WO2022112198A1 - Method to select the optimal immune checkpoint therapies - Google Patents

Method to select the optimal immune checkpoint therapies Download PDF

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WO2022112198A1
WO2022112198A1 PCT/EP2021/082574 EP2021082574W WO2022112198A1 WO 2022112198 A1 WO2022112198 A1 WO 2022112198A1 EP 2021082574 W EP2021082574 W EP 2021082574W WO 2022112198 A1 WO2022112198 A1 WO 2022112198A1
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cancer
genes
group
tvn
immune checkpoint
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French (fr)
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Vladimir Lazar
Catherine BRESSON
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Worldwide Innovative Network
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    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the present invention relates to the field on oncology, especially to personalized medicine in cancer therapy.
  • Immunotherapy represents today a major field of interest in the treatment of cancers. Indeed, leverage of the negative immune checkpoint blockade is able to induce durable responses across multiple types of cancers. The most advanced knowledge has been generated around the therapies targeting PD-1/PD-L1 or CTLA-4, and to a less extent anti-LAG3 or anti-TLR4. However, overall, only a fraction of patients has a therapeutic benefit of treatments targeting the immune checkpoint blockade, more specifically less than 25% of them, and only some have responses with long duration. Indeed, most patients do not respond or, after an initial response, develop resistance. Many patients have also important toxicity problems due to the treatment. Therefore, identifying the optimal treatment that would lead to a clinical benefit remains an important unmet need in the field.
  • High TMB correlates with a higher likelihood of expression of neoantigens thus triggering infiltrating effector T-cells through a complex mechanism involving interaction of different immune cells (APC, LyTCD4+ Helper 1, LyTCD8+, NK, Tregs).
  • the activation of the T cell receptor (TCR) induces the selection and proliferation of cytotoxic T cells (LyT), a process that is strictly controlled by PD-1 which blocks this activation after engaging PDL-1 or PDL-2.
  • CTLA-4 blocks another activator of TCR, CD28, by competitive binding of B7-1 and B7-2 ligands, thereby contributing to the negative blockade of LyTCD8+. Leveraging this negative immune-blockade led to development of effective Immuno-oncology (10) treatments using anti-PD-1 and anti-CTLA-4 antibodies.
  • LAG-3 delivers inhibitory signals upon binding to ligands, such as FGL1 (responsible for LAG-3 T-cell inhibitory function). Following TCR engagement, LAG-3 associates with CD3-TCR and directly inhibits T-cell activation. LAG-3 negatively regulates the proliferation, activation, effector function and homeostasis of both CD8(+) and CD4(+) T-cells. It also mediates immune tolerance. LAG-3 is constitutively expressed on a subset of regulatory T-cells (Tregs) and contributes to their suppressive function.
  • Tregs regulatory T-cells
  • TLR-4 lipopolysaccharide
  • LPS lipopolysaccharide
  • LAG-3 and TLR-4 make them key candidates for modulation in an attempt to increase the 10 therapeutic armamentarium.
  • anti-LAG-3 BMS-986016
  • anti-PD- 1 nivolumab
  • an anti-TLR-4 antibody NI-0101 was tested in a phase I first in-human study performed on healthy volunteers, as well as a phase II study in patients with Rheumatoid Arthritis, and showed a safe profile.
  • regulators of immune activity such as VISTA, TIM-3, TIGIT, TQRb, ICOS, 0X40 and IDOl, may also play an important role that should be considered.
  • Different combinations have already been tested clinically such as anti-PD-1 combined with anti-TIM-3; anti-PD-1 combined with 0X40; anti-PD-1 combined with anti-TIGIT; anti-PD-1 combined with anti-IDOl, and other novel immune-checkpoint inhibitors. All these studies were performed without any specific biomarker strategy beyond the conventional TMB and/or PDL-1 status, which may explain the varying degrees of efficacy reported.
  • the inventors also explored the activation of VISTA, TIM-3, TGF , TIGIT, ICOS, 0X40 and IDOl in these patients.
  • Expanding 10 therapeutic options may significantly increase the fraction of patients with clinical benefit.
  • the present invention concerns a method for selecting an optimal immune checkpoint blockade therapy for treating a subject suffering from cancer, wherein the method comprises:
  • a histologically matched normal sample of a set of genes comprising the following genes or groups of genes: 1) a group of genes comprising PD-1, PD-L1, and optionally PD-L2; 2) a group of genes comprising CTLA-4, CD28, and optionally CD80 and CD86; 3) LAG3; 4) TLR4 and optionally one or more of the following genes 5) VISTA, TIM-3, TIGIT, TGF , ICOS, 0X40 and IDOl; and wherein the tumor and histologically matched normal samples are from the same subject;
  • the invention also concerns a method for determining if a subject suffering from a cancer is susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy, wherein the method comprises:
  • a histologically matched normal sample of a set of genes comprising the following genes or groups of genes: 1) a group of genes comprising PD-1, PD-L1, and optionally PD-L2; 2) a group of genes comprising CTLA-4, CD28, and optionally CD80 and CD86; 3) LAG3; 4) TLR4 and optionally one or more of the following genes 5) VISTA, TIM-3, TIGIT, ICOS, 0X40 and IDOl; and wherein the tumor and histologically matched normal samples are from the same subject;
  • the Fc TvN for a particular group of genes is the average or arithmetic mean of the fold changes of genes belonging to the group of genes and having a Fc TvN of 1.3 or more.
  • the method may further comprise the multiplication of the Fc TvN by expression intensity of the gene (l n ), either in the tumor sample (l n T) or in the histologically matched normal sample (l n N).
  • the normalization step is performed using a calibrator consisting of Fc TvN for each gene or group of genes of a set of subjects.
  • the mRNA expression level in the tumor sample and in the normal histologically matched sample is further corrected with the expression of miRNA targeting the transcript.
  • mRNA expression level of other genes is studied in the method but the total number of genes is no more than 16 genes.
  • the normalization step is in the form of deciles and the score is from 1 to 10, with 10 being the highest FcTvN and 1 the lowest one.
  • a score superior to 4 is predictive of an efficacy of an immune checkpoint blockade therapy for treating a cancer in the subject or of a susceptibility of the subject to benefit from an immune checkpoint blockade therapy.
  • the method may further comprise the selection of the optimal immune checkpoint blockade therapy based on the ranked genes or group of genes, preferably the one, two or three gene(s) or group(s) of genes having the highest scores.
  • the immune checkpoint blockade therapy is a monotherapy, a bitherapy or a tritherapy.
  • the immune checkpoint blockade therapy is selected from the group consisting of an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti-CTLA-4 antibody, an anti-LAG3 antibody, an anti-TLR4 antibody and any combination thereof, and optionally one or several among an anti-OX40, an anti-IDOl, an anti-TIME3, an anti-TIGIT and an anti-VISTA.
  • the immune checkpoint blockade therapy is selected from the group consisting of pembrolizumab, nivolumab, atezolizumab, durvalumab, avelumab, ipilimumab, NI-0101, BMS- 986016, Sym022, GSK2831781, AZ MEDI5752, and optionally AZ MEDI6469, GSK3174998, INCB024360, MBG453, MTIG7192A, CI-8993 and tiragolumab.
  • the cancer is selected from the group consisting of prostate cancer, bladder cancer, breast cancer, colon cancer, colorectal cancer, Esophagus cancer, hypopharynx cancer, gastric cancer, rectum cancer, head and neck cancer, liver cancer, brain cancer, hepatocarcinoma, kidney cancer, ovarian cancer, cervical cancer, pancreatic cancer, sarcoma, lung cancer, lymphoma, osteosarcoma, melanoma, neuroendocrine cancer, pleural cancer, Small Intestine cancer, endometrial cancer, soft tissue cancer, non small cell lung carcinomas (NSCLC), metastatic non-small cell lung cancer, muscle cancer, adrenal cancer, thyroid cancer, uterine cancer, advanced renal cell carcinoma (RCC), and sub ependymal giant cell astrocytoma (SEGA) associated with tuberous sclerosis (TS), preferably colorectal cancer, head and neck cancer, lung cancer, non-small cell lung carcinomas (NSCLC), bladder cancer, breast cancer, esophagus cancer
  • cancer refers to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, and/or immortality, and/or metastatic potential, and/or rapid growth and/or proliferation rate, and/or certain characteristic morphological features.
  • This term refers to any type of malignancy (primary or metastases) in any type of subject. It may refer to solid tumor as well as hematopoietic tumor.
  • tumor sample refers to any sample containing tumor cells derived or retrieved from a patient or a subject.
  • tumor cells may be obtained from fluid sample such as blood, plasma, urine and seminal fluid samples as well as from biopsies, organs, tissues or cell samples.
  • tumor cells are obtained from tumor biopsy or resection sample from the patient.
  • Cancer tissues are particularly composed of cancer cells and the surrounding cancer stromal cells, vascular endothelial cells, and immune cells, in addition to the extracellular matrix.
  • the sample contains only tumor cells.
  • the cancer sample contains nucleic acids and/or proteins.
  • samples containing tumor cells may be treated prior to their use.
  • a tumor cell enrichment sorting may be performed. It may be fresh, frozen or fixed (e.g. formaldehyde or paraffin fixed) sample.
  • anti-cancer therapy As used herein, the terms “anti-cancer therapy”, “anti-cancer treatment” and “anticancer agents” are used interchangeably and refer to compounds which are used in the treatment of cancer, such as chemotherapeutic or immunotherapeutic compounds.
  • the anti-cancer therapy is an immune- oncology therapy, especially an immune checkpoint inhibitor therapy.
  • the terms “subject”, “individual” or “patient” are interchangeable and refer to an animal, preferably to a mammal, even more preferably to a human.
  • the term “subject” can also refer to non-human animals, in particular mammals such as dogs, cats, horses, cows, pigs, sheep and non-human primates, among others.
  • a subject who responds to a treatment of cancer refers to a subject who responds to a treatment of cancer, for example such as the volume of the tumor is decreased, at least one of his symptoms is alleviated, or the development of the cancer is stopped, or slowed down.
  • a subject who responds to a cancer treatment is a subject who will be completely treated (cured), i.e., a subject who will survive cancer or a patient that will survive longer.
  • a subject who responds to a cancer treatment is also, in the sense of the present invention, a subject who has an overall survival higher than the mean overall survival known for the particular cancer, in particular in the absence of a treatment or in the presence of unsuitable treatment.
  • a patient who shows a good therapeutic benefit from a treatment that is to say a longer disease-free survival, a longer overall survival, a decreased metastasis occurrence, a decreased tumor growth and/or a tumor regression in comparison to a population of patients suffering from the same cancer, in particular in the absence of a treatment.
  • non-responder refers to a subject who does not respond to an anti cancer treatment, for example such as the volume of the tumor does not substantially decrease, or the symptoms of the cancer in the subject are not alleviated, or the cancer progresses, for example the volume of the tumor increases and/or the tumor generates local or distant metastasis.
  • non-responder also refers to a subject who will die from cancer, or will have an overall survival lower than the mean overall survival known for the particular cancer.
  • “poor responder” or “non-responder” is intended a patient who shows a weak therapeutic benefit of the treatment, that is to say a shorter disease-free survival, a shorter overall survival, an increased metastasis occurrence and/or an increased tumor growth in comparison to a population of patients suffering from the same cancer and having the same treatment.
  • treatment refers to any act intended to ameliorate the health status of patients such as therapy, prevention, prophylaxis and retardation of the disease or of the symptoms of the disease. It designates both a curative treatment and/or a prophylactic treatment of a disease.
  • a curative treatment is defined as a treatment resulting in cure or a treatment alleviating, improving and/or eliminating, reducing and/or stabilizing a disease or the symptoms of a disease or the suffering that it causes directly or indirectly.
  • a prophylactic treatment comprises both a treatment resulting in the prevention of a disease and a treatment reducing and/or delaying the progression and/or the incidence of a disease or the risk of its occurrence.
  • such a term refers to the improvement or eradication of a disease, a disorder, an infection or symptoms associated with it. In other embodiments, this term refers to minimizing the spread or the worsening of cancers.
  • Treatments according to the present invention do not necessarily imply 100% or complete treatment. Rather, there are varying degrees of treatment of which one of ordinary skill in the art recognizes as having a potential benefit or therapeutic effect.
  • diagnosis refers to the determination as to whether a subject is likely to be affected by a cancer or to the determination of whether a subject is susceptible to benefit from a treatment.
  • diagnosis markers the presence, absence, or amount of which is indicative of the presence or absence of the cancer.
  • diagnosis it is also intended to refer to the provision of information useful for the diagnosis of cancer, for the prognosis of patient survival or for the determination of the response of a patient to an anti-cancer treatment.
  • the term "marker” or “biomarker” refers to a measurable biological parameter that helps to predict the occurrence of a cancer or the efficiency of an anti-cancer treatment. It is in particular a measurable indicator for predicting the clinical outcome of a patient undergoing anticancer therapy or the response of a subject having cancer to an anti-cancer therapy.
  • a histologically matched normal sample or “matched normal sample” or “matched normal control” is meant herein a sample that corresponds to the same or similar organ, tissue or fluid as the cancer sample to which it is compared.
  • a histologically matched normal sample can be a matching normal adjacent mammary tissue sample.
  • the histologically matched normal sample is for example a sample from normal bronchial mucosa. Further examples are provided here below, in particular under the "Patients and tumor” paragraph.
  • the terms “clinical outcome” and “prognosis” are interchangeable and refer to the determination as to whether a subject is likely to be affected by a cancer relapse, recurrence or metastasis, or death. These terms also relate to the survival, in particular the overall survival.
  • OS Overall survival
  • PFS progression-free survival
  • a or “an” can refer to one of or a plurality of the elements it modifies (e.g., "a reagent” can mean one or more reagents) unless it is contextually clear either one of the elements or more than one of the elements is described.
  • the methods disclosed herein rely on the study of gene expression, in particular in a set of genes.
  • the method according to the invention comprises providing expression level in a tumor sample and a histologically matched normal sample of a set of genes comprising the following genes or groups of genes: 1) a group of genes comprising PD-1, PD-L1, and optionally PD-L2; 2) a group of genes comprising CTLA-4 and CD28, and optionally CD80 and CD86; 3) LAG3; 4) TLR4, and optionally one or more of the following genes 5) VISTA, TIM-3, TIG IT, TGF , ICOS, 0X40 and IDOl.
  • genes useful for assessing the responsiveness of a patient to an immune checkpoint blockade therapy in particular a therapy targeting PD-1/PD-L1.
  • This group of genes includes PD- 1, PD-L1 and PD-L2.
  • This group can be referred as group 1.
  • the role of PD-1, PD-L1 and PD-L2 is well-known.
  • PD-1 negatively regulates T cell activation through interaction with PD-L1 and PD-L2.
  • the therapy targeting PD-1/PD-L1 aims to block the interaction between PD-1 and PD-L1 and/or PD-L2 so as to remove this blockade.
  • PD1 PD-1
  • PCDC1 Programmed cell death 1
  • CD279 refers to the human PDCD1 gene, for example such as described under the Uniprot reference: Q15116 or GeneCard ID: GC02M241849.
  • the terms “PDL1” , “PD-L1”, “PCDCL1”, “PD-l-ligand 1”, “Programmed cell death 1 ligand 1", and “CD274" are used interchangeably and refer to the human CD274 gene, for example such as described under the Uniprot reference: Q9NZQ7 or Gene ID: 29126.
  • the terms “PDL2”, “PD-l-ligand 2”, “Programmed Cell Death 1 Ligand 2” are used interchangeably and refer to the human PDL2 gene, for example such as described under the Uniprot reference: Q2LC89 or GeneCard ID: GC09P005510.
  • CTLA- 4 genes useful for assessing the responsiveness of a patient to an immune checkpoint blockade therapy, especially a therapy targeting CTLA-4.
  • This set of genes includes CTLA- 4 and CD28 and may optionally comprise CD80 and CD86.
  • This group can be referred as group 2.
  • CTLA- 4 genes useful for assessing the responsiveness of a patient to an immune checkpoint blockade therapy, especially a therapy targeting CTLA-4.
  • This set of genes includes CTLA- 4 and CD28 and may optionally comprise CD80 and CD86.
  • This group can be referred as group 2.
  • CTLA- 4 and CD28 genes useful for assessing the responsiveness of a patient to an immune checkpoint blockade therapy, especially a therapy targeting CTLA-4.
  • This set of genes includes CTLA- 4 and CD28 and may optionally comprise CD80 and CD86.
  • This group can be referred as group 2.
  • CTLA- 4 and CD28 genes useful for assessing the responsiveness of a patient to an immune checkpoint blockade therapy, especially a
  • CTLA-4 negatively regulates TCR signaling through competition with the costimulating CD28 for the binding of CD80/CD86 for which CTLA-4 has a higher affinity and avidity.
  • the therapy targeting CTLA-4 aims to block the interaction between CTLA-4 and CD80 or CD86 so as to remove this blockade.
  • CTLA4 Cytotoxic T-Lymphocyte Associated Protein 4" and “CD152” are used interchangeably and refer to the human CTLA4 gene, for example such as described under the Uniprot reference: P16410 or GeneCard ID: GC02P203867.
  • CD28 As used herein, the terms "CD28”, “T-Cell-Specific Surface Glycoprotein CD28” and “TP44” are used interchangeably and refer to the human CD28 gene, for example such as described under the Uniprot reference: P10747 or GeneCard ID: GC02P203706.
  • CD80 T-Lymphocyte Activation Antigen CD80
  • CTLA-4 Counter-Receptor B7.1 » and “B7-1” are used interchangeably and refer to the human CD80 gene, for example such as described under the Uniprot reference: P33681 or GeneCard ID: GC03M119524.
  • CD86 CD86
  • T-Cell-Specific Surface Glycoprotein CD28 and “TP44” are used interchangeably and refer to the human CD86 gene, for example such as described under the Uniprot reference: P42081or GeneCard ID: GC03P122055.
