ICI) alone or in combination therapies with radiotherapy, chemotherapy, cancer vaccine, and/or other checkpoint inhibitors. In some particular embodiments, the immune checkpoint inhibitor is selected from the group consisting of PD-1 inhibitors, PD-L1 inhibitors, Lag-3 inhibitors, Tim-3 inhibitors, TIGIT inhibitors, BTLA inhibitors, V-domain Ig suppressor of T-cell activation (VISTA) inhibitors, CTLA-4 inhibitors and IDO inhibitors. In some embodiments, the method further comprises the stratification (or classification) of the cancer patient(s) into responder(s) and non-responder(s) (including low or poor responder(s)) to a cancer treatment based on the level of effector regulatory T cells determined in the biological sample from the patient(s). Patients stratified as responders using the method of prediction according to the invention will benefit from the cancer treatment, whereas patients stratified as non-responders using the method of prediction according to the invention, will benefit from a more aggressive cancer treatment, in terms of both treatment type and treatment regimen; thereby increasing the efficacy of a cancer treatment in cancer patients. In some particular embodiments, the method according to the present invention further comprises a step consisting in providing help with the decisions between several medical treatments based on the stratification. Patients diagnosed as responders using the method of prediction according to the invention may benefit from a less aggressive cancer treatment, in terms of both treatment type and treatment regimen; thereby reducing side-effects and improving patient’s comfort and well-being. On the opposite, patient diagnosed as non-responders or poor responders using the method of prediction according to the invention, may benefit from a more aggressive cancer treatment, in terms of both treatment type and treatment regimen; thereby increasing the efficacy of treatment in cancer patients.

The invention also relates to a method of treating cancer, comprising: predicting or monitoring the response to a cancer treatment in a patient before initiation or during the course of a cancer treatment according to the method of prediction according to the present disclosure; and administering the cancer treatment if the patient is diagnosed as responder or administering another cancer therapy if the patient is diagnosed as non-responder or poor responder.

The practice of the present invention will employ, unless otherwise indicated, conventional techniques which are within the skill of the art. Such techniques are explained fully in the literature.

The invention will now be exemplified with the following examples, which are not limitative, with reference to the attached drawings in which: BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1. FACS analysis of circulating PBMCs

Gating strategies for the quantification of effector Tregs among Total Tregs.

Figure 2. The abundance of Effector Tregs is associated to response to anti-PD-1 treatment at base line and during treatment in NSCLC patients

A to D. Quantification by FACS of the percentages s of the Effector Treg population among total Tregs at baseline (Tl), on treatment (T2), and the dynamic changes (T1 vsT2) in responders and non-responders. A. Graph shows results of percentage of Eff Tregs among total Tregs only for paired samples at timepoint 1 (Tl, baseline) (Responders, n=6; Stable disease, n=7; Progressive disease, n=9). B and C: Graphs show results only for paired samples at timepoint 1 and 2 (Tl -T2), individual patients (B) and collated data (C). D: graph shows results for the whole NSCLC cohort at baseline (Tl) (Responders, n=9; Stable disease, n=17; Progressive disease, n=18). The dotted line at 13.5 indicates the cut-off of response.

E. The percentage total Tregs (among all CD4+T cells) between responders and non- responders at baseline is shown as control. Effector Tregs cells were defined as CD4+CD127- CD45RA-CD25hi cells and Total Tregs as CD4+CD127-CD25hi cells. Results are expressed as % among total Tregs (A to D) or total CD4+ T cells (E). Statistical tests: Wilcoxon test (paired non parametric) (C)- Mann-Whitney test (unpaired non parametric) (A, B, D, E). *p< 0.05, ** p<0.01, ***p<0.001.

Figure 3. The abundance of Effector Tregs is associated to response to anti-PD-1 treatment at base line and during treatment in Neuroendocrine digestive and lung cancer patients

A to C. Validation by FACS of the percentages of the Effector Treg population among total Tregs at baseline (Tl), on treatment (T2), and the dynamic changes (Tl vsT2) in responders and non-responders. A: Graph shows results of percentage of Eff Tregs among total Tregs at timepoint 1 (Tl, baseline) (Responders, n=14; Stable disease, n=20; Progressive disease, n=13). B and C: Graph show results for samples at timepoint 1 and 2 (Tl -T2), individual patients (B) and collated data (C). The dotted line at 13.5 indicates the cut-off of response. Statistical tests: Wilcoxon test (paired non parametric) (C)- Mann-Whitney test (unpaired non parametric) (A and B). *p< 0.05, ** p<0.01, ***p<0.001. Figure 4. Density-based visualization of Treg subsets in a naive vs effector dimensional space

Two-dimensional scatterplot of single cells based on naive (abscissa) and effector (ordinate) module scores, computed from predefined gene expression signatures. Contour based gating was applied to define naive, effector, and intermediate regulatory T cell populations.