  • CTLA-4 and PD-1 act at least in part through a similar molecular mechanism of attenuating CD28-mediated costimulation, it seems important to provide in the method information about the expression of genes relating to PD-1 and those relating to CTLA-4 (i.e., groups 1 and 2) in order to provide a global assessment of the immune-negative blockade.
  • LAG3 is an inhibitory receptor on antigen activated T-cells. It delivers inhibitory signals upon binding to ligands, such as FGL1. Following TCR engagement, LAG3 associates with CD3-TCR in the immunological synapse and directly inhibits T-cell activation. A synergistic effect with PD-1 has been mentioned. LAG3 may act as a co-receptor of PD-1. Then, it could be interesting to provide data on the expression of LAG3.
  • LAG3 Lymphocyte Activating 3
  • CD223 CD223
  • FDC FDC
  • the stimulation of TLR4, in particular its overexpression, has also an important role in the stimulation of the immune response. Therefore, the fourth group comprises TLR4.
  • TLR4 the terms “TLR4", “Toll Like Receptor 4", "CD284" and “ARMD10” are used interchangeably and refer to the human TLR4 gene, for example such as described under the Uniprot reference: 000206 or GeneCard ID: GC09P117704.
  • TIM3, TIGIT, TGF , VISTA, ICOS, 0X40, IDOl can be added in the set of genes to be studied for their expression.
  • TIM3 Hepatitis A virus cellular receptor 2
  • HAVCR2 Hepatitis A virus cellular receptor 2
  • T-cell immunoglobulin and mucin domain-containing protein 3 T-cell immunoglobulin mucin receptor 3
  • T cell membrane protein-3 T cell membrane protein-3
  • TIGIT T-cell immunoreceptor with Ig and ITIM domains
  • V-set and immunoglobulin domain-containing protein 9 V-set and transmembrane domain-containing protein 3
  • TGF Transforming growth factor beta-1
  • TGFB Transforming growth factor beta-1
  • TGFB1 Transforming growth factor beta1
  • TGFbeta TGFbeta
  • VISTA V-type immunoglobulin domain-containing suppressor of T-cell activation
  • Platinum receptor Gi24 Stress-induced secreted protein-1
  • V-set domain-containing immunoregulatory receptor V-set domain-containing immunoregulatory receptor
  • ICOS Inducible T-cell costimulator
  • Activation-inducible lymphocyte immunomediatory molecule and “CD278” are used interchangeably and refer to the human ICOS gene, for example such as described under the Uniprot reference: Q9Y6W8 or GeneCard ID: GC02P203937.
  • 0X40 Tumor necrosis factor receptor superfamily member 4
  • TNFRSF4 Tumor necrosis factor receptor superfamily member 4
  • ACT35 antigen antigen of the human TNFRSF4 gene
  • IDOl Indoleamine 2,3-Dioxygenase 1
  • Indoleamine-Pyrrole 2,3-Dioxygenase and “INDO” are used interchangeably and refer to the human IDOl gene, for example such as described under the Uniprot reference: P14902 or GeneCard ID: GC08P039891.
  • Table 1 - Table with a summary of the description of the genes.
  • the set of genes may further comprise additional genes.
  • the total number of genes in each group of genes is no more than 20, 19, 18, 17, 16, 15, 14, 13, 12, or 11 genes.
  • the set of genes taken into consideration in the methods of the present invention includes or comprises at least one of the following set of genes:
  • Any of the above-mentioned set may further include or comprise one or several genes selected in the group consisting of VISTA, TIM-3, TIG IT, TGF , ICOS, 0X40 and IDOl.
  • the methods disclosed herein allow the selection of an anti-cancer therapy for treating a subject suffering from cancer or to assess the responsiveness of a subject to an anti-cancer therapy.
  • the anti cancer therapy is an immune-oncology therapy, especially an immune checkpoint blockade therapy.
  • immune checkpoint blockade therapy refers to a therapy or treatment that uses medications known as immune checkpoint inhibitors to address several types of disease, especially such as cancer.
  • Such therapy targets immune checkpoints, key regulators of the immune system that when stimulated can dampen the immune response to an immunologic stimulus. When these checkpoints are blocked, T cells can kill cancer cells better.
  • approved checkpoint inhibitors target for example the immune checkpoint CTLA4, PD-1, and PD-L1.
  • the immune checkpoint blockade therapy disclosed herein can be a monotherapy, a bitherapy ora tritherapy.
  • the immune checkpoint blockade therapy can be a PD-1 inhibitor, alone (monotherapy) or in combination with a CTLA4 inhibitor (bitherapy), in combination with a CTLA4 inhibitor and a LAG3 inhibitor (tritherapy).
  • the immune checkpoint blockade therapy disclosed herein can be a monotherapy, a bitherapy or a tritherapy of any of the therapy described below.
  • the immune checkpoint blockade therapy can be any of: (i) a PD-1 inhibitor, a PD-L1 inhibitor, a CTLA-4 inhibitor, a LAG3 inhibitor, a TLR4 inhibitor (monotherapies), (ii) a PD-1 inhibitor and a CTLA-4 inhibitor, a PD-1 inhibitor and a LAG3 inhibitor, a PD-1 inhibitor and a TLR4 inhibitor, a PD-L1 inhibitor and a CTLA-4 inhibitor, a PD-L1 inhibitor and a LAG3 inhibitor, a PD-L1 inhibitor and a TLR4 inhibitor, a CTLA-4 inhibitor and a LAG3 inhibitor, a CTLA-4 inhibitor and a TLR4 inhibitor, a LAG3 inhibitor and a TLR4 inhibitor, a LAG3 inhibitor and a TLR4 inhibitor (bitherapies) and iii) a PD-1 inhibitor, a CTLA-4 inhibitor and a LAG3 inhibitor; a PD-1 inhibitor, a CTLA-4 inhibitor and
  • the immune checkpoint blockade therapy may be selected from the group consisting of a PD- 1 inhibitor, a PD-L1 inhibitor, a CTLA-4 inhibitor, a LAG3 inhibitor, a TLR4 inhibitor and any combination thereof, and optionally an 0X40 inhibitor, an IDOl inhibitor, an ICOS inhibitor, a TIME3 inhibitor, a TIG IT inhibitor, a TGF inhibitor and a VISTA inhibitor and any combination thereof.
  • the immune checkpoint blockade therapy is selected from the group consisting of an anti-PDT antibody, an anti-PD-Ll antibody, an anti-CTLA-4 antibody, an anti-LAG3 antibody, an anti-TLR4 antibody and any combination thereof, and optionally an anti-OX40 antibody, an anti-IDOl antibody, an anti-ICOS antibody, an anti-TIME3 antibody, an anti-TIGIT antibody, an anti-TGFB antibody and an anti-VISTA antibody and any combination thereof.
  • the anticancer therapy is an immune checkpoint inhibitor, preferably a PDT or PD-L1 inhibitor.
  • Programmed cell death protein 1 (PDT) inhibitors and programmed death-ligand 1 (PD-L1) inhibitors are a group of checkpoint inhibitor anticancer drugs that block the activity of PDT and PDL1 immune checkpoint proteins present on the surface of cells.
  • PDT programmed cell death protein 1
  • PD-L1 programmed death-ligand 1
  • An immune checkpoint blockade therapy targeting PD-1/PD-L1 can be for instance an inhibiting anti-PDT antibody, an inhibiting anti-PD-Ll antibody or an inhibiting anti-PD-L2 antibody, preferably an inhibiting anti-PDT antibody or an inhibiting anti-PD-Ll antibody.
  • Such antibodies are well-known in the art. Several antibodies have already been accepted as drug. Others are still in clinical development.
  • the anti-PDl antibody can be selected from the group consisting of Pembrolizumab (also known as Keytruda lambrolizumab, MK-3475), Nivolumab (Opdivo, MDX-1106, BMS-936558, ONO-4538), Pidilizumab (CT-011), Cemiplimab (Libtayo), Camrelizumab, AUNP12, AMP-224, AGEN-2034, BGB-A317 (Tisleizumab), PDR001 (spartalizumab), MK-3477, SCH-900475, PF-06801591, JNJ-63723283, genolimzumab (CBT-501), LZM-009, BCD-100, SHR-1201, BAT-1306, AK-103 (HX-008), MEDI-0680 (also known as AMP-514) MEDI
  • BI-754091 CBT-501, INCSHR1210 (also known as SHR- 1210), TSR-042 (also known as ANB011), GLS-010 (also known as WBP3055), AM-0001 (Armo), STI-1110 (see WO 2014/194302), AGEN2034 (see WO 2017/040790), MGA012 (see WO 2017/19846), or IBI308 (see WO 2017/024465, WO 2017/025016, WO 2017/132825, and WO 2017/133540), monoclonal antibodies 5C4, 17D8, 2D3, 4H1, 4A11, 7D3, and 5F4, described in WO 2006/121168.
  • Bifunctional or bispecific molecules targeting PDT are also known such as RG7769 (Roche), XmAb20717 (Xencor), MEDI5752 (AstraZeneca), FS118 (F-star), SL-279252 (Takeda) and XmAb23104 (Xencor).
  • anti-PD-Ll are already clinically approved and others are still in clinical developments.
  • the anti-PD-Ll antibody can be selected from the group consisting of Atezolizumab (Tecentriq), Avelumab (Bavencio), Durvalumab (Imfinzi), KN035, CK-301 (Checkpoint Therapeutics), AUNP12, CA-170, BMS-986189.
  • the PD-1 or PD-L1 inhibitor is selected from the group consisting of atezolizumab, durvalumab, avelumab, nivolumab, pembrolizumab, pidilizumab, cemiplimab, camrelizumab, sintilimab (IBI308), tislelizumab (BGB-A317), toripalimab (JS 001), dostarlimab (TSR-042, WBP-285), BMS 936559, MPDL3280A, MSB0010718C, MEDI4736 and any combination thereof, preferably nivolumab, pembrolizumab and atezolizumab.
  • the anticancer therapy is an immune checkpoint inhibitor, preferably a CTLA-4 inhibitor.
  • An immune checkpoint blockade therapy targeting CTLA-4 can be for instance an inhibiting anti-CTLA-4 antibody.
  • Such antibodies are well-known in the art. Several antibodies have already been accepted as drug. Others are still in clinical development.
  • the CTLA-4 inhibitor can be selected from ipilimumab, tremelimumab, and AGEN-1884.
  • Anti-CTLA-4 antibodies are also disclosed in WO18025178, W019179388,
  • WO16015675 WO12120125, W009100140 and W007008463.
  • the anticancer therapy is an immune checkpoint inhibitor, preferably a LAG3 inhibitor.
  • An immune checkpoint blockade therapy targeting LAG3 can be for instance an inhibiting anti-LAG3 antibody.
  • Such antibodies are well-known in the art. Several antibodies have already been accepted as drug. Others are still in clinical development.
  • the LAG-3 inhibitor can be selected from IMP321 (Immuntep ® ), LAG525 (Novartis), BMS-986016 (Bristol-Myers Squibb), or TSR-033 (Tesaro).
  • Anti-LAG-3 antibodies are also disclosed in W02008132601, EP2320940, W019152574.
  • the anticancer therapy is an immune checkpoint inhibitor, preferably a TLR4 inhibitor.
  • An immune checkpoint blockade therapy targeting TLR4 can be for instance an inhibiting anti-TLR4 antibody.
  • Numerous antagonist antibodies are disclosed in the art, illustrative examples of which include as described for example in U.S. Pat. App. Pub. No. 2009/0136509 to Blake (e.g., antibodies based on monoclonal antibody MTS510) and U.S. Pat. App. Pub. No. 2012/0177648 to Kosco-Vilbois et a I., are hereby incorporated by reference herein in their entirety.
  • TLR4 antagonist antibodies are available commercially, a non- limiting example of which is NI-0101, which is available from Novlmmune SA (Plan-les- Ouates, Switzerland). TLR4 inhibitor are also under FDA approval, for example such as eritoran (phase III) and ibudilast (Av41 1 ; phase II).
  • the anticancer therapy is an ICOS inhibitor, in particular an anti-ICOS antibody.
  • ICOS antibodies are known in the art, such as JTX-2011, GSK3359609, MEDI-570 (NCI) and KY1044 (Kymab Limited).
  • the anticancer therapy is an IDOl inhibitor, in particular an anti-IDOl antibody.
  • IDOl inhibitors are known in the art, such as LY3381916, BMS-986205, Epacadostat, Navoximod, PF-06840003, INCB024360 (Incyte; 4-( ⁇ 2-[(aminosulfonyl)amino]ethyl ⁇ amino)-N-(3-bromo-4-fiuorophenyl)-N'-hydroxy-l,2,5-oxadiazole- 3-carboximidamide), Indoximod, 1-methyl-tryptophan (New Link Genetics), GDC-0919 (Genentech), indoximod (NewLink Genetics, see, e.g., Clinical Trial Identifier Nos. NCT01191216; NCT01792050), or compounds such as described in WO2015119944.
  • the anticancer therapy is an 0X40 inhibitor, in particular an anti- 0X40 antibody.
  • 0X40 inhibitors are known in the art, such as AZ MEDI6469, MEDI6383, MEDI0562, INCAGN01949, GSK3174998, 9B12, MOXR 0916 and PF-04518600 (PF-8600).
  • the anticancer therapy is a TIM3 inhibitor, in particular an anti-TIM3 antibody.
  • TIM3 antibodies are known in the art, such as MBG453 and MEDI9447 or antibodies described in WO2016111947 and W02011155607A1.
  • the anticancer therapy is a TGFB inhibitor, in particular an anti- TGF antibody.
  • TGF inhibitors are known in the art, such as Trabedersen (AP12009), M7824, Galusertinib (LY2157299), AVID200, Fresolimumab.
  • the anticancer therapy is a TIG IT inhibitor in particular an anti-TIGIT antibody.
  • Antibodies directed against TIG IT are also known in the art, such as tiragolumab, OMP-31M32, MTIG7192A (Genentech), BMS-986207 or AB154, BMS-986207 CPA.9.086, CHA.9.547.18, CPA.9.018, CPA.9.027, CPA.9.049, CPA.9.057, CPA.9.059, CPA.9.083, CPA.9.089, CPA.9.093, CPA.9.101, CPA.9.103, CHA.9.536.1, CHA.9.536.3, CHA.9.536.4, CHA.9.536.5, CHA.9.536.6, CHA.9.536.7, CHA.9.536.8, CHA.9.560.1, CHA.9.560.3, CHA.9.560.4, CHA.9.560.5, CHA.9.560.6, CHA.9.560.7, CHA
  • Anti-TIGIT antibodies are also disclosed in WO16028656, W016106302, W016191643, W017030823, W017037707, WO17053748, WO17152088, WO18033798, WO18102536, WO18102746, W018160704, W018200430, WO18204363, W019023504, WO19062832, W019129221, W019129261, W019137548, W019152574, W019154415, W019168382 and W019215728.
  • the anticancer therapy is a VISTA inhibitor, preferably an anti-VISTA antibody.
  • Antibodies directed against TIG IT are also known in the art, such as CI-8993 (Curis Inc), JNJ-61610588, CA-170
  • the immune checkpoint blockade therapy is selected from the group consisting of pembrolizumab, nivolumab, atezolizumab, durvalumab, avelumab, ipilimumab, NI-0101, BMS-986016, Sym022, GSK2831781, AZ MEDI5752, and optionally AZ MEDI6469, GSK3174998, INCB024360, MBG453, MTIG7192A, CI-8993 and tiragolumab.
  • the methods disclosed herein can be used to predict the optimal anti-cancer treatment or a response to a cancer treatment.
  • the cancer treatment can be any immune checkpoint inhibitor treatment including, but not limited, to the treatments and therapies described here above, in particular under the "Immune checkpoint blockade therapy” paragraph. Examples of cancers are provided here below, in particular under the "Patient and Tumor” paragraph.
  • the invention concerns a method for selecting an optimal immune checkpoint blockade therapy for treating a subject suffering from cancer, wherein the method comprises:
  • a histologically matched normal sample of a set of genes comprising the following genes or groups of genes: 1) a group of genes comprising PD-1, PD-L1, and optionally PD-L2; 2) a group of genes comprising CTLA-4 and CD28, and optionally CD80 and CD86; 3) LAG3; 4) TLR4 and optionally one or more of the following genes 5) ICOS, IDOl, 0X40, TIM-3, TIGIT, TGF , and VISTA; and wherein the tumor and histologically matched normal samples are from the same subject;
  • the present invention also relates to a method for selecting a set of genes for which the differential expression between a tumor sample and a normal histologically matched sample from the same patient is indicative of a therapeutic benefit of a treatment with an immune checkpoint blockade therapy. More particularly, as disclosed above, the method provides the mRNA expression level, and optionally miRNA expression levels, in a tumor sample and a normal histologically matched sample from the same patient.
  • the invention particularly concerns a method for determining if a subject suffering from a cancer is susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy, wherein the method comprises:
  • a histologically matched normal sample of a set of genes comprising the following genes or groups of genes: 1) a group of genes comprising PD-1, PD-L1, and optionally PD-L2; 2) a group of genes comprising CTLA-4 and CD28, and optionally CD80 and CD86; 3) LAG3; 4) TLR4 and optionally one or more of the following genes 5) ICOS, IDOl, 0X40, TIM-3, TIGIT, TGF , and VISTA; and wherein the tumor and histologically matched normal samples are from the same subject;
  • the method according to the invention comprises a step of determining the expression level of one or a set of genes comprising the following genes or groups of genes: 1) a group of genes comprising PD-1, PD-L1, and optionally PD-L2; 2) a group of genes comprising CTLA-4 and CD28; 3) LAG3; 4) TLR4 and optionally one or more of the following genes 5) VISTA, TIM-3, TIGIT, TGF , ICOS, 0X40 and IDOl.
  • the gene expression levels of a cancer sample are compared to the gene expression levels from a normal sample, in particular a cell or tissue known to be free of, or suspected to be free of cancer, preferably from the same patient, more preferably a histologically matched normal sample from the patient.
  • a normal sample in particular a cell or tissue known to be free of, or suspected to be free of cancer, preferably from the same patient, more preferably a histologically matched normal sample from the patient.
  • Determining the expression level for a gene or group of genes such as those identified above can be carried out by any method known in the art and may vary among embodiments of the invention.
  • Determining the expression levels of a gene may be carried out by any method known in the art such as, but not limited to, Northern analysis, mRNA or cDNA microarrays, polymerase chain reaction (PCR), quantitative or semi-quantitative RT-PCR, real time quantitative or semi-quantitative RT-PCR, enzyme-linked immunosorbent assay (ELISA), magnetic immunoassay (MIA), flow cytometry, microarrays, ligase chain reaction (LCR), transcription-mediated amplification (TMA), strand displacement amplification (SDA) and nucleic acid sequence based amplification (NASBA) or any such methods known in the art.
  • the method comprises the determination of the expression profile of a cancer and/or normal sample having probes to a specific set of genes or proteins.
  • the level of expression can be determined with a ship comprising a set of primers or probes specific for the set of genes.
  • Expression levels obtained from cancer and normal samples may be normalized by using expression levels of proteins which are known to have stable expression such as RPLPO (acidic ribosomal phosphoprotein PO), TBP (TATA box binding protein), GAPDH (glyceraldehyde 3-phosphate dehydrogenase) or b-actin.
  • mRNA expression levels of the gene Based on the mRNA expression levels of the gene, it can be assessed (i) which genes are overexpressed in the cancer sample in comparison to the histologically matched normal sample; ii) which genes are expressed at a similar level in the cancer sample in comparison to the normal histologically matched sample; and iii) which genes are underexpressed in the tumor sample in comparison to the normal histologically matched sample.
  • This difference of expression between the normal and tumor sample allows defining a mRNA fold change of the expression of genes between Tumor versus Normal (Fc TvN).
  • the Fc TvN is the average or arithmetic mean of the fold changes of the genes belonging to the group of genes.
  • group 1) consists of PD-1, PD-L1 and PD-L2
  • the Fc TvN is the average or arithmetic mean of the sum of Fc TvN(PD-l), Fc TvN(PD- Ll) and Fc TvN(PD-L2).
  • group 1) consists of PD-1 and PD-L1
  • the Fc TvN is the average or arithmetic mean of the sum of Fc TvN(PD-l) and Fc TvN(PD-Ll).
  • group 2) consists of CTLA-4 and CD28
  • the Fc TvN is the average or arithmetic mean of the sum of FcTvN(CTLA-4) and Fc TvN(CD28).
  • the group of genes consists of one gene, obviously, there is no need of average or arithmetic mean for the gene. Then, when there is only one gene, the Fc TvN of the gene is taken into consideration in the method.
  • a gene is overexpressed when the fold change between the tumor sample and the histologically matched normal sample is higher than 1.3, a gene is expressed at a similar level when the fold change is between -1.3 and 1.3, and a gene is underexpressed when the fold change is lower than -1.3.
  • different threshold of fold change may also be used, for instance a first class with a fold change higher than x, a second class with a fold change is between -x and x, and a third class with a fold change lower than -x, x being a number between 1 and 5, preferably between 1 and 4, between 1 and 3 or between 1 and 2.
  • x could be 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9 or 2. 3.
  • the Fc TvN for a gene or for a group of genes only comprises genes having an absolute value of fold change of 1.3 or more (i.e., Fc TvN or mean Fc TvN of 1.3 or more or of - 1.3 or less). More particularly, when a group of genes is considered, only the genes having an absolute value of fold change of
  • Fc TvN 1.3 or more (i.e., Fc TvN of 1.3 or more or of - 1.3 or less) are used to calculate the mean Fc TvN.
  • a score of 0 is attributed to the gene. This means that drug targeting such gene is not the best appropriate option to treat the subject.
  • the Fc TvN for a gene or for a group of genes only comprises genes having a fold change of
  • 1.3 or more are used to calculate the mean Fc TvN.
  • the method is thus limited to genes that are overexpressed.
  • Up regulation (or overexpression) and down regulation (or under-expression) are relative terms meaning that a detectable difference, beyond the contribution of noise in the system used to measure it, may be found in the amount of expression of genes relative to a baseline.
  • a baseline expression level may be measured from the amount of mRNA for a particular genetic marker in a normal cell or other standard cell (i.e.
  • genes which are known to have stable expression for example such as RPLPO (acidic ribosomal phosphoprotein PO), TBP (TATA box binding protein), GAPDH (glyceraldehyde 3-phosphate dehydrogenase) or b-actin.
  • RPLPO acidic ribosomal phosphoprotein PO
  • TBP TATA box binding protein
  • GAPDH glycosyl transfer protein dehydrogenase
  • the mRNA fold change of a gene can be corrected by considering the expression of the miRNA of the gene in order to adjust possible miRNA intervention in translation.
  • the mRNA fold change of a gene can be corrected by considering the expression of the miRNA of the gene in order to adjust possible miRNA intervention in translation. More preferably, a mean miRNAs fold change for each gene is calculated as the average of the miRNA fold changes between the tumor sample and the normal histologically matched sample for the gene. Then, a corrected mRNA fold change is calculated by dividing the mRNA fold change between the tumor sample and the normal histologically matched sample of the gene (mRNA TvN fold change) by the mean fold change for the miRNAs of the gene (mean miRNA TvN fold change), and the corrected mRNA fold change of the gene is then used in the method for classifying the genes into the three classes.
  • Levels of miRNAs for the genes are determined in the tumor and normal samples.
  • the miRNAs most likely to be involved in the gene expression regulation can be determined by using Target scan ⁇ www.targetscan.org/ ⁇ .
  • the method for measuring miRNA are well-known in the art.
  • the mRNA expression level in the tumor sample and in the normal histologically matched sample is further corrected with the expression of miRNA targeting the transcript of the gene.
  • Another information that will be used in the method of the present invention are the intensity of the mRNA expression in tumour and in histological matched normal tissue from the same patients.
  • the intensity can be assessed by measuring the signal that can be detected using of the microarrays technologies that enable to assess the Relative Fluorescent Units, whose value correlates with the steady state level of the mRNA (or microRNA). Detection can be performed also by RNAseq technologies (such as Next generation sequencing) and the intensities are assessed by the counts of the number of reads (tag), which also correlates with the steady state levels of the mRNA studies. Globally, technologies used enable to identify and measure the intensities/expression levels of all the types of mRNA (miRNA).
  • the intensity measurements may be equated (transformed) to the degree of expression of the gene corresponding to the signal intensity of labeled cDNA or cRNA.
  • the method according to the invention may detect the variability in expression by detecting differences in mRNA levels in cancerous tissue over normal tissue or standard intensities.
  • the method further comprises the multiplication of the Fc TvN by expression intensity of the gene (I), either in the tumor sample (I T) or in the histologically matched normal sample (I N).
  • the expression intensity of each gene is measured as relative fluorescence unit (RFU).
  • Distinctions between expression of a genetic marker in normal sample versus cancerous sample may be made through the use of mathematical/statistical values that are related to each other. For example, in some embodiments, distinctions may be derived from a mean signal indicative of gene expression in a normal sample and variation from this mean signal may be interpreted as being indicative of cancerous tissue. In other embodiments, distinctions may be made by use of the mean signal ratios between different groups of readings, i.e. intensity measurements, and the standard deviations of the signal ratio measurements. A great number of such mathematical/statistical values can be used in their place such as return at a given percentile. These values can then be used to determine whether a cancer or tumor will likely respond to a treatment to an immune checkpoint blockade therapy and determine the optimal immune checkpoint blockade therapy to administer to the patient.
  • the method comprises a normalization step.
  • Such normalization step may be performed using a calibrator consisting of FcTvN of a set of subjects. Normalization of FcTvN means adjusting values measured on different scales to a notionally common scale, often prior to averaging. It is thus performed for a single patient in comparison to a group of patients.
  • This information can be provided by a group of patients having a cancer and receiving, having received and planned to receive similar or the same immune checkpoint blockade therapy.
  • the group of patients may include 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 150 or 160 patients or more.
  • similar immune checkpoint blockade therapy it is intended that the immune checkpoint blockade therapy has the same or similar target, i.e., i) PD-1/PD-L1, PD-L2, ii) CTLA-4/CD80,CD86,CD28, iii) LAG3 or iv) TLR4 or (i) PD-1, ii) PD-L1, iii) PD-L2, iv) CTLA-4, v) CD80 vi) CD86 vii) CD28.
  • similar immune checkpoint blockade therapy can be different antibodies (e.g. Nivolumab or Pidilizumab) directed against the same immune checkpoint (e.g., PD-1).
  • the anti-cancer therapy is the same molecule, inhibitor or antibody (e.g. pembrolizumab).
  • Anti-cancer therapies are particular provided here below under the paragraph “immune checkpoint blockade therapy”.
  • the patients of the group of patients used as a calibrator may have any type of cancer or any type of solid tumors.
  • the patients may have the same type of cancers.
  • the patients may have the same cancer.
  • the patients may have various therapeutic histories.
  • the patients may have received the same number of therapeutic lines, or even the same therapeutic lines.
  • selection of the patients as calibrator can be patients with available transcriptomics data and clinical outcome (PFS) under treatment with each drug available can be assessed using data from publicly available clinical trial, such as found in clinicaltrials.gov, for example. Patients can be retrieved from the same or different clinical trials, so long they have been receiving the same or similar anti-cancer treatment.
  • PFS clinical outcome
  • the normalization step is in the form of deciles.
  • decile refers to the values that split the population data into ten equal fragments such that each fragment is representative of 1/lOth of the population.
  • each successive decile corresponds to an increase of 10% points such that the 1st decile or D1 has 10% of the observations below it, then 2nd decile or D2 has 20% of the observations below it, and so on so forth.
  • the method according to the invention comprises the attribution of a score for each gene or groups of genes.
  • the method according to the invention comprises the attribution of a score for each gene when the group includes only one gene or groups of genes when the group includes several genes.
  • the score is from 1 to 10, with 10 being the highest Fc TvN and 1 the lowest one.
  • a score superior to 4 is predictive of an efficacy of an immune checkpoint blockade therapy for treating a cancer in the subject or of a susceptibility of the subject to benefit from an immune checkpoint blockade therapy.
  • a score lower to 4 s predictive of an inefficacy of an immune checkpoint blockade therapy for treating a cancer in the subject or of a susceptibility of the subject to have no benefit from an immune checkpoint blockade therapy.
  • Group of genes or gene with the highest score (and not below a score of 4) are considered activated in the tumor of the patient and the corresponding immune checkpoint therapy, targeting such genes or group of genes is recommended. If more than one gene or group of genes meets this criterion, a combination of immune oncology treatment is considered as a potential therapy of interest in comparison to a single therapy (i.e. bitherapy or tritherapy). Accordingly, the group of gene(s) or the gene ranked first is combined with the group of gene(s) or gene ranked second if they both have a score of at least 4. This allows selecting the immune checkpoint therapy that targets these genes and groups of genes as the optimal therapy for the patient.
  • the method according to the invention may further comprise the selection of the optimal immune checkpoint blockade therapy based on the ranked genes or group of genes, preferably the one two or three group(s) of genes having the highest scores. It can also comprise a step of administering a therapeutic amount of the optimal immune checkpoint blockade therapy to patient.
  • the method may further comprise a step of selecting a patient susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy. It can also comprise a step of administering a therapeutic amount of the optimal immune checkpoint blockade therapy to the selected patient.
  • the method may also or alternatively comprise a step of selecting a patient who is not susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy or is a non-responder. Then, the selected patient will not be suitable to receive a therapeutic benefit of a treatment with an immune checkpoint blockade therapy because he/she would be a non-responder or because the treatment will likely be associated with adverse side effects.
  • the patient is an animal, preferably a mammal, even more preferably a human.
  • the patient can also be a non-human animal, in particular mammals such as dogs, cats, horses, cows, pigs, sheep, donkeys, rabbits, ferrets, gerbils, hamsters, chinchillas, rats, mice, guinea pigs and non-human primates, among others, that are in need of treatment.
  • the human patient according to the invention may be a human at the prenatal stage, a new-born, a child, an infant, an adolescent or an adult, in particular an adult of at least 30 years old or at least 40 years old, preferably an adult of at least 50 years old, still more preferably an adult of at least 60 years old, even more preferably an adult of at least 70 years old.
  • the patient has been diagnosed with a cancer.
  • the patient suffers from a metastatic cancer or a cancer at an advanced stage.
  • the patient has been diagnosed with a cancer of stage III or IV.
  • the patient suffers from an advanced solid tumor.
  • the amount of immune checkpoint inhibitor therapy to be administered is determined by standard procedure well known by those of ordinary skills in the art. Physiological data of the patient (e.g. age, size, weight, and physical general condition) and the routes of administration are taken into account to determine the appropriate dosage, so as a therapeutically effective amount will be administered to the patient. "An effective amount” or a “therapeutic effective amount” as used herein refers to the amount of active agent required to confer therapeutic effect on the subject, either alone or in combination with one or more other active agents, e.g. the amount of active agent that is needed to treat the targeted disease or disorder, or to produce the desired effect.
  • the “effective amount” will vary depending on the agent(s), the disease and its severity, the characteristics of the subject to be treated including age, physical condition, size, gender and weight, the duration of the treatment, the nature of concurrent therapy (if any), the specific route of administration and like factors within the knowledge and expertise of the health practitioner. These factors are well known to those of ordinary skill in the art and can be addressed with no more than routine experimentation. It is generally preferred that a maximum dose of the individual components or combinations thereof be used, that is, the highest safe dose according to sound medical judgment.
  • the immune checkpoint inhibitor therapy may be administered as a single dose or in multiple doses.
  • the treatment starts no longer than a month, preferably no longer than a week, after the determination of the optimal immune checkpoint inhibitor therapy for the patient suffering from cancer.
  • the immune checkpoint inhibitor therapy is administered regularly, preferably between every day and every month, more preferably between every day and every two weeks, even more preferably between every day and every week.
  • the duration of treatment is preferably comprised between 1 day and 24 weeks, more preferably between 1 day and 10 weeks, even more preferably between 1 day and 4 weeks. In a particular embodiment, the treatment last as long as the cancer persists.
  • the method of the invention is aimed to select and/or treat a patient affected with a tumor.
  • the tumor is from a cancer selected from the group consisting of leukemias, seminomas, melanomas, teratomas, lymphomas, non-Hodgkin lymphoma, neuroblastomas, gliomas, adenocarninoma, mesothelioma (including pleural mesothelioma, peritoneal mesothelioma, pericardial mesothelioma and end stage mesothelioma), rectal cancer, endometrial cancer, thyroid cancer (including papillary thyroid carcinoma, 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, basal cell carcinoma, squamous cell carcinoma,
  • the cancer is selected from the group consisting of head and neck (HN) cancer, Lung cancer, colorectal cancer (CRC), esophagus cancer, gastrointestinal (Gl) cancer; neuroendocrine (NE) cancer; non small cell lung carcinomas (NSCLC), bladder cancer, breast cancer, hepatocarcinoma, kidney cancer, leyomiosarcoma, liposarcoma (LS), lymphoma, melanoma, soft tissue cancer and rhabdomyosarcoma.
  • HN head and neck
  • CRC colorectal cancer
  • NE neuroendocrine
  • NSCLC non small cell lung carcinomas
  • bladder cancer breast cancer, hepatocarcinoma, kidney cancer, leyomiosarcoma, liposarcoma (LS), lymphoma, melanoma, soft tissue cancer and rhabdomyosarcoma.
  • the methods according to the invention comprise a step of characterizing a tumor sample of a patient in comparison with a normal sample from the same patient. Therefore, the methods according to the invention may comprise an initial step of providing samples from the patient.
  • the sample can, for example, be obtained from a subject by, but not limited to, venipuncture, excretion, biopsy, needle aspirate, lavage sample, scraping, surgical incision, colonoscopy, fibroscopy, endoscopy, surgery or, any combination thereof, and the like.
  • the tumor sample and the normal sample provides from the same type of tissue. More particularly, the tumor and normal samples are histologically matched tissues. In one embodiment, the cancer sample and the normal sample are from the same patient.
  • the tumour sample and the normal sample provides from the same type of tissue.
  • the tumor and normal samples are histologically matched tissues.
  • Tumor tissue is a fragment obtained from the tumor or metastatic lesions, (usually provided in interventional radiology) and containing at least 50% tumoral cells, immune infiltrating cells, stromal cells, vessels.
  • the normal tissue is a fragment from histologically matched normal tissue (usually provided in fibroscopy or endoscopy units) and containing at least 30% normal cells (e.g., epithelial cells).
  • RNA and total RNA preparations are performed and only high- quality nucleic acids quality are used for transcriptomics investigations (measure of differential expression between the tumor and normal tissues.
  • the "normal" sample preferably does not comprise any cancer cell.
  • pairs of tumors with corresponding histological normal tissue are the followings:
  • lung cancer adenocarcinomas or derived metastases - bronchial normal mucosa
  • breast cancer tumors or derived metastases normal epithelial breast cells
  • colon cancers adenocarcinomas or derived metastases - normal colon or rectal mucosa
  • kidney cancers or derived metastases normal kidney cells
  • liver carcinomas or derived metastases normal liver cells
  • ORL Oral-pharyngeals tumors
  • pancreatic cancers normal parenchimatous tissue from pancreas
  • the method further comprises isolating genetic material from the cancer and the histologically matched normal samples.
  • SIMS score intensity plots for 160 patients of the WINTHER trial showing the variability of the importance of key 10 genes depending upon cancer histology and pointing out to the importance of LAG-3 and TLR-4 for some cancer types in particular.