Figure 5. Baseline effector Treg frequency across clinical response groups

Boxplot showing the proportion of effector Tregs at baseline (Tl) in responders (R), stable disease (SD), and progressive disease (PD) patients. Responders exhibit significantly lower effector Treg frequency at baseline, p-values from Wilcoxon rank sum test. The Figures shows the response group on the abscissa (from left to right: R, SD and PD) and on the ordinate the effector Treg proportion (eff Treg proportion).

Figure 6. Evolution of effector Treg frequency before and after treatment

Paired comparison of effector Treg proportions at tl (pre-treatment) and t2 (post-treatment) within each clinical response group R, SD and PD. A significant increase in effector Tregs post-treatment is observed in responders only, p-values from Wilcoxon signed rank test are indicated. Abscissa: timepoint tl and t2 for each group, for left to right R, SD and PD. Ordinate: the effector Treg proportion (eff Treg proportion).

EXAMPLES

Example 1

Material and methods

Cohorts

Extended NSCLC cohort (60 patients) and NSCLC cohort (23 selected patients):

Advanced or metastatic Non-small Cell Lung Cancer (NSCLC) tumors, treated with a-PD-1

- Blood samples at different timepoints :T1 baseline (pre-treatment) ; T2 on-treatment (Week 4 to 6 post-treatment). The Extended NSCLC is composed of 60 patients including 23 patients with paired samples (NLSC cohort) and patients with unpaired samples. Analyzable patients: 9 Responders (R), 17 stable disease (SD), 18 progressive disease (PD).

The NLSC cohort is composed of 23 patients with paired samples selected from the extended NSCLC: 6 Responders, 9 stable disease and 8 Progressive disease.

Additional cohort :

- Neuroendocrine digestive and lung tumors

154 patients, treated with a-PD-1 and/or a-CTLA-4.

Analyzable patients: n=14; Stable disease, n=20; Progressive disease, n=13

- Blood samples at different timepoints : T1 baseline (pre-treatment) ; T2 on-treatment (Week 6 post-treatment).

FACS analysis of circulating P BMC s

Total PBMCs from patients collected before (Tl) or after (T2) treatment with anti-PD-1, were stained with a panel of antibodies and analyzed by flow cytometry using the gating strategies shown in Figure 1. The panel of antibodies for the NSCL cohort 23 patients included the following antibodies: aqua dead (aqua florescent reactive dye, Invitrogen), CD127 (FITC, Blue A, surface); CD45RA (PE-C5; Green C; surface); CD4 (BV785; Violet A; surface); CD3 (BV650; Violet C; surface) ; CD25 (BV737; UVA; surface). For the 23 patients of the NSCLC cohort for which paired samples (Tl and T2) were available, paired non parametric Wilcoxon test was performed. The panel of antibodies for the other cohorts included the following antibodies: CD127 (BV650, Violet C, surface); CD45RA (PC7; Green A; surface); CD4 (PE TX; Green D; surface); CD3 (Alexa700; Red B; surface); CD25 (PE; Green E; surface). Additionally, optional antibodies may be added to exclude non relevant populations for this study: TCRgd (FITC, Blue B); TCRVa7.2 (PerCP5.5, Blue A); TCRVa24 (BV510, Violet E); CD161 (BV785, Violet A); and CD8b (PC5, Green C). For the other patients for which paired samples at Tl and T2 were not available, Mann Whitney unpaired test was applied. Results

Cohorts of cancer patients (NSCLC and Neuroendocrine digestive and lung tumors) treated with immune checkpoint therapy (anti-PD-1) were studied. In these cohorts, a tumor biopsy is collected at patient inclusion and blood samples are collected before (Tl) and after (T2) the beginning of the treatment. Patients presenting a response (R), stable disease (SD) or no response, , progressive disease (PD) to treatment were retrospectively selected and the peripheral T cell compartment from frozen PBMCs at 2 timepoints (including baseline) was analyzed by FACS using the gating strategy presented in Figure 1. The results are presented in Figures 2 and 3.

At baseline (Tl), although total Treg percentages do not significantly change between responders and non-responders (Figure 2E), responders display a statistically significant lower abundance of Effector Tregs compared to non-responders in both cohorts. As shown in Figure 2A, 2D and Figure 3A, responders have the lowest frequencies of effector Tregs among total Tregs, and statistically significant from SD and PD patients. Thus, these results indicate that a low abundance of Effector Tregs at baseline is associated to response to a-PD- 1 treatment.

On treatment (T2), responders display a higher abundance of Effector Tregs compared to non- responders. As shown in Figure 2B and Figure 3B, responders have the highest frequencies of effector Tregs among total Tregs. Thus, these results indicate that a high abundance of Effector Tregs on treatment is associated to response to a-PD-1 treatment.

Finally, the dynamic changes between responders and non-responders upon a-PD-1 treatment were studied. Responder patients show a statistically significant increase in effector Tregs. As shown in Figure 2C and Figure 3C, responder patients show a statistically significant increase in effector Treg fractions among total Tregs upon a-PDl treatment, while nonresponder patients do not.

Overall, these results show that:

- (i) At baseline, cancer patients who respond to a-PDl have less than 13.5% of effectors Tregs among total Tregs (Figure 2A, 2B and 2D; Figure 3A and 3B). - (ii) Cancer patients who respond to a-PDl show an increase in effector Treg among total Treg cells upon a-PDl treatment, while stable disease and non-responder patients do not (Figure 2B and 2C; Figure 3B and 3C).

Example 2

The biomarker quality of the frequency of effector regulatory T cells has been further confirmed by transcriptomic analysis at the single-cell level in the same cohort of patients described in example 1.

Single-cell RNA-seq analysis

Single-cell transcriptomic analysis was performed on FACS-sorted Tregs-enriched (DAPI- CD4+ CD25hi CD1271o) fraction of peripheral blood mononuclear cells collected pre- and post-treatment from patients treated with anti-PDl therapy, as part of the paired samples cohort. Sorted cells were processed using a microfluidics-based single-cell capture system and sequenced with 5' gene expression and V(D)J enrichment kits according to the manufacturer's protocol. Transcript count matrices were generated using standard pipelines.

Following quality control and ambient RNA correction, the Treg-enriched datasets were integrated and clustered across patients and timepoints using established dimensionality reduction and batch correction methods. Cell identities were annotated based on marker gene expression.

For transcriptomic confirmation of the effector Treg biomarker, a naive gene module (SATB 1, CCR7, TCF7, LEF1) and an effector module (TNFRSF9, CTLA4, BATF, TNFRSF18) were defined. Module scores were calculated for each cell, and a two-dimensional scatterplot of naive versus effector scores was used to gate a population corresponding to effector regulatory T cells.

Group-level comparisons of effector Treg frequency across clinical response categories (responder, stable disease, progressive disease) at baseline were tested using Wilcoxon ranksum test and pre- and post-treatment comparisons within each patient group were evaluated using the Wilcoxon signed rank test. Results

All single cells were projected in this 2 dimensional space and 3 populations of interest were identified: naive, intermediate, and effector Tregs (Figure 4). Cells falling within the “effector” gate displayed high expression of activation and suppressive markers and corresponded to the transcriptomic counterpart of the phenotypic CD3+CD4+CD127- CD45RA-CD25hi eTregs identified by flow cytometry.

The proportion of effector Tregs (as defined by transcriptomic signature gating) was computed for each patient, using the baseline sample. As observed with the FACS data, patients classified as responders exhibited significantly lower baseline frequencies of effector Tregs compared to stable disease or progressive disease groups (Figure 5). Moreover, when comparing pre- and post- treatment samples in responders, the effector Treg frequency increased significantly after anti-PDl treatment, while no such change was observed in SD or PD patients (Figure 6). This dynamics were specific to the effector subset and were not reproduced when naive or intermediate populations were analyzed alone.

Accordingly, the transcriptomic data confirms that a low baseline proportion of effector Tregs, whether measured by surface markers (FACS) or inferred via gene expression, is predictive of response to immune checkpoint blockade. The combined use of surface protein and gene expression data provides orthogonal validation of the biomarker, increasing its robustness and potential for diagnostic application.

Claims

1. A method for selecting a cancer patient for a cancer treatment or for predicting or monitoring the response of a cancer patient to a cancer treatment, comprising: determining the level of effector regulatory T cells in a biological sample from the patient, wherein said level of effector regulatory T cells correlates with the responsiveness of said patient to the cancer treatment.

2. The method according to claim 1, wherein the biological sample is a blood sample, preferably whole-blood or PBMC fraction thereof.