  • Z. axis shows SIMS score from 1 to 10 for each group of genes: C. PDL-1/PDL-2/PD-1, D. CTLA-4, E. LAG-3, F. TLR-4.
  • a color code indicates the level of activation.
  • Each type of cancer is represented in 3D plot, with the symbols described; Y.
  • axis shows the intensity of mRNA expression component (in relative fluorescent unit) from the SIMS scores of the tumor tissue compared to the analogous normal tissue of each patient.
  • X axis patients ranked.
  • Figure 2 Distribution of the recommended targets of the 10 treatments for all WINTHER trial 160 patients explored in silico. Recommended therapy for the 160 patients of the WINTHER trial based on the individual SIMS score for each of A. the four group of genes (PDL-1, CTLA-4, LAG-3, TLR-4); B. other genes ⁇ VISTA, TIM- 3, TIGIT, ICOS, 0X40 and IDOl). A treatment was selected when the SIMS score of activation of an gene or group of genes was 4 or above.
  • PDL1 SIMS scores 8, 9 and 10 correspond to the group PDL1>50%.
  • PDL1 SIMS scores >4 correspond to PDL1>% group.
  • Figure 5 In silico modeling of CYTISCAPE based on SIMS scoring of 32 metastatic NSCLC. Legend: Distribution of transcriptomic profiles indicating activation of PDL1 (ranked from lowest to highest activation) and display of TGFB co-activation for each of the 32 patients with metastatic NSCLC from WINTHER dataset.
  • Figure 6 Predicted efficacy of 10 in metastatic NSCLC - Best predicted options for combinations with anti- PDLl.
  • Distribution of transcriptomic profiles indicating activation of PDL-1 (ranked from lowest to highest activation) and displaying the co-activation of CTLA-4, IDO-1, TIGIT, LAG-3, 0X40, TIM-3, TGFB1, ICOS and TLR-4 for each of the patients with metastatic NSCLC (n 32 patients) from the WINTHER dataset.
  • Y axis Log2 (PDL-1 -or other) genes Fold change tumor versus normal;
  • X axis patients ranked in the order of increased PDL-1 overexpression. Each gene is presented with symbols and colors.
  • the WINTHER database includes transcriptomic and genomic data of 160 patients with advanced cancers such as non-small-cell lung carcinoma (NSCLC), colorectal cancer (CRC), Head and Neck (HN) and others (breast, hepatocellular cancer, bladder, rhabomyosarcoma, melanoma, liposarcoma, lymphoma, kidney, Gl, etc.), who progressed under standard treatment.
  • NSCLC non-small-cell lung carcinoma
  • CRC colorectal cancer
  • HN Head and Neck
  • the dataset used in the in-silico analysis consists in RNA-Seq data generated from tumor and analogous organ matched normal tissues from each patient.
  • the normal tissue of origin of the tumor was biopsied.
  • the matched normal tissue was the rectal mucosa obtained separately by rectal endoscopy.
  • the inventors defined immune oncology groups of genes, which are composed of common markers associated with available immune oncology therapies. Such groups of genes are also called intervention or interventional points.
  • Each immune oncology group of genes used in the study consists of both the therapy targets and the genes upstream of the targets, while together they can shine a light on the biological activity of the group of genes.
  • Four different groups of genes were defined as described in Table 4.
  • SIMS Score The SIMS score ranks the activation level of the groups of genes on a scale of 1 to 10 based on calculation which integrates genomics and transcriptomics generated from tumor and analogous organ matched normal tissues. For the assessment of 10 genes and groups of genes no genomic data was used due to the absence of relevant oncogenic events (mutations) for the related genes. Following the SIMS algorithm (Lazar V et a I., Oncotarget. 2015), the fold change values for all patients were calculated based on the ratio between mRNA expression in the tumor samples vs. the normal samples. Only target genes with log based 2 mRNA expression fold change values equals or higher than a threshold of 1 were considered and included in the SIMS algorithm.
  • the average or arithmetic mean of the target genes that attained the threshold was calculated as follows: for each group of gene(s), mRNA steady state level in tumor vs. normal was used to calculate a mean fold change of the pathway/point. Averaging was performed on the values of individual fold change (Fc) of tumor vs. normal for each gene belonging to the groups of gene(s). For calculating the mean/average fold change of a group of gene(s) k, denoted as E k , the fold changes of differentially expressed genes with a fold change of at least 1.3 are used. Based on Agilent microarrays specifications, the threshold of 1.3 was considered as the lowest conferring accurate detection, since all Fc values were obtained by combining two dye swap microarray experiments. In other words, for each group of gene(s), an average fold-change of the genes / of the group of genes k is calculated, trimming values with a threshold of ⁇ 1.3.
  • E k is the average of the fold change of the genes m*.
  • m k [i ⁇ i E M k and IF > 1.3 ⁇ .
  • the fold change for a particular group of genes is the average or arithmetic mean of the fold changes of genes belonging to the group of gene(s) as defined in Table 6 and having a fold change T vs. N of 1.3 or more.
  • the SIMS scores was calculated by applying a rank normalization step in the form of deciles, while the calibrator was composed of all 160 patients. Each patient was then matched to scores of 1 to 10 according to the decile to which the mRNA fold change expression is attributed. Scores amongst patients is comparable since all patients included in the calibrator.
  • the SIMS scores for the immune oncology groups of gene(s) were reviewed individually patient by patient and an assessment of a potential single or combined immune oncology treatment that can benefit a patient was assessed using consistent methodology. Groups of gene(s) with the highest SIMS score (and not below a score of 4) were considered activated and the corresponding 10 therapy recommended. If more than one gene or group of gene(s) met this criterion, a combination of immune oncology treatment was considered as a potential therapy rather than a single therapy. Immune oncology therapies targeting gene or group of gene(s) with scores lower than 4 were considered less likely to benefit the patient. In pathologies where only few patients (1 or 2) were identified to benefit from a single or combined immune oncology therapy, the patients were included in the closest therapy group from which a benefit can still be derived.
  • the inventors investigated the level of activation of PD-1, CTLA-4, LAG-3, TLR-4, VISTA, TIM-3, TIGIT, ICOS, 0X40 and IDOl by comparing the differential gene expression between the tumor and the analogous normal tissues for each patient enrolled in the WINTHER trial.
  • the inventors determined retrospectively insilico an activation score of the molecular pathways based on the Simplified Interventional Mapping System (SIMS) algorithm for 160 patients with a variety of advanced/metastatic cancers (non-small-cell lung carcinomas (NSCLC), head & neck adenocarcinomas (HN), colorectal carcinomas (CRC), etc.) from the WINTHER trial for whom the transcriptomic data was available (these patients were not treated with the selected 10 therapies).
  • the inventors also analyzed retrospectively gene expression data obtained from 123 patients with primary resected NSCLC from the CHEMORES study, for whom transcriptomic information from tumor and normal lung tissues obtained from the resection specimens was also available.
  • Figure 1 presents the distribution of the PDL-1 expression in tumor compared with normal tissues in 32 patients with metastatic NSCLC ( Figure 1A) and 123 patients with resected NSCLC ( Figure IB).
  • the percentage of patients with the gene expression-based SIMS score activated (>4) for PDL-1 is about 60%, which is in line with real world data for PDL-1 >1% expression by immunohistochemistry (IHC).
  • IHC immunohistochemistry
  • Figures 2C, 2D, 2E and 2F present the distribution of SIMS scores that allows to identify specific profiles of patients that match each potential 10 therapies, as summarized in Table 3, Figure 2 and Table 6.
  • Figure 1C shows the distribution of the 4 main groups of gene(s): i) PD-l/PDL-1/2, ii) CTLA-4, iii) LAG-3 and iv) TLR-4 (both expressed as SIMS scores and as transcriptomic expression) in the cohort of 160 patients with advanced metastatic disease including 56 with CRC, 32 with NSCLC, 26 with HN and 41 patients with other types of solid tumors (including kidney, breast, neuroendocrine, gastric, esophagus and liver tumors).
  • anti-PD-1 would only be recommended in 6% of metastatic NSCLC and 3% of primary NSCLC. In contrast, used in combination, anti-PD-1 would be recommended in 66% of metastatic NSCLC and 27% of primary NSCLC.
  • the new targets LAG-3 and TLR-4 that could lead to monotherapies, display very different distributions in primary or advanced NSCLC.
  • anti-LAG-3 monotherapy could be proposed to treat 9% of primary NSCLC but not patients with advanced disease.
  • anti-TLR-4 monotherapy could potentially benefit 6% of advanced NSCLC but only 1% of patients with primary disease.
  • Figure 2B shows the profile of activation of the other 10 targets: VISTA, TIM-3, TIGIT, ICOS, 0X40 and IDOl, that could represent further therapeutic options, alone or in combination with anti-PD-l/PDL-1.
  • Patients who are expected to benefit from monotherapies represent a minority, with sub-cohorts not exceeding 5%.
  • combination therapies would potentially benefit an important fraction of patients: co-activation of PD-l/PDL-1 and TIM-3 was observed in 25% of patients in the WINTHER cohort (160 patients with various types of metastatic solid tumors) and co-activation of PD-l/PDL-1 and IDOl was observed in 25%.
  • the fraction of patients that would benefit from a 10 therapy based on anti-PDL-1 and anti-TIGIT represents respectively 16% of all metastatic NSCLC and 26% of PDL-1 positive (>1%) patients with metastatic NSCLC.
  • Figure 4 presents a detailed investigation, patient by patient, of the co-activation of PDL1 and TIGIT targets.
  • the Table presents the most prevalent therapeutic (i) combination for patients with PDL-1 SIMS > 4 with an anti-PD-l/PDL-1 (backbone of the combinations) associated with one of the inhibitors of co-activated 10 targets. 37
  • Table 5 Distribution of the recommended 10 options for: A. Treatment of patients with advanced metastatic solid tumors; B. Neo-adjuvant (or adjuvant) treatment of patients with primary NSCLC tumors.
  • CRC colorectal carcinomas
  • NSCLC non-small-cell lung carcinomas, HN heac and neck carcinomas
  • ADC adenocarcinomas
  • SCC squamous cell carcinomas
  • TMB T lymphocytes
  • TILS T lymphocytes
  • TILS T lymphocytes
  • the clone of LyT that recognizes the neo-antigen is activated and proliferates.
  • the recruitment of activated LyT that recognize the tumor is a complex process that involves different antigen presentation mechanisms.
  • Antigen Presenting Cells present the neo-antigen associated to the major histocompatibility complex II (CMH2) recognized by LyT CD4+ that differentiate in LyT Helper 1 (Ly Thl) and Helper 2 (Ly Th2). Ly Thl are key in recruitment of naive LyT CD8+ and induce their activation.
  • CytotoxicT Lymphocytes (CD8+) and Natural Killer cells (NK) also recognize the neo-antigen restricted to CMH1 (Histocompatibility complex 1) and are subsequently activated, and can directly destroy tumor cells presenting the neo-antigen.
  • the biomarker strategy was based on the SIMS scoring.
  • SIMS is an algorithm (i) taking into consideration genomic alterations of the tumor and (ii) assessing the difference between gene expression levels in the tumor compared to expression levels in the analogous normal organ tissues.
  • a dual biopsy of the tumor tissue and the analogous normal corresponding tissue is therefore necessary.
  • the WINTHER trial demonstrated the feasibility of this dual biopsy approach that was well accepted by patients without significant comorbidity [20].
  • the analogous normal tissue biopsy consisted in normal bronchial mucosa obtained with a bronchoscopy; for colorectal cancer, the normal tissue was colonic mucosa obtained by endoscopy (see Material and Methods).
  • This SIMS biomarker strategy enabled assessment of each of the selected 10 targets (PDL-1, CTLA-4, LAG-3, TLR-4) individually, in order to compare and select the more highly activated targets to be treated with single agents or combinations.
  • the phase II CITYSCAPE trial - NCT03563716 (https://clinicaltrials.gov/ct2/show/NCT03563716), was the first randomized study evaluating the efficacy and safety of tiragolumab (anti-TIGIT) plus atezolizumab (anti-PDL- 1) compared with atezolizumab alone as an initial (first-line) treatment for patients with PDL-l-positive metastatic NSCLC.
  • the trial reported positive results with both primary endpoints met at the 6 months evaluation in the intention-to treat (ITT) population with PDL1>1%: improvement in the objective response rate (ORR) (31.3% vs.
  • Figure 3C shows that in the 60% of metastatic NSCLC with SIMS score for PDL-1 activated ( ⁇ 4), corresponding to the intention-to-treat population (ITT) in the CYTISCAPE trial, the fraction of patients with a profile with co activation of TIG IT and PD-l/PDL-1 target is 26 % which aligns with the findings of the CYTISCAPE trial.
  • ITT intention-to-treat population
  • immunotherapy including but not limited to CD137 or 0X40 agonists, anti-CTLA-4, anti-PD-1, or anti-PD-Ll, anti-PD-L2 antibody or pathway-targeting agents.
  • NSCLC advanced solid tumors
  • a program for testing hypothesis prospectively would be of major interest for patients around the world, in particular those who have exhausted all therapeutic options.
  • the invention offers an existing platform for such endeavor that will require cooperation between academic and pharmaceutical industries. Such studies will enable to compare the real impact of the different biomarker strategies, and inform the most adequate decision support tools to help physicians for a rational treatment of cancer patients.
  • Table 6 SIMS score allocation to 10 therapy options for 160 patients with advanced metastatic cancers I

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Abstract

The present invention relates to methods determining the optimal immune checkpoint blockade therapy that will benefit to a subject suffering from cancer.

Description

Method to select the optimal Immune checkpoint therapies Field of the Invention
The present invention relates to the field on oncology, especially to personalized medicine in cancer therapy.
Background of the Invention
Immunotherapy represents today a major field of interest in the treatment of cancers. Indeed, leverage of the negative immune checkpoint blockade is able to induce durable responses across multiple types of cancers. The most advanced knowledge has been generated around the therapies targeting PD-1/PD-L1 or CTLA-4, and to a less extent anti-LAG3 or anti-TLR4. However, overall, only a fraction of patients has a therapeutic benefit of treatments targeting the immune checkpoint blockade, more specifically less than 25% of them, and only some have responses with long duration. Indeed, most patients do not respond or, after an initial response, develop resistance. Many patients have also important toxicity problems due to the treatment. Therefore, identifying the optimal treatment that would lead to a clinical benefit remains an important unmet need in the field.
Several biomarker strategies have been validated or proposed in order to identify patients most or least likely to benefit from anti-PD-l/PDL-1 and anti-CTLA-4 therapies. For example, detection of high microsatellite instability (MSI-H) in a tumor is associated with higher likelihood of clinical benefit. However, no more than ~3% of patients with solid tumors have MSI-H status. Similarly, tumor mutation burden (TMB) or PDL-1 (programmed death-ligand) expression by immunohistochemistry have shown correlations with outcome. High TMB correlates with a higher likelihood of expression of neoantigens thus triggering infiltrating effector T-cells through a complex mechanism involving interaction of different immune cells (APC, LyTCD4+ Helper 1, LyTCD8+, NK, Tregs). The activation of the T cell receptor (TCR) induces the selection and proliferation of cytotoxic T cells (LyT), a process that is strictly controlled by PD-1 which blocks this activation after engaging PDL-1 or PDL-2. CTLA-4 blocks another activator of TCR, CD28, by competitive binding of B7-1 and B7-2 ligands, thereby contributing to the negative blockade of LyTCD8+. Leveraging this negative immune-blockade led to development of effective Immuno-oncology (10) treatments using anti-PD-1 and anti-CTLA-4 antibodies.
However, other important pathways are involved in the control of activation of the immune system. LAG-3 delivers inhibitory signals upon binding to ligands, such as FGL1 (responsible for LAG-3 T-cell inhibitory function). Following TCR engagement, LAG-3 associates with CD3-TCR and directly inhibits T-cell activation. LAG-3 negatively regulates the proliferation, activation, effector function and homeostasis of both CD8(+) and CD4(+) T-cells. It also mediates immune tolerance. LAG-3 is constitutively expressed on a subset of regulatory T-cells (Tregs) and contributes to their suppressive function.
Overexpression of TLR-4 and activation by lipopolysaccharide (LPS) activates MAPK and NF-KB pathways, implicating cell-autonomous TLR-4 signaling in regulation of carcinogenesis, in particular, through increased proliferation of tumor cells, apoptosis inhibition and metastasis. TLR-4 signaling in immune and inflammatory cells of the tumor microenvironment may lead to the production of pro-inflammatory cytokines (TNF, IL-Ib, IL-6, IL-18, etc.), immunosuppressive cytokines (IL-10, TGF-b, etc.) and angiogenic mediators (VEGF, EGF, TGF- b)·
Thus, the respective roles of LAG-3 and TLR-4 make them key candidates for modulation in an attempt to increase the 10 therapeutic armamentarium. Indeed, anti-LAG-3 (BMS-986016) in combination with anti-PD- 1 (nivolumab) showed activity in patients with melanoma who had relapsed or were refractory to anti-PD- 1/PDL-l therapy. In addition, an anti-TLR-4 antibody (NI-0101) was tested in a phase I first in-human study performed on healthy volunteers, as well as a phase II study in patients with Rheumatoid Arthritis, and showed a safe profile.
Other regulators of immune activity such as VISTA, TIM-3, TIGIT, TQRb, ICOS, 0X40 and IDOl, may also play an important role that should be considered. Different combinations have already been tested clinically such as anti-PD-1 combined with anti-TIM-3; anti-PD-1 combined with 0X40; anti-PD-1 combined with anti-TIGIT; anti-PD-1 combined with anti-IDOl, and other novel immune-checkpoint inhibitors. All these studies were performed without any specific biomarker strategy beyond the conventional TMB and/or PDL-1 status, which may explain the varying degrees of efficacy reported.
Unfortunately, not all tumors benefit from immune oncology (10) agents, and treatment becomes more challenging after failure of first and second line regimens. Extending the application of precision medicine therefore requires a deeper understanding of cancer biology. Improvement in the ability to select patients is needed, both in respect to identify responding versus resistant tumors and in pinpointing patients at risk for severe toxicities.
There is a persistent need to develop new strategies and to identify reliable methods and biomarkers to personalize treatment for patient suffering from cancer, thereby making anti-cancer treatments more effective for patients and increasing patient survival.
Here the inventors report a new methodology that aims to select the optimal 10 therapies among an expanded 10 therapeutic landscape. SUMMARY
Immuno-oncology (10), in particular use of immune checkpoint inhibitors, has achieved remarkable benefits for a small fraction (~20%) of patients with many types of advanced cancer. The inventors examined factors that may impact sensitivity of tumors to 10 beyond PDL-1 and CTLA-4 status to include LAG-3 and TLR-4. From analysis of data obtained from 160 patients with advanced metastatic solid tumors from WINTHER, a completed clinical trial, the inventors identified tumor transcriptomic profiles that could benefit from 10 distinct major therapeutic strategies using anti-PD-1, anti-CTLA-4, anti-LAG-3 and anti-TLR-4 antibodies, alone or in combinations. The inventors also explored the activation of VISTA, TIM-3, TGF , TIGIT, ICOS, 0X40 and IDOl in these patients. The inventors also explored 123 patients with primary non-small-cell lung cancer (NSCLC) treated by curative surgery from an observational study and identified a transcriptomic profile distinct from the group of patients with metastatic NSCLC from the WINTHER trial.
Altogether this more personalized approach could significantly increase the fraction of patients with advanced cancers that benefit from 10. The presented data suggest the importance of rational patient stratification based on a personalized biomarker strategy to guide the selection of the best 10 therapies for patients with advanced/metastatic solid tumors, or for neo-adjuvant/adjuvant therapies for patients with cancer, such as primary resectable NSCLC.
Expanding 10 therapeutic options may significantly increase the fraction of patients with clinical benefit.
The present invention concerns a method for selecting an optimal immune checkpoint blockade therapy for treating a subject suffering from cancer, wherein the method comprises:
- providing mRNA expression level in a tumor sample and a histologically matched normal sample of a set of genes comprising the following genes or groups of genes: 1) a group of genes comprising PD-1, PD-L1, and optionally PD-L2; 2) a group of genes comprising CTLA-4, CD28, and optionally CD80 and CD86; 3) LAG3; 4) TLR4 and optionally one or more of the following genes 5) VISTA, TIM-3, TIGIT, TGF , ICOS, 0X40 and IDOl; and wherein the tumor and histologically matched normal samples are from the same subject;
- determining a mRNA fold change of Tumor versus Normal (Fc TvN) for each gene of the set of genes; and calculating a mean FcTvN fold change for each group of genes;
- normalizing the Fc TvN fold changes or the mean Fc TvN fold changes for the group of genes; - ranking the normalized Fc TvN fold changes and the normalized mean Fc TvN fold changes, the highest value of the normalized Fc TvN fold changes and mean Fc TvN fold changes being ranking first and being predictive of a highest efficacy of an immune checkpoint blockade therapy for treating the cancer of the subject; and
- optionally selecting the optimal immune checkpoint blockade therapy based on the ranked genes or group of genes.
The invention also concerns a method for determining if a subject suffering from a cancer is susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy, wherein the method comprises:
- providing mRNA expression level in a tumor sample and a histologically matched normal sample of a set of genes comprising the following genes or groups of genes: 1) a group of genes comprising PD-1, PD-L1, and optionally PD-L2; 2) a group of genes comprising CTLA-4, CD28, and optionally CD80 and CD86; 3) LAG3; 4) TLR4 and optionally one or more of the following genes 5) VISTA, TIM-3, TIGIT, ICOS, 0X40 and IDOl; and wherein the tumor and histologically matched normal samples are from the same subject;
- determining a mRNA fold change of Tumor versus Normal (Fc TvN) for each gene of the set of genes; and calculating a mean Fc TvN fold change for each group of genes;
- normalizing the Fc TvN fold changes or the mean Fc TvN fold changes for the group of genes;
- ranking the normalized Fc TvN fold changes and the normalized mean Fc TvN fold changes, the highest value of the normalized Fc TvN fold changes and mean Fc TvN fold changes being ranking first and being predictive of the susceptibility of the subject to benefit from the treatment with an immune checkpoint blockade therapy; and
- optionally selecting the immune checkpoint blockade therapy most susceptible to benefit to the subject based on the ranked genes or group of genes.
In particular, the Fc TvN for a particular group of genes is the average or arithmetic mean of the fold changes of genes belonging to the group of genes and having a Fc TvN of 1.3 or more. The method may further comprise the multiplication of the Fc TvN by expression intensity of the gene (ln), either in the tumor sample (ln T) or in the histologically matched normal sample (ln N).
Preferably, the normalization step is performed using a calibrator consisting of Fc TvN for each gene or group of genes of a set of subjects. In particular, the mRNA expression level in the tumor sample and in the normal histologically matched sample is further corrected with the expression of miRNA targeting the transcript.
Particularly, mRNA expression level of other genes is studied in the method but the total number of genes is no more than 16 genes.
In an aspect, the normalization step is in the form of deciles and the score is from 1 to 10, with 10 being the highest FcTvN and 1 the lowest one. Preferably, a score superior to 4 is predictive of an efficacy of an immune checkpoint blockade therapy for treating a cancer in the subject or of a susceptibility of the subject to benefit from an immune checkpoint blockade therapy.
The method may further comprise the selection of the optimal immune checkpoint blockade therapy based on the ranked genes or group of genes, preferably the one, two or three gene(s) or group(s) of genes having the highest scores.
Preferably, the immune checkpoint blockade therapy is a monotherapy, a bitherapy or a tritherapy. In particular, the immune checkpoint blockade therapy is selected from the group consisting of an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti-CTLA-4 antibody, an anti-LAG3 antibody, an anti-TLR4 antibody and any combination thereof, and optionally one or several among an anti-OX40, an anti-IDOl, an anti-TIME3, an anti-TIGIT and an anti-VISTA. Preferably, the immune checkpoint blockade therapy is selected from the group consisting of pembrolizumab, nivolumab, atezolizumab, durvalumab, avelumab, ipilimumab, NI-0101, BMS- 986016, Sym022, GSK2831781, AZ MEDI5752, and optionally AZ MEDI6469, GSK3174998, INCB024360, MBG453, MTIG7192A, CI-8993 and tiragolumab.
Particularly, the cancer is selected from the group consisting of prostate cancer, bladder cancer, breast cancer, colon cancer, colorectal cancer, Esophagus cancer, hypopharynx cancer, gastric cancer, rectum cancer, head and neck cancer, liver cancer, brain cancer, hepatocarcinoma, kidney cancer, ovarian cancer, cervical cancer, pancreatic cancer, sarcoma, lung cancer, lymphoma, osteosarcoma, melanoma, neuroendocrine cancer, pleural cancer, Small Intestine cancer, endometrial cancer, soft tissue cancer, non small cell lung carcinomas (NSCLC), metastatic non-small cell lung cancer, muscle cancer, adrenal cancer, thyroid cancer, uterine cancer, advanced renal cell carcinoma (RCC), and sub ependymal giant cell astrocytoma (SEGA) associated with tuberous sclerosis (TS), preferably colorectal cancer, head and neck cancer, lung cancer, non-small cell lung carcinomas (NSCLC), bladder cancer, breast cancer, esophagus cancer, gastro intestinal cancer, hepatocarcinoma, kidney cancer, leyomiosarcoma, liposarcoma, lymphoma, melanoma, soft tissue cancer, neuroendocrine cancer and rhabdomyosarcoma. DETAILED DESCRIPTION OF THE INVENTION
Definitions
In order that the present invention may be more readily understood, certain terms are defined hereafter. Additional definitions are set forth throughout the detailed description.
Unless otherwise defined, all terms of art, notations and other scientific terminology used herein are intended to have the meanings commonly understood by those of skill in the art to which this invention pertains. In some cases, terms with commonly understood meanings are defined herein for clarity and/or for ready reference, and the inclusion of such definitions herein should not necessarily be construed to represent a difference over what is generally understood in the art. The techniques and procedures described or referenced herein are generally well understood and commonly employed using conventional methodologies by those skilled in the art.
The term "cancer" or "tumor", as used herein, refers to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, and/or immortality, and/or metastatic potential, and/or rapid growth and/or proliferation rate, and/or certain characteristic morphological features. This term refers to any type of malignancy (primary or metastases) in any type of subject. It may refer to solid tumor as well as hematopoietic tumor.
The term "cancer sample" or "tumor sample" refers to any sample containing tumor cells derived or retrieved from a patient or a subject. In particular, tumor cells may be obtained from fluid sample such as blood, plasma, urine and seminal fluid samples as well as from biopsies, organs, tissues or cell samples. In a preferred embodiment, tumor cells are obtained from tumor biopsy or resection sample from the patient. Cancer tissues are particularly composed of cancer cells and the surrounding cancer stromal cells, vascular endothelial cells, and immune cells, in addition to the extracellular matrix. Preferably, the sample contains only tumor cells. Preferably the cancer sample contains nucleic acids and/or proteins. Optionally, samples containing tumor cells may be treated prior to their use. As example, a tumor cell enrichment sorting may be performed. It may be fresh, frozen or fixed (e.g. formaldehyde or paraffin fixed) sample.
As used herein, the terms "anti-cancer therapy", "anti-cancer treatment" and "anticancer agents" are used interchangeably and refer to compounds which are used in the treatment of cancer, such as chemotherapeutic or immunotherapeutic compounds. Preferably, the anti-cancer therapy is an immune- oncology therapy, especially an immune checkpoint inhibitor therapy.
As used herein, the terms "subject", "individual" or "patient" are interchangeable and refer to an animal, preferably to a mammal, even more preferably to a human. However, the term "subject" can also refer to non-human animals, in particular mammals such as dogs, cats, horses, cows, pigs, sheep and non-human primates, among others.
Within the context of this invention, "responder", "responsive" or "have a therapeutic benefit" refers to a subject who responds to a treatment of cancer, for example such as the volume of the tumor is decreased, at least one of his symptoms is alleviated, or the development of the cancer is stopped, or slowed down. Typically, a subject who responds to a cancer treatment is a subject who will be completely treated (cured), i.e., a subject who will survive cancer or a patient that will survive longer. A subject who responds to a cancer treatment is also, in the sense of the present invention, a subject who has an overall survival higher than the mean overall survival known for the particular cancer, in particular in the absence of a treatment or in the presence of unsuitable treatment. It is also intended to refer to a patient who shows a good therapeutic benefit from a treatment, that is to say a longer disease-free survival, a longer overall survival, a decreased metastasis occurrence, a decreased tumor growth and/or a tumor regression in comparison to a population of patients suffering from the same cancer, in particular in the absence of a treatment.
Within the context of this invention, "non-responder" refers to a subject who does not respond to an anti cancer treatment, for example such as the volume of the tumor does not substantially decrease, or the symptoms of the cancer in the subject are not alleviated, or the cancer progresses, for example the volume of the tumor increases and/or the tumor generates local or distant metastasis. The terms "non-responder" also refers to a subject who will die from cancer, or will have an overall survival lower than the mean overall survival known for the particular cancer. By "poor responder" or "non-responder" is intended a patient who shows a weak therapeutic benefit of the treatment, that is to say a shorter disease-free survival, a shorter overall survival, an increased metastasis occurrence and/or an increased tumor growth in comparison to a population of patients suffering from the same cancer and having the same treatment.
The term "treatment" refers to any act intended to ameliorate the health status of patients such as therapy, prevention, prophylaxis and retardation of the disease or of the symptoms of the disease. It designates both a curative treatment and/or a prophylactic treatment of a disease. A curative treatment is defined as a treatment resulting in cure or a treatment alleviating, improving and/or eliminating, reducing and/or stabilizing a disease or the symptoms of a disease or the suffering that it causes directly or indirectly. A prophylactic treatment comprises both a treatment resulting in the prevention of a disease and a treatment reducing and/or delaying the progression and/or the incidence of a disease or the risk of its occurrence. In certain embodiments, such a term refers to the improvement or eradication of a disease, a disorder, an infection or symptoms associated with it. In other embodiments, this term refers to minimizing the spread or the worsening of cancers. Treatments according to the present invention do not necessarily imply 100% or complete treatment. Rather, there are varying degrees of treatment of which one of ordinary skill in the art recognizes as having a potential benefit or therapeutic effect.
As used herein, the term "diagnosis" refers to the determination as to whether a subject is likely to be affected by a cancer or to the determination of whether a subject is susceptible to benefit from a treatment. The skilled artisan often makes a diagnosis on the basis of one or more diagnosis markers, the presence, absence, or amount of which is indicative of the presence or absence of the cancer. By "diagnosis", it is also intended to refer to the provision of information useful for the diagnosis of cancer, for the prognosis of patient survival or for the determination of the response of a patient to an anti-cancer treatment.
As used herein, the term "marker" or "biomarker" refers to a measurable biological parameter that helps to predict the occurrence of a cancer or the efficiency of an anti-cancer treatment. It is in particular a measurable indicator for predicting the clinical outcome of a patient undergoing anticancer therapy or the response of a subject having cancer to an anti-cancer therapy.
By "histologically matched normal sample" or "matched normal sample" or "matched normal control" is meant herein a sample that corresponds to the same or similar organ, tissue or fluid as the cancer sample to which it is compared. For example, in the case of a breast cancer, a histologically matched normal sample can be a matching normal adjacent mammary tissue sample. Alternatively, when the tumor sample is a lung cancer, the histologically matched normal sample is for example a sample from normal bronchial mucosa. Further examples are provided here below, in particular under the "Patients and tumor" paragraph.
As used herein, the terms "clinical outcome" and "prognosis" are interchangeable and refer to the determination as to whether a subject is likely to be affected by a cancer relapse, recurrence or metastasis, or death. These terms also relate to the survival, in particular the overall survival. "Overall survival" (OS) as used herein refers to the time span from starting the treatment until cancer specific death of the patient. "Progression-free survival" (PFS) is "the length of time during and after the treatment of a disease, such as cancer, that a patient lives with the disease but it does not get worse".
The term "and/or" as used herein is to be taken as specific disclosure of each of the two specified features or components with or without the other. For example, "A and/or B" is to be taken as specific disclosure of each of (i) A, (ii) B and (iii) A and B, just as if each is set out individually.
The term "a" or "an" can refer to one of or a plurality of the elements it modifies (e.g., "a reagent" can mean one or more reagents) unless it is contextually clear either one of the elements or more than one of the elements is described. Genes and Groups of genes
The methods disclosed herein rely on the study of gene expression, in particular in a set of genes. In particular, the method according to the invention comprises providing expression level in a tumor sample and a histologically matched normal sample of a set of genes comprising the following genes or groups of genes: 1) a group of genes comprising PD-1, PD-L1, and optionally PD-L2; 2) a group of genes comprising CTLA-4 and CD28, and optionally CD80 and CD86; 3) LAG3; 4) TLR4, and optionally one or more of the following genes 5) VISTA, TIM-3, TIG IT, TGF , ICOS, 0X40 and IDOl.
In a first group of genes, are included genes useful for assessing the responsiveness of a patient to an immune checkpoint blockade therapy, in particular a therapy targeting PD-1/PD-L1. This group of genes includes PD- 1, PD-L1 and PD-L2. This group can be referred as group 1. The role of PD-1, PD-L1 and PD-L2 is well-known.
PD-1 negatively regulates T cell activation through interaction with PD-L1 and PD-L2. The therapy targeting PD-1/PD-L1 aims to block the interaction between PD-1 and PD-L1 and/or PD-L2 so as to remove this blockade.
As used herein, the terms "PD1" , "PD-1", "PCDC1", "Programmed cell death 1", and "CD279" are used interchangeably and refer to the human PDCD1 gene, for example such as described under the Uniprot reference: Q15116 or GeneCard ID: GC02M241849.
As used herein, the terms “PDL1" , "PD-L1", "PCDCL1", "PD-l-ligand 1", "Programmed cell death 1 ligand 1", and "CD274" are used interchangeably and refer to the human CD274 gene, for example such as described under the Uniprot reference: Q9NZQ7 or Gene ID: 29126. As used herein, the terms "PDL2", "PD-l-ligand 2", "Programmed Cell Death 1 Ligand 2" are used interchangeably and refer to the human PDL2 gene, for example such as described under the Uniprot reference: Q2LC89 or GeneCard ID: GC09P005510.
In a second group of genes, are included genes useful for assessing the responsiveness of a patient to an immune checkpoint blockade therapy, especially a therapy targeting CTLA-4. This set of genes includes CTLA- 4 and CD28 and may optionally comprise CD80 and CD86. This group can be referred as group 2. Briefly, CTLA-
4 is immediately upregulated following TCR engagement. CTLA-4 negatively regulates TCR signaling through competition with the costimulating CD28 for the binding of CD80/CD86 for which CTLA-4 has a higher affinity and avidity. The therapy targeting CTLA-4 aims to block the interaction between CTLA-4 and CD80 or CD86 so as to remove this blockade. As used herein, the terms “CTLA4", "Cytotoxic T-Lymphocyte Associated Protein 4" and "CD152" are used interchangeably and refer to the human CTLA4 gene, for example such as described under the Uniprot reference: P16410 or GeneCard ID: GC02P203867.
As used herein, the terms "CD28", "T-Cell-Specific Surface Glycoprotein CD28" and "TP44" are used interchangeably and refer to the human CD28 gene, for example such as described under the Uniprot reference: P10747 or GeneCard ID: GC02P203706.