3. The method according to claim 1 or claim 2, wherein the biological sample is obtained at baseline, before initiation of the cancer treatment.

4. The method according to claim 1 or claim 2, wherein the biological sample is obtained during the cancer treatment.

5. The method according to any one of the preceding claims, wherein said effector regulatory T cells are:

CD3+CD4+CD127-CD45RA-CD25hi cells or CD3+CD4+CD127- CD45RO+CD25hi cells and/or

6. The method according to any one of the preceding claims, wherein said level is the percentage of effector regulatory T cells among the population of regulatory T cells in said biological sample.

7. The method according to claim 6, which comprises determining:

- the percentage of CD4+CD127-CD45RA-CD25hi cells or CD4+CD127- CD45RO+CD25hi cells within the population of CD4+CD127-CD25+ cells in said biological sample; and/or - the expression level of at least one gene, and in particular of all genes, selected from TNFRSF9, CTLA4, BATF, and TNFRSF18 in said biological sample.

8. The method according to any one of the preceding claims, which further comprises: comparing the determined level of effector regulatory T cells with a reference level.

9. The method according to claim 8, wherein said reference level is a percentage of 13.5% of effector regulatory T cells among the population of regulatory T cells.

10. The method according to any one of the preceding claims, which is for predicting the response of a cancer patient to a cancer treatment, wherein the responsiveness of said patient to the cancer treatment is inversely proportional to the level of effector regulatory T cells in a biological sample obtained at baseline.

11. The method for predicting the response of a cancer patient to a cancer treatment according to claim 10, wherein a level of effector regulatory T cells at baseline lower than the reference is predictive of the responsiveness of said patient to a cancer treatment and/or a level of effector regulatory T cells at baseline higher than the reference level is predictive of the inefficacy or lower efficacy of a cancer treatment in the cancer patient.

12. The method according to any one of the preceding claims, which is for predicting or monitoring the response of a cancer patient to a cancer treatment, wherein the responsiveness of said patient to the cancer treatment is proportional to the level of effector regulatory T cells in a biological sample obtained during cancer treatment.

13. The method for predicting or monitoring the response of a cancer patient to a cancer treatment according to claim 12, wherein a level of effector regulatory T cells during cancer treatment higher than the reference is predictive of the responsiveness of said patient to a cancer treatment and/or a level of effector regulatory T cells lower than the reference level is predictive of the inefficacy or lower efficacy of a cancer treatment in the cancer patient.

14. The method according to any one of the preceding claims, which is for monitoring the response of a cancer patient to a cancer treatment, wherein an increase of the level of effector regulatory T cells between the level of effector regulatory T cells at baseline and after cancer treatment in the cancer patient is predictive of the responsiveness of said patient to a cancer treatment and/or a decrease or unchanged level of effector regulatory T cells between the level of effector regulatory T cells at baseline and after cancer treatment is predictive of the inefficacy or lower efficacy of a cancer treatment in the cancer patient.

15. The method according to any one of the preceding claims, wherein the cancer is selected from the group consisting of: lung cancer; neuroendocrine cancer; breast cancer; kidney cancer; prostate cancer; colon or colorectal cancer; ovarian cancer; uterus cancer, such as endometrial or cervical cancer; liver cancer such as hepatocellular carcinoma; skin cancer such as melanoma; brain cancer; thyroid cancer; adrenal gland cancer; gastric cancer; esophageal cancer; pancreatic cancer; muscle cancer; head and neck cancer; hypopharynx cancer; and bladder cancer.

16. The method according to any one of the preceding claims, wherein the cancer treatment is radiotherapy, chemotherapy, immunotherapy or a combination thereof.

17. The method according to claim 16, wherein the cancer treatment is immunotherapy with an immune checkpoint inhibitor alone or in combination therapies with radiotherapy, chemotherapy, cancer vaccine, and/or other checkpoint inhibitors.

18. The method according to claim 17, wherein the immune checkpoint inhibitor(s) are selected from the group consisting of : PD-1 inhibitors, PD-L1 inhibitors, Lag-3 inhibitors, Tim-3 inhibitors, TIGIT inhibitors, BTLA inhibitors, V-domain Ig suppressor of T-cell activation (VISTA) inhibitors, CTLA-4 inhibitors and IDO inhibitors. The method according to any one of the preceding claims, which further comprises classifying the cancer patient into responder and non-responder to cancer treatment based on the level of effector regulatory T cells determined in the biological sample from the patient.