As used herein, the terms "CD80", "T-Lymphocyte Activation Antigen CD80", "CTLA-4 Counter-Receptor B7.1 » and "B7-1" are used interchangeably and refer to the human CD80 gene, for example such as described under the Uniprot reference: P33681 or GeneCard ID: GC03M119524. As used herein, the terms " CD86 ", "T-Cell-Specific Surface Glycoprotein CD28" and "TP44" are used interchangeably and refer to the human CD86 gene, for example such as described under the Uniprot reference: P42081or GeneCard ID: GC03P122055.
As CTLA-4 and PD-1 act at least in part through a similar molecular mechanism of attenuating CD28-mediated costimulation, it seems important to provide in the method information about the expression of genes relating to PD-1 and those relating to CTLA-4 (i.e., groups 1 and 2) in order to provide a global assessment of the immune-negative blockade.
In a third group of gene, is included a gene useful for assessing the responsiveness of a patient to an immune checkpoint blockade therapy, especially a therapy targeting LAG3. LAG3 is an inhibitory receptor on antigen activated T-cells. It delivers inhibitory signals upon binding to ligands, such as FGL1. Following TCR engagement, LAG3 associates with CD3-TCR in the immunological synapse and directly inhibits T-cell activation. A synergistic effect with PD-1 has been mentioned. LAG3 may act as a co-receptor of PD-1. Then, it could be interesting to provide data on the expression of LAG3.
As used herein, the terms "LAG3” , "Lymphocyte Activating 3", "CD223" and "FDC" are used interchangeably and refer to the human LAG3 gene, for example such as described under the Uniprot reference: P18627or GeneCard ID: GC12P008203.
In a fourth group of gene, is included a gene useful for assessing the responsiveness of a patient to an immune checkpoint blockade therapy, especially a therapy targeting TLR4. The stimulation of TLR4, in particular its overexpression, has also an important role in the stimulation of the immune response. Therefore, the fourth group comprises TLR4. As used herein, the terms “TLR4", "Toll Like Receptor 4", "CD284" and "ARMD10" are used interchangeably and refer to the human TLR4 gene, for example such as described under the Uniprot reference: 000206 or GeneCard ID: GC09P117704.
Additional genes or group of genes can be taken into consideration in the present method. For instance, TIM3, TIGIT, TGF , VISTA, ICOS, 0X40, IDOl can be added in the set of genes to be studied for their expression.
As used herein, the terms "TIM3", "Hepatitis A virus cellular receptor 2", "HAVCR2", "T-cell immunoglobulin and mucin domain-containing protein 3", "T-cell immunoglobulin mucin receptor 3" and "T cell membrane protein-3", are used interchangeably and refer to the human HAVCR2 gene, for example such as described under the Uniprot reference: Q8TDQ0 or GeneCard ID: GC05M 157063.
As used herein, the terms "TIGIT", "T-cell immunoreceptor with Ig and ITIM domains", "V-set and immunoglobulin domain-containing protein 9", "V-set and transmembrane domain-containing protein 3" are used interchangeably and refer to the human TIGIT gene, for example such as described under the Uniprot reference: Q495A1 or GeneCard ID: GC03P114276.
As used herein, the terms "TGF ", "Transforming growth factor beta-1", "TGFB", "TGFB1" and "TGFbeta" are used interchangeably and refer to the human TGFB1 gene, for example such as described under the Uniprot reference: P01137 or GeneCard ID: GC19M041301.
As used herein, the terms "VISTA", "V-type immunoglobulin domain-containing suppressor of T-cell activation", "Platelet receptor Gi24", "Stress-induced secreted protein-1", "V-set domain-containing immunoregulatory receptor", "VSIR" and "SISP1" are used interchangeably and referto the human VSIR gene, for example such as described under the Uniprot reference: P14902or GeneCard ID: GC08P039891.
As used herein, the terms "ICOS", "Inducible T-cell costimulator", "Activation-inducible lymphocyte immunomediatory molecule", and "CD278" are used interchangeably and refer to the human ICOS gene, for example such as described under the Uniprot reference: Q9Y6W8 or GeneCard ID: GC02P203937.
As used herein, the terms "0X40", "Tumor necrosis factor receptor superfamily member 4", "TNFRSF4", "ACT35 antigen", "AX transcriptionally-activated glycoprotein 1 receptor" and "CD134 "are used interchangeably and refer to the human TNFRSF4 gene, for example such as described under the Uniprot reference: P43489 or GeneCard ID: GC01M001211.
As used herein, the terms " IDOl ", "Indoleamine 2,3-Dioxygenase 1", "Indoleamine-Pyrrole 2,3-Dioxygenase" and "INDO" are used interchangeably and refer to the human IDOl gene, for example such as described under the Uniprot reference: P14902 or GeneCard ID: GC08P039891. Table 1 - Table with a summary of the description of the genes.
Figure imgf000013_0001
Optionally, in particular as detailed above, the set of genes may further comprise additional genes. However, in a preferred aspect, the total number of genes in each group of genes is no more than 20, 19, 18, 17, 16, 15, 14, 13, 12, or 11 genes.
In an aspect, the set of genes taken into consideration in the methods of the present invention includes or comprises at least one of the following set of genes:
- PD-1, PD-L1, CTLA-4, CD28, LAG3 and TLR4;
- PD-1, PD-L1, PD-L2, CTLA-4, CD28, LAG 3 and TLR4;
- PD-1, PD-L1, CTLA-4, CD28, CD80, LAG 3 and TLR4;
- PD-1, PD-L1, PD-L2, CTLA-4, CD28, CD80, LAG 3 and TLR4;
- PD-1, PD-L1, CTLA-4, CD28, CD86, LAG 3 and TLR4;
- PD-1, PD-L1, PD-L2, CTLA-4, CD28, CD86, LAG 3 and TLR4;
- PD-1, PD-L1, CTLA-4, CD28, CD80, CD86, LAG 3 and TLR4;
- PD-1, PD-L1, PD-L2, CTLA-4, CD28, CD80, CD86, LAG3 and TLR4;
- PD-1, PD-L1, CTLA-4, CD28, LAG3 and TLR4;
- PD-1, PD-L1, PD-L2, CTLA-4, CD28, and TLR4;
- PD-1, PD-L1, CTLA-4, CD28, CD80, and TLR4;
- PD-1, PD-L1, PD-L2, CTLA-4, CD28, CD80, and TLR4;
- PD-1, PD-L1, CTLA-4, CD28, CD86, and TLR4;
- PD-1, PD-L1, PD-L2, CTLA-4, CD28, CD86, and TLR4;
- PD-1, PD-L1, CTLA-4, CD28, CD80, CD86, and TLR4;
- PD-1, PD-L1, PD-L2, CTLA-4, CD28, CD80, CD86, and TLR4;
- PD-1, PD-L1, CTLA-4, CD28, LAG3 and TLR4;
- PD-1, PD-L1, PD-L2, CTLA-4, CD28, and LAG 3;
- PD-1, PD-L1, CTLA-4, CD28, CD80, and LAG 3;
- PD-1, PD-L1, PD-L2, CTLA-4, CD28, CD80, and LAG3;
- PD-1, PD-L1, CTLA-4, CD28, CD86, and LAG 3;
- PD-1, PD-L1, PD-L2, CTLA-4, CD28, CD86, and LAG3;
- PD-1, PD-L1, CTLA-4, CD28, CD80, CD86, and LAG 3;
- PD-1, PD-L1, PD-L2, CTLA-4, CD28, CD80, CD86, and LAG3;
- PD-1, PD-L1, CTLA-4, LAG 3 and TLR4;
- PD-1, PD-L1, PD-L2, CTLA-4, LAG 3 and TLR4;
- PD-1, PD-L1, CTLA-4, CD80, LAG3 and TLR4; - PD-1, PD-L1, PD-L2, CTLA-4, CD80, LAG 3 and TLR4;
- PD-1, PD-L1, CTLA-4, CD86, LAG3 and TLR4;
- PD-1, PD-L1, PD-L2, CTLA-4, CD86, LAG 3 and TLR4;
- PD-1, PD-L1, CTLA-4, CD80, CD86, LAG 3 and TLR4; and
- PD-1, PD-L1, PD-L2, CTLA-4, CD80, CD86, LAG 3 and TLR4.
Any of the above-mentioned set may further include or comprise one or several genes selected in the group consisting of VISTA, TIM-3, TIG IT, TGF , ICOS, 0X40 and IDOl.
Immune checkpoint blockade therapy
The methods disclosed herein allow the selection of an anti-cancer therapy for treating a subject suffering from cancer or to assess the responsiveness of a subject to an anti-cancer therapy. In particular, the anti cancer therapy is an immune-oncology therapy, especially an immune checkpoint blockade therapy.
As used herein, the terms "immune checkpoint blockade therapy", ""immune checkpoint therapy" or "immune checkpoint inhibitor therapy" are interchangeable are refers to a therapy or treatment that uses medications known as immune checkpoint inhibitors to address several types of disease, especially such as cancer. Such therapy targets immune checkpoints, key regulators of the immune system that when stimulated can dampen the immune response to an immunologic stimulus. When these checkpoints are blocked, T cells can kill cancer cells better. Currently approved checkpoint inhibitors target for example the immune checkpoint CTLA4, PD-1, and PD-L1.
The immune checkpoint blockade therapy disclosed herein can be a monotherapy, a bitherapy ora tritherapy. For example, the immune checkpoint blockade therapy can be a PD-1 inhibitor, alone (monotherapy) or in combination with a CTLA4 inhibitor (bitherapy), in combination with a CTLA4 inhibitor and a LAG3 inhibitor (tritherapy). The immune checkpoint blockade therapy disclosed herein can be a monotherapy, a bitherapy or a tritherapy of any of the therapy described below.
In particular, the immune checkpoint blockade therapy can be any of: (i) a PD-1 inhibitor, a PD-L1 inhibitor, a CTLA-4 inhibitor, a LAG3 inhibitor, a TLR4 inhibitor (monotherapies), (ii) a PD-1 inhibitor and a CTLA-4 inhibitor, a PD-1 inhibitor and a LAG3 inhibitor, a PD-1 inhibitor and a TLR4 inhibitor, a PD-L1 inhibitor and a CTLA-4 inhibitor, a PD-L1 inhibitor and a LAG3 inhibitor, a PD-L1 inhibitor and a TLR4 inhibitor, a CTLA-4 inhibitor and a LAG3 inhibitor, a CTLA-4 inhibitor and a TLR4 inhibitor, a LAG3 inhibitor and a TLR4 inhibitor (bitherapies) and iii) a PD-1 inhibitor, a CTLA-4 inhibitor and a LAG3 inhibitor; a PD-1 inhibitor, a CTLA-4 inhibitor and a TLR4 inhibitor; a PD-L1 inhibitor, a CTLA-4 inhibitor and a LAG3 inhibitor; a PD-L1 inhibitor, a CTLA-4 inhibitor and a TLR4 inhibitor; a CTLA-4 inhibitor, a LAG3 inhibitor and a TLR4 inhibitor (tritherapies). In one aspect, the immune checkpoint blockade therapy may be selected from the group consisting of a PD- 1 inhibitor, a PD-L1 inhibitor, a CTLA-4 inhibitor, a LAG3 inhibitor, a TLR4 inhibitor and any combination thereof, and optionally an 0X40 inhibitor, an IDOl inhibitor, an ICOS inhibitor, a TIME3 inhibitor, a TIG IT inhibitor, a TGF inhibitor and a VISTA inhibitor and any combination thereof.
Preferably, the immune checkpoint blockade therapy is selected from the group consisting of an anti-PDT antibody, an anti-PD-Ll antibody, an anti-CTLA-4 antibody, an anti-LAG3 antibody, an anti-TLR4 antibody and any combination thereof, and optionally an anti-OX40 antibody, an anti-IDOl antibody, an anti-ICOS antibody, an anti-TIME3 antibody, an anti-TIGIT antibody, an anti-TGFB antibody and an anti-VISTA antibody and any combination thereof.
In one aspect, the anticancer therapy is an immune checkpoint inhibitor, preferably a PDT or PD-L1 inhibitor. Programmed cell death protein 1 (PDT) inhibitors and programmed death-ligand 1 (PD-L1) inhibitors are a group of checkpoint inhibitor anticancer drugs that block the activity of PDT and PDL1 immune checkpoint proteins present on the surface of cells. In the cancer disease state, interaction of PD-L1 on tumor cells with PDT on T-cells reduces T-cell function signals to prevent the immune system from attacking and clearing the tumor cells, creating an immunosuppressive environment. An immune checkpoint blockade therapy targeting PD-1/PD-L1 can be for instance an inhibiting anti-PDT antibody, an inhibiting anti-PD-Ll antibody or an inhibiting anti-PD-L2 antibody, preferably an inhibiting anti-PDT antibody or an inhibiting anti-PD-Ll antibody. Such antibodies are well-known in the art. Several antibodies have already been accepted as drug. Others are still in clinical development.
Several anti-PDT are already clinically approved and others are still in clinical developments. For instance, the anti-PDl antibody can be selected from the group consisting of Pembrolizumab (also known as Keytruda lambrolizumab, MK-3475), Nivolumab (Opdivo, MDX-1106, BMS-936558, ONO-4538), Pidilizumab (CT-011), Cemiplimab (Libtayo), Camrelizumab, AUNP12, AMP-224, AGEN-2034, BGB-A317 (Tisleizumab), PDR001 (spartalizumab), MK-3477, SCH-900475, PF-06801591, JNJ-63723283, genolimzumab (CBT-501), LZM-009, BCD-100, SHR-1201, BAT-1306, AK-103 (HX-008), MEDI-0680 (also known as AMP-514) MEDI0608, JS001 (see Si-Yang Liu et a I., J. Hematol. Oncol.10:136 (2017)), BI-754091, CBT-501, INCSHR1210 (also known as SHR- 1210), TSR-042 (also known as ANB011), GLS-010 (also known as WBP3055), AM-0001 (Armo), STI-1110 (see WO 2014/194302), AGEN2034 (see WO 2017/040790), MGA012 (see WO 2017/19846), or IBI308 (see WO 2017/024465, WO 2017/025016, WO 2017/132825, and WO 2017/133540), monoclonal antibodies 5C4, 17D8, 2D3, 4H1, 4A11, 7D3, and 5F4, described in WO 2006/121168. Bifunctional or bispecific molecules targeting PDT are also known such as RG7769 (Roche), XmAb20717 (Xencor), MEDI5752 (AstraZeneca), FS118 (F-star), SL-279252 (Takeda) and XmAb23104 (Xencor). Several anti-PD-Ll are already clinically approved and others are still in clinical developments. For instance, the anti-PD-Ll antibody can be selected from the group consisting of Atezolizumab (Tecentriq), Avelumab (Bavencio), Durvalumab (Imfinzi), KN035, CK-301 (Checkpoint Therapeutics), AUNP12, CA-170, BMS-986189.
Particularly, the PD-1 or PD-L1 inhibitor is selected from the group consisting of atezolizumab, durvalumab, avelumab, nivolumab, pembrolizumab, pidilizumab, cemiplimab, camrelizumab, sintilimab (IBI308), tislelizumab (BGB-A317), toripalimab (JS 001), dostarlimab (TSR-042, WBP-285), BMS 936559, MPDL3280A, MSB0010718C, MEDI4736 and any combination thereof, preferably nivolumab, pembrolizumab and atezolizumab.
In one aspect, the anticancer therapy is an immune checkpoint inhibitor, preferably a CTLA-4 inhibitor. An immune checkpoint blockade therapy targeting CTLA-4 can be for instance an inhibiting anti-CTLA-4 antibody. Such antibodies are well-known in the art. Several antibodies have already been accepted as drug. Others are still in clinical development. For instance, the CTLA-4 inhibitor can be selected from ipilimumab, tremelimumab, and AGEN-1884. Anti-CTLA-4 antibodies are also disclosed in WO18025178, W019179388,
W019179391, WO19174603, W019148444, WO19120232, WO19056281, WO19023482, W018209701,
W018165895, WO18160536, WO18156250, WO18106862, WO18106864, WO18068182, W018035710,
WO18025178, W017194265, WO17106372, W017084078, WO17087588, W016196237, WO16130898,
WO16015675, WO12120125, W009100140 and W007008463.
In one aspect, the anticancer therapy is an immune checkpoint inhibitor, preferably a LAG3 inhibitor. An immune checkpoint blockade therapy targeting LAG3 can be for instance an inhibiting anti-LAG3 antibody. Such antibodies are well-known in the art. Several antibodies have already been accepted as drug. Others are still in clinical development. For instance, the LAG-3 inhibitor can be selected from IMP321 (Immuntep®), LAG525 (Novartis), BMS-986016 (Bristol-Myers Squibb), or TSR-033 (Tesaro). Anti-LAG-3 antibodies are also disclosed in W02008132601, EP2320940, W019152574.
In one aspect, the anticancer therapy is an immune checkpoint inhibitor, preferably a TLR4 inhibitor. An immune checkpoint blockade therapy targeting TLR4 can be for instance an inhibiting anti-TLR4 antibody. Numerous antagonist antibodies are disclosed in the art, illustrative examples of which include as described for example in U.S. Pat. App. Pub. No. 2009/0136509 to Blake (e.g., antibodies based on monoclonal antibody MTS510) and U.S. Pat. App. Pub. No. 2012/0177648 to Kosco-Vilbois et a I., are hereby incorporated by reference herein in their entirety. Other TLR4 antagonist antibodies are available commercially, a non- limiting example of which is NI-0101, which is available from Novlmmune SA (Plan-les- Ouates, Switzerland). TLR4 inhibitor are also under FDA approval, for example such as eritoran (phase III) and ibudilast (Av41 1 ; phase II). In one aspect, the anticancer therapy is an ICOS inhibitor, in particular an anti-ICOS antibody. ICOS antibodies are known in the art, such as JTX-2011, GSK3359609, MEDI-570 (NCI) and KY1044 (Kymab Limited).
In one aspect, the anticancer therapy is an IDOl inhibitor, in particular an anti-IDOl antibody. IDOl inhibitors are known in the art, such as LY3381916, BMS-986205, Epacadostat, Navoximod, PF-06840003, INCB024360 (Incyte; 4-({2-[(aminosulfonyl)amino]ethyl}amino)-N-(3-bromo-4-fiuorophenyl)-N'-hydroxy-l,2,5-oxadiazole- 3-carboximidamide), Indoximod, 1-methyl-tryptophan (New Link Genetics), GDC-0919 (Genentech), indoximod (NewLink Genetics, see, e.g., Clinical Trial Identifier Nos. NCT01191216; NCT01792050), or compounds such as described in WO2015119944.
In one aspect, the anticancer therapy is an 0X40 inhibitor, in particular an anti- 0X40 antibody. 0X40 inhibitors are known in the art, such as AZ MEDI6469, MEDI6383, MEDI0562, INCAGN01949, GSK3174998, 9B12, MOXR 0916 and PF-04518600 (PF-8600).
In one aspect, the anticancer therapy is a TIM3 inhibitor, in particular an anti-TIM3 antibody. TIM3 antibodies are known in the art, such as MBG453 and MEDI9447 or antibodies described in WO2016111947 and W02011155607A1.
In one aspect, the anticancer therapy is a TGFB inhibitor, in particular an anti- TGF antibody. TGF inhibitors are known in the art, such as Trabedersen (AP12009), M7824, Galusertinib (LY2157299), AVID200, Fresolimumab.
In one aspect, the anticancer therapy is a TIG IT inhibitor in particular an anti-TIGIT antibody. Antibodies directed against TIG IT are also known in the art, such as tiragolumab, OMP-31M32, MTIG7192A (Genentech), BMS-986207 or AB154, BMS-986207 CPA.9.086, CHA.9.547.18, CPA.9.018, CPA.9.027, CPA.9.049, CPA.9.057, CPA.9.059, CPA.9.083, CPA.9.089, CPA.9.093, CPA.9.101, CPA.9.103, CHA.9.536.1, CHA.9.536.3, CHA.9.536.4, CHA.9.536.5, CHA.9.536.6, CHA.9.536.7, CHA.9.536.8, CHA.9.560.1, CHA.9.560.3, CHA.9.560.4, CHA.9.560.5, CHA.9.560.6, CHA.9.560.7, CHA.9.560.8, CHA.9.546.1, CHA.9.547.1, CHA.9.547.2, CHA.9.547.3, CHA.9.547.4, CHA.9.547.6, CHA.9.547.7, CHA.9.547.8, CHA.9.547.9, CHA.9.547.13, CHA.9.541.1, CHA.9.541.3, CHA.9.541.4, CHA.9.541.5, CHA.9.541.6, CHA.9.541.7, and CHA.9.541.8 as disclosed in W019232484. Anti-TIGIT antibodies are also disclosed in WO16028656, W016106302, W016191643, W017030823, W017037707, WO17053748, WO17152088, WO18033798, WO18102536, WO18102746, W018160704, W018200430, WO18204363, W019023504, WO19062832, W019129221, W019129261, W019137548, W019152574, W019154415, W019168382 and W019215728.
In one aspect, the anticancer therapy is a VISTA inhibitor, preferably an anti-VISTA antibody. Antibodies directed against TIG IT are also known in the art, such as CI-8993 (Curis Inc), JNJ-61610588, CA-170 Particularly, the immune checkpoint blockade therapy is selected from the group consisting of pembrolizumab, nivolumab, atezolizumab, durvalumab, avelumab, ipilimumab, NI-0101, BMS-986016, Sym022, GSK2831781, AZ MEDI5752, and optionally AZ MEDI6469, GSK3174998, INCB024360, MBG453, MTIG7192A, CI-8993 and tiragolumab.
Prediction methods
The methods disclosed herein can be used to predict the optimal anti-cancer treatment or a response to a cancer treatment. The cancer treatment can be any immune checkpoint inhibitor treatment including, but not limited, to the treatments and therapies described here above, in particular under the "Immune checkpoint blockade therapy" paragraph. Examples of cancers are provided here below, in particular under the "Patient and Tumor" paragraph.
In one embodiment, the invention concerns a method for selecting an optimal immune checkpoint blockade therapy for treating a subject suffering from cancer, wherein the method comprises:
- providing mRNA expression level in a tumor sample and a histologically matched normal sample of a set of genes comprising the following genes or groups of genes: 1) a group of genes comprising PD-1, PD-L1, and optionally PD-L2; 2) a group of genes comprising CTLA-4 and CD28, and optionally CD80 and CD86; 3) LAG3; 4) TLR4 and optionally one or more of the following genes 5) ICOS, IDOl, 0X40, TIM-3, TIGIT, TGF , and VISTA; and wherein the tumor and histologically matched normal samples are from the same subject;
- determining a mRNA fold change of Tumor versus Normal (Fc TvN) for each gene of the set of genes; and calculating a mean FcTvN fold change for each group of genes;
- normalizing the Fc TvN fold changes or the mean Fc TvN fold changes for the group of genes;
- ranking the normalized Fc TvN fold changes and the normalized mean Fc TvN fold changes, the highest value of the normalized Fc TvN fold changes and mean Fc TvN fold changes being ranking first and being predictive of a highest efficacy of an immune checkpoint blockade therapy for treating the cancer of the subject; and
- optionally selecting the optimal immune checkpoint blockade therapy based on the ranked genes or group of genes.
The present invention also relates to a method for selecting a set of genes for which the differential expression between a tumor sample and a normal histologically matched sample from the same patient is indicative of a therapeutic benefit of a treatment with an immune checkpoint blockade therapy. More particularly, as disclosed above, the method provides the mRNA expression level, and optionally miRNA expression levels, in a tumor sample and a normal histologically matched sample from the same patient. The invention particularly concerns a method for determining if a subject suffering from a cancer is susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy, wherein the method comprises:
- providing mRNA expression level in a tumor sample and a histologically matched normal sample of a set of genes comprising the following genes or groups of genes: 1) a group of genes comprising PD-1, PD-L1, and optionally PD-L2; 2) a group of genes comprising CTLA-4 and CD28, and optionally CD80 and CD86; 3) LAG3; 4) TLR4 and optionally one or more of the following genes 5) ICOS, IDOl, 0X40, TIM-3, TIGIT, TGF , and VISTA; and wherein the tumor and histologically matched normal samples are from the same subject;
- determining a mRNA fold change of Tumor versus Normal (Fc TvN) for each gene of the set of genes; and calculating a mean Fc TvN fold change for each group of genes;
- normalizing the Fc TvN fold changes or the mean Fc TvN fold changes for the group of genes;
- ranking the normalized Fc TvN fold changes and the normalized mean Fc TvN fold changes, the highest value of the normalized Fc TvN fold changes and mean Fc TvN fold changes being ranking first and being predictive of the susceptibility of the subject to benefit from the treatment with an immune checkpoint blockade therapy; and
- optionally selecting the immune checkpoint blockade therapy most susceptible to benefit to the subject based on the ranked genes or group of genes.
The method according to the invention comprises a step of determining the expression level of one or a set of genes comprising the following genes or groups of genes: 1) a group of genes comprising PD-1, PD-L1, and optionally PD-L2; 2) a group of genes comprising CTLA-4 and CD28; 3) LAG3; 4) TLR4 and optionally one or more of the following genes 5) VISTA, TIM-3, TIGIT, TGF , ICOS, 0X40 and IDOl. In particular, the gene expression levels of a cancer sample are compared to the gene expression levels from a normal sample, in particular a cell or tissue known to be free of, or suspected to be free of cancer, preferably from the same patient, more preferably a histologically matched normal sample from the patient. These biological samples are more particularly described hereafter under the paragraph "Patients and Tumor".
Determining the expression level for a gene or group of genes such as those identified above can be carried out by any method known in the art and may vary among embodiments of the invention.
Determining the expression levels of a gene may be carried out by any method known in the art such as, but not limited to, Northern analysis, mRNA or cDNA microarrays, polymerase chain reaction (PCR), quantitative or semi-quantitative RT-PCR, real time quantitative or semi-quantitative RT-PCR, enzyme-linked immunosorbent assay (ELISA), magnetic immunoassay (MIA), flow cytometry, microarrays, ligase chain reaction (LCR), transcription-mediated amplification (TMA), strand displacement amplification (SDA) and nucleic acid sequence based amplification (NASBA) or any such methods known in the art. In some embodiments, the method comprises the determination of the expression profile of a cancer and/or normal sample having probes to a specific set of genes or proteins.
Alternatively, the level of expression can be determined with a ship comprising a set of primers or probes specific for the set of genes. Expression levels obtained from cancer and normal samples may be normalized by using expression levels of proteins which are known to have stable expression such as RPLPO (acidic ribosomal phosphoprotein PO), TBP (TATA box binding protein), GAPDH (glyceraldehyde 3-phosphate dehydrogenase) or b-actin.
Based on the mRNA expression levels of the gene, it can be assessed (i) which genes are overexpressed in the cancer sample in comparison to the histologically matched normal sample; ii) which genes are expressed at a similar level in the cancer sample in comparison to the normal histologically matched sample; and iii) which genes are underexpressed in the tumor sample in comparison to the normal histologically matched sample. This difference of expression between the normal and tumor sample allows defining a mRNA fold change of the expression of genes between Tumor versus Normal (Fc TvN).
In one aspect, when a group of genes is considered, the Fc TvN is the average or arithmetic mean of the fold changes of the genes belonging to the group of genes. In this context, for example, when group 1) consists of PD-1, PD-L1 and PD-L2, the Fc TvN is the average or arithmetic mean of the sum of Fc TvN(PD-l), Fc TvN(PD- Ll) and Fc TvN(PD-L2). When group 1) consists of PD-1 and PD-L1, the Fc TvN is the average or arithmetic mean of the sum of Fc TvN(PD-l) and Fc TvN(PD-Ll). When group 2) consists of CTLA-4 and CD28, the Fc TvN is the average or arithmetic mean of the sum of FcTvN(CTLA-4) and Fc TvN(CD28).
Alternatively, when the group of genes consists of one gene, obviously, there is no need of average or arithmetic mean for the gene. Then, when there is only one gene, the Fc TvN of the gene is taken into consideration in the method.
In a preferred aspect, a gene is overexpressed when the fold change between the tumor sample and the histologically matched normal sample is higher than 1.3, a gene is expressed at a similar level when the fold change is between -1.3 and 1.3, and a gene is underexpressed when the fold change is lower than -1.3. However, different threshold of fold change may also be used, for instance a first class with a fold change higher than x, a second class with a fold change is between -x and x, and a third class with a fold change lower than -x, x being a number between 1 and 5, preferably between 1 and 4, between 1 and 3 or between 1 and 2. For instance, x could be 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9 or 2. 3.
In a particular aspect, the Fc TvN for a gene or for a group of genes only comprises genes having an absolute value of fold change of 1.3 or more (i.e., Fc TvN or mean Fc TvN of 1.3 or more or of - 1.3 or less). More particularly, when a group of genes is considered, only the genes having an absolute value of fold change of
1.3 or more (i.e., Fc TvN of 1.3 or more or of - 1.3 or less) are used to calculate the mean Fc TvN.
Particularly, for a group of genes that comprises only one gene (i.e. group 3 or 4 comprising LAG3 or TLR4, respectively), if the Fc TvN of the gene is inferior to 1.3 or superior to -1.3 (e.g. if the Fc TvN of the gene is comprised between -1.2 and 1.2) a score of 0 is attributed to the gene. This means that drug targeting such gene is not the best appropriate option to treat the subject.
In another aspect, the Fc TvN for a gene or for a group of genes only comprises genes having a fold change of
1.3 or more. More particularly, when a group of genes is considered, only the genes having a fold change of
1.3 or more are used to calculate the mean Fc TvN. In this aspect, the method is thus limited to genes that are overexpressed. Up regulation (or overexpression) and down regulation (or under-expression) are relative terms meaning that a detectable difference, beyond the contribution of noise in the system used to measure it, may be found in the amount of expression of genes relative to a baseline. In some embodiments, a baseline expression level may be measured from the amount of mRNA for a particular genetic marker in a normal cell or other standard cell (i.e. positive or negative control) or may be normalized by using expression levels of genes which are known to have stable expression, for example such as RPLPO (acidic ribosomal phosphoprotein PO), TBP (TATA box binding protein), GAPDH (glyceraldehyde 3-phosphate dehydrogenase) or b-actin.
Optionally, the mRNA fold change of a gene can be corrected by considering the expression of the miRNA of the gene in order to adjust possible miRNA intervention in translation.
Optionally, the mRNA fold change of a gene can be corrected by considering the expression of the miRNA of the gene in order to adjust possible miRNA intervention in translation. More preferably, a mean miRNAs fold change for each gene is calculated as the average of the miRNA fold changes between the tumor sample and the normal histologically matched sample for the gene. Then, a corrected mRNA fold change is calculated by dividing the mRNA fold change between the tumor sample and the normal histologically matched sample of the gene (mRNA TvN fold change) by the mean fold change for the miRNAs of the gene (mean miRNA TvN fold change), and the corrected mRNA fold change of the gene is then used in the method for classifying the genes into the three classes. Levels of miRNAs for the genes are determined in the tumor and normal samples. The miRNAs most likely to be involved in the gene expression regulation can be determined by using Target scan {www.targetscan.org/}. The method for measuring miRNA are well-known in the art. Then, in one embodiment, in the method according to the invention, the mRNA expression level in the tumor sample and in the normal histologically matched sample is further corrected with the expression of miRNA targeting the transcript of the gene.
Table 2 - Top 5 miRNA for each gene. (From TARGETSCAN database)
Figure imgf000023_0001
Another information that will be used in the method of the present invention are the intensity of the mRNA expression in tumour and in histological matched normal tissue from the same patients. The intensity can be assessed by measuring the signal that can be detected using of the microarrays technologies that enable to assess the Relative Fluorescent Units, whose value correlates with the steady state level of the mRNA (or microRNA). Detection can be performed also by RNAseq technologies (such as Next generation sequencing) and the intensities are assessed by the counts of the number of reads (tag), which also correlates with the steady state levels of the mRNA studies. Globally, technologies used enable to identify and measure the intensities/expression levels of all the types of mRNA (miRNA). Several technologies are exemplified, Agilent Microarrays, Affymetrix microarrays, lllumina RNAseq, and many others, including but not limited to RT- QPCR, Nanostring etc. The intensities measured in tumour tissues divded by the intensities measured in Normal tissues generates the Fold change of mRNAs and miRNAs.
The intensity measurements may be equated (transformed) to the degree of expression of the gene corresponding to the signal intensity of labeled cDNA or cRNA. Thus, the method according to the invention may detect the variability in expression by detecting differences in mRNA levels in cancerous tissue over normal tissue or standard intensities. Then, in one embodiment, the method further comprises the multiplication of the Fc TvN by expression intensity of the gene (I), either in the tumor sample (I T) or in the histologically matched normal sample (I N). Optionally, the expression intensity of each gene is measured as relative fluorescence unit (RFU).
Distinctions between expression of a genetic marker in normal sample versus cancerous sample may be made through the use of mathematical/statistical values that are related to each other. For example, in some embodiments, distinctions may be derived from a mean signal indicative of gene expression in a normal sample and variation from this mean signal may be interpreted as being indicative of cancerous tissue. In other embodiments, distinctions may be made by use of the mean signal ratios between different groups of readings, i.e. intensity measurements, and the standard deviations of the signal ratio measurements. A great number of such mathematical/statistical values can be used in their place such as return at a given percentile. These values can then be used to determine whether a cancer or tumor will likely respond to a treatment to an immune checkpoint blockade therapy and determine the optimal immune checkpoint blockade therapy to administer to the patient.
In a particular embodiment, the method comprises a normalization step. Such normalization step may be performed using a calibrator consisting of FcTvN of a set of subjects. Normalization of FcTvN means adjusting values measured on different scales to a notionally common scale, often prior to averaging. It is thus performed for a single patient in comparison to a group of patients.
This information can be provided by a group of patients having a cancer and receiving, having received and planned to receive similar or the same immune checkpoint blockade therapy. The group of patients may include 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 150 or 160 patients or more.
By "similar immune checkpoint blockade therapy", it is intended that the immune checkpoint blockade therapy has the same or similar target, i.e., i) PD-1/PD-L1, PD-L2, ii) CTLA-4/CD80,CD86,CD28, iii) LAG3 or iv) TLR4 or (i) PD-1, ii) PD-L1, iii) PD-L2, iv) CTLA-4, v) CD80 vi) CD86 vii) CD28. By the "similar immune checkpoint blockade therapy" can be different antibodies (e.g. Nivolumab or Pidilizumab) directed against the same immune checkpoint (e.g., PD-1). By the "same immune checkpoint blockade therapy", it is intended that the anti-cancer therapy is the same molecule, inhibitor or antibody (e.g. pembrolizumab). Anti-cancer therapies are particular provided here below under the paragraph "immune checkpoint blockade therapy".
Optionally, the patients of the group of patients used as a calibrator may have any type of cancer or any type of solid tumors. Optionally, the patients may have the same type of cancers. Optionally, the patients may have the same cancer. Optionally, the patients may have various therapeutic histories. Optionally, the patients may have received the same number of therapeutic lines, or even the same therapeutic lines. For instance, selection of the patients as calibrator can be patients with available transcriptomics data and clinical outcome (PFS) under treatment with each drug available can be assessed using data from publicly available clinical trial, such as found in clinicaltrials.gov, for example. Patients can be retrieved from the same or different clinical trials, so long they have been receiving the same or similar anti-cancer treatment.
In particular, the normalization step is in the form of deciles. In descriptive statistics, the term "decile" refers to the values that split the population data into ten equal fragments such that each fragment is representative of 1/lOth of the population. In other words, each successive decile corresponds to an increase of 10% points such that the 1st decile or D1 has 10% of the observations below it, then 2nd decile or D2 has 20% of the observations below it, and so on so forth.
The method according to the invention comprises the attribution of a score for each gene or groups of genes. In particular, the method according to the invention comprises the attribution of a score for each gene when the group includes only one gene or groups of genes when the group includes several genes. In one embodiment, the score is from 1 to 10, with 10 being the highest Fc TvN and 1 the lowest one.
Particularly, a score superior to 4 is predictive of an efficacy of an immune checkpoint blockade therapy for treating a cancer in the subject or of a susceptibility of the subject to benefit from an immune checkpoint blockade therapy. On the opposite, a score lower to 4 s predictive of an inefficacy of an immune checkpoint blockade therapy for treating a cancer in the subject or of a susceptibility of the subject to have no benefit from an immune checkpoint blockade therapy.
Group of genes or gene with the highest score (and not below a score of 4) are considered activated in the tumor of the patient and the corresponding immune checkpoint therapy, targeting such genes or group of genes is recommended. If more than one gene or group of genes meets this criterion, a combination of immune oncology treatment is considered as a potential therapy of interest in comparison to a single therapy (i.e. bitherapy or tritherapy). Accordingly, the group of gene(s) or the gene ranked first is combined with the group of gene(s) or gene ranked second if they both have a score of at least 4. This allows selecting the immune checkpoint therapy that targets these genes and groups of genes as the optimal therapy for the patient.
Then, the method according to the invention may further comprise the selection of the optimal immune checkpoint blockade therapy based on the ranked genes or group of genes, preferably the one two or three group(s) of genes having the highest scores. It can also comprise a step of administering a therapeutic amount of the optimal immune checkpoint blockade therapy to patient. The method may further comprise a step of selecting a patient susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy. It can also comprise a step of administering a therapeutic amount of the optimal immune checkpoint blockade therapy to the selected patient.
The method may also or alternatively comprise a step of selecting a patient who is not susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy or is a non-responder. Then, the selected patient will not be suitable to receive a therapeutic benefit of a treatment with an immune checkpoint blockade therapy because he/she would be a non-responder or because the treatment will likely be associated with adverse side effects.
Patients and Tumor
The patient is an animal, preferably a mammal, even more preferably a human. However, the patient can also be a non-human animal, in particular mammals such as dogs, cats, horses, cows, pigs, sheep, donkeys, rabbits, ferrets, gerbils, hamsters, chinchillas, rats, mice, guinea pigs and non-human primates, among others, that are in need of treatment.
The human patient according to the invention may be a human at the prenatal stage, a new-born, a child, an infant, an adolescent or an adult, in particular an adult of at least 30 years old or at least 40 years old, preferably an adult of at least 50 years old, still more preferably an adult of at least 60 years old, even more preferably an adult of at least 70 years old.
Preferably, the patient has been diagnosed with a cancer. In another particular embodiment, the patient suffers from a metastatic cancer or a cancer at an advanced stage. In one embodiment, the patient has been diagnosed with a cancer of stage III or IV.
In one embodiment, the patient suffers from an advanced solid tumor.
The amount of immune checkpoint inhibitor therapy to be administered is determined by standard procedure well known by those of ordinary skills in the art. Physiological data of the patient (e.g. age, size, weight, and physical general condition) and the routes of administration are taken into account to determine the appropriate dosage, so as a therapeutically effective amount will be administered to the patient. "An effective amount" or a "therapeutic effective amount" as used herein refers to the amount of active agent required to confer therapeutic effect on the subject, either alone or in combination with one or more other active agents, e.g. the amount of active agent that is needed to treat the targeted disease or disorder, or to produce the desired effect. The "effective amount" will vary depending on the agent(s), the disease and its severity, the characteristics of the subject to be treated including age, physical condition, size, gender and weight, the duration of the treatment, the nature of concurrent therapy (if any), the specific route of administration and like factors within the knowledge and expertise of the health practitioner. These factors are well known to those of ordinary skill in the art and can be addressed with no more than routine experimentation. It is generally preferred that a maximum dose of the individual components or combinations thereof be used, that is, the highest safe dose according to sound medical judgment.
The immune checkpoint inhibitor therapy may be administered as a single dose or in multiple doses.
Preferably, the treatment starts no longer than a month, preferably no longer than a week, after the determination of the optimal immune checkpoint inhibitor therapy for the patient suffering from cancer.
Preferably, the immune checkpoint inhibitor therapy is administered regularly, preferably between every day and every month, more preferably between every day and every two weeks, even more preferably between every day and every week.
The duration of treatment is preferably comprised between 1 day and 24 weeks, more preferably between 1 day and 10 weeks, even more preferably between 1 day and 4 weeks. In a particular embodiment, the treatment last as long as the cancer persists.
The method of the invention is aimed to select and/or treat a patient affected with a tumor.
In one embodiment, the tumor is from a cancer selected from the group consisting of leukemias, seminomas, melanomas, teratomas, lymphomas, non-Hodgkin lymphoma, neuroblastomas, gliomas, adenocarninoma, mesothelioma (including pleural mesothelioma, peritoneal mesothelioma, pericardial mesothelioma and end stage mesothelioma), rectal cancer, endometrial cancer, thyroid cancer (including papillary thyroid carcinoma, 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, basal cell carcinoma, squamous cell carcinoma, Karposi's sarcoma, keratoacanthoma, moles, dysplastic nevi, lipoma, angioma and dermatofibroma), nervous system cancer, brain cancer (including astrocytoma, medulloblastoma, glioma, lower grade glioma, 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, 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, Karposi'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 HER2- enriched breast cancer, luminal A breast cancer, luminal B breast cancer and triple negative 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, 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 clear renal cell carcinoma, chromophobe renal cell carcinoma, papillary renal cell carcinoma, adenocarcinoma, Wilm's tumor, nephroblastoma, lymphoma or leukemia), adrenal cancer, 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.
Preferably, the cancer is selected from the group consisting of head and neck (HN) cancer, Lung cancer, colorectal cancer (CRC), esophagus cancer, gastrointestinal (Gl) cancer; neuroendocrine (NE) cancer; non small cell lung carcinomas (NSCLC), bladder cancer, breast cancer, hepatocarcinoma, kidney cancer, leyomiosarcoma, liposarcoma (LS), lymphoma, melanoma, soft tissue cancer and rhabdomyosarcoma.
The methods according to the invention comprise a step of characterizing a tumor sample of a patient in comparison with a normal sample from the same patient. Therefore, the methods according to the invention may comprise an initial step of providing samples from the patient. The sample can, for example, be obtained from a subject by, but not limited to, venipuncture, excretion, biopsy, needle aspirate, lavage sample, scraping, surgical incision, colonoscopy, fibroscopy, endoscopy, surgery or, any combination thereof, and the like.
Preferably, the tumor sample and the normal sample provides from the same type of tissue. More particularly, the tumor and normal samples are histologically matched tissues. In one embodiment, the cancer sample and the normal sample are from the same patient.
Then, two samples are necessary, namely one tumor sample and one normal sample from the same patient. Preferably, the tumour sample and the normal sample provides from the same type of tissue. More particularly, the tumor and normal samples are histologically matched tissues. Tumor tissue is a fragment obtained from the tumor or metastatic lesions, (usually provided in interventional radiology) and containing at least 50% tumoral cells, immune infiltrating cells, stromal cells, vessels. The normal tissue is a fragment from histologically matched normal tissue (usually provided in fibroscopy or endoscopy units) and containing at least 30% normal cells (e.g., epithelial cells). DNA and total RNA preparations are performed and only high- quality nucleic acids quality are used for transcriptomics investigations (measure of differential expression between the tumor and normal tissues. The "normal" sample preferably does not comprise any cancer cell. Non-exhaustively, examples of pairs of tumors with corresponding histological normal tissue are the followings:
1. lung cancer adenocarcinomas or derived metastases - bronchial normal mucosa
2. breast cancer tumors or derived metastases - normal epithelial breast cells
3. colon cancers adenocarcinomas or derived metastases - normal colon or rectal mucosa
4. kidney cancers or derived metastases - normal kidney cells
5. melanomas or derived metastases - synchronous naevi
6. rhabdomyosarcomas or derived metastases - normal muscle tissue, normal skeletal muscle
7. liver carcinomas or derived metastases - normal liver cells
8. Oral-pharyngeals tumors (ORL) - normal buccal mucosa
9. Stomach carcinomas or derived metastases - normal stomach mucosa
10. Ovary cancer- normal Fallope tube mucosa
11. pancreatic cancers - normal parenchimatous tissue from pancreas
12. bladder tumor - Normal urothelial tissue
13. liposarcoma - normal adipose tissue
14. Lymphoma - normal tissue from lymph nodes
In some embodiments, the method further comprises isolating genetic material from the cancer and the histologically matched normal samples.
BRIEF DESCRIPTION OF THE DRAWING
Figure 1: Distribution of the SIMS score of activation and transcriptomic expression of the 10 targets (PD-1, CTLA-4, LAG-3 and TLR-4) for the 160 patients of the WINTHER dataset. Distribution of transcriptomic profiles indicating activation of PDL1 for A: the 32 patients with metastatic NSCLC from WINTHER dataset; B: the 123 patients with primary resectable NSCLC from CHEMORES dataset. Y axis = Log2 (PDL1 Fold change tumor versus normal); X axis= patients ranked in the order of increased PDL1 overexpression. SIMS score intensity plots for 160 patients of the WINTHER trial showing the variability of the importance of key 10 genes depending upon cancer histology and pointing out to the importance of LAG-3 and TLR-4 for some cancer types in particular. X axis: represents each patient from the WINTHER cohort (n=160); Z. axis: shows SIMS score from 1 to 10 for each group of genes: C. PDL-1/PDL-2/PD-1, D. CTLA-4, E. LAG-3, F. TLR-4. A color code indicates the level of activation. Each type of cancer is represented in 3D plot, with the symbols described; Y. axis: shows the intensity of mRNA expression component (in relative fluorescent unit) from the SIMS scores of the tumor tissue compared to the analogous normal tissue of each patient. X axis: patients ranked. Figure 2: Distribution of the recommended targets of the 10 treatments for all WINTHER trial 160 patients explored in silico. Recommended therapy for the 160 patients of the WINTHER trial based on the individual SIMS score for each of A. the four group of genes (PDL-1, CTLA-4, LAG-3, TLR-4); B. other genes {VISTA, TIM- 3, TIGIT, ICOS, 0X40 and IDOl). A treatment was selected when the SIMS score of activation of an gene or group of genes was 4 or above. When several groups of genes achieved the mandated threshold of activation, this pointed to potential combinations of 10 therapies.
Figure imgf000031_0001
with metastatic NSCLC from WINTHER dataset; B. the 123 patients with primary resectable NSCLC from CHEMORES dataset, based on the individual SIMS score for each of the four groups of genes (PDL-1, CTLA-4, LAG-3, TLR-4). A treatment target was selected when the SIMS score of activation of a gene or group of genes was above 4. When several groups of genes achieved the mandated threshold of activation, this pointed out to related combinations of 10 therapies. Recommended therapy targets for: C. the 32 patients with metastatic NSCLC from WINTHER dataset; D. the 123 patients with primary resectable NSCLC from CHEMORES dataset, based on the individual SIMS score for each of the four genes (VISTA, TIMS, TIGIT, ICOS, 0X40 and IDOl).
Figure 4: In silico modeling of CYTISCAPE based on SIMS scoring of 32 metastatic NSCLC. Legend: Distribution of transcriptomic profiles indicating activation of PDL1 (ranked from lowest to highest activation) and display of TIGIT co-activation for each of the 32 patients with metastatic NSCLC from WINTHER dataset; Y axis = Log2(PDLl Fold change tumor versus normal); X axis= patients ranked in the order of increased PDL1 overexpression. PDL1 SIMS scores 8, 9 and 10 correspond to the group PDL1>50%. PDL1 SIMS scores >4 correspond to PDL1>% group. SIMS modeling of potential efficacy of anti-PDLl and anti-TIGIT combination: In the group PDL1 >50% (n=8), SIMS data predicts efficacy for 5 patients: 26, 28, 29, 31 and 32 (representing 62.5%): CYTISCAPE reported 55.2 Overall response rate in this subgroup. In the group PDL1> 1% (n=19) SIMS data predicts efficacy for 7 patients: 20, 22, 26, 28, 29, 31 and 32 (representing 36%); CYTISCAPE reported 32% overall response rate in this subgroup.
Figure 5: In silico modeling of CYTISCAPE based on SIMS scoring of 32 metastatic NSCLC. Legend: Distribution of transcriptomic profiles indicating activation of PDL1 (ranked from lowest to highest activation) and display of TGFB co-activation for each of the 32 patients with metastatic NSCLC from WINTHER dataset.
Figure 6: Predicted efficacy of 10 in metastatic NSCLC - Best predicted options for combinations with anti- PDLl. SIMS immunogram of potential efficacy of the combination of 10 agents in metastatic NSCLC patients (n=32). Legend: Distribution of transcriptomic profiles indicating activation of PDL-1 (ranked from lowest to highest activation) and displaying the co-activation of CTLA-4, IDO-1, TIGIT, LAG-3, 0X40, TIM-3, TGFB1, ICOS and TLR-4 for each of the patients with metastatic NSCLC (n=32 patients) from the WINTHER dataset. Y axis = Log2 (PDL-1 -or other) genes Fold change tumor versus normal; X axis= patients ranked in the order of increased PDL-1 overexpression. Each gene is presented with symbols and colors.
EXAMPLES
MATERIALS AND METHOD Patients/Dataset: This study used the data generated during the WINTHER clinical trial and CHEMORES study. The WINTHER database includes transcriptomic and genomic data of 160 patients with advanced cancers such as non-small-cell lung carcinoma (NSCLC), colorectal cancer (CRC), Head and Neck (HN) and others (breast, hepatocellular cancer, bladder, rhabomyosarcoma, melanoma, liposarcoma, lymphoma, kidney, Gl, etc.), who progressed under standard treatment. The dataset used in the in-silico analysis consists in RNA-Seq data generated from tumor and analogous organ matched normal tissues from each patient.
For each patient, the normal tissue of origin of the tumor, as defined below, was biopsied. For example, in the case of a tumor tissue biopsy of a liver metastasis from a rectal adenocarcinoma, the matched normal tissue was the rectal mucosa obtained separately by rectal endoscopy.
Table 3. Matched normal tissue biopsy
Figure imgf000032_0001
The CHEMORES study consisted in the collection of tissue samples from a cohort of 123 patients who underwent complete surgical resection at the Institut Mutualiste Montsouris (IMM, Paris, France) between January 2002 and June 2006. This bio-banking study was approved by IMM's Ethics Committee. Patients were included in the study upon availability of histologically confirmed NSCLC tumor tissue and distant normal tissue from resected specimens and satisfactory RNA quality controls.
Genes and groups of genes
The inventors defined immune oncology groups of genes, which are composed of common markers associated with available immune oncology therapies. Such groups of genes are also called intervention or interventional points. Each immune oncology group of genes used in the study consists of both the therapy targets and the genes upstream of the targets, while together they can shine a light on the biological activity of the group of genes. Four different groups of genes were defined as described in Table 4.
Table 4. Groups of gene(s)
Figure imgf000033_0001
SIMS Score The SIMS score ranks the activation level of the groups of genes on a scale of 1 to 10 based on calculation which integrates genomics and transcriptomics generated from tumor and analogous organ matched normal tissues. For the assessment of 10 genes and groups of genes no genomic data was used due to the absence of relevant oncogenic events (mutations) for the related genes. Following the SIMS algorithm (Lazar V et a I., Oncotarget. 2015), the fold change values for all patients were calculated based on the ratio between mRNA expression in the tumor samples vs. the normal samples. Only target genes with log based 2 mRNA expression fold change values equals or higher than a threshold of 1 were considered and included in the SIMS algorithm.
The average or arithmetic mean of the target genes that attained the threshold was calculated as follows: for each group of gene(s), mRNA steady state level in tumor vs. normal was used to calculate a mean fold change of the pathway/point. Averaging was performed on the values of individual fold change (Fc) of tumor vs. normal for each gene belonging to the groups of gene(s). For calculating the mean/average fold change of a group of gene(s) k, denoted as Ek, the fold changes of differentially expressed genes with a fold change of at least 1.3 are used. Based on Agilent microarrays specifications, the threshold of 1.3 was considered as the lowest conferring accurate detection, since all Fc values were obtained by combining two dye swap microarray experiments. In other words, for each group of gene(s), an average fold-change of the genes / of the group of genes k is calculated, trimming values with a threshold of <1.3.
Formally, the inventors calculate Ek as the following: let Mk denote the set of genes that belong to the group of genes k, and m* denote the subset of Mk that includes only differential expressed genes with an absolute fold change >1.3. Ek is the average of the fold change of the genes m*. mk = [i\i E Mk and IF > 1.3}. The inventors then calculated the mean expression level for all the genes in /?¾: Ek = Ft wherein i E mk. In other words, the fold change for a particular group of genes is the average or arithmetic mean of the fold changes of genes belonging to the group of gene(s) as defined in Table 6 and having a fold change T vs. N of 1.3 or more.
For each immune oncology group of gene(s), the SIMS scores was calculated by applying a rank normalization step in the form of deciles, while the calibrator was composed of all 160 patients. Each patient was then matched to scores of 1 to 10 according to the decile to which the mRNA fold change expression is attributed. Scores amongst patients is comparable since all patients included in the calibrator.
Matching a patient to 10 therapy
The SIMS scores for the immune oncology groups of gene(s) were reviewed individually patient by patient and an assessment of a potential single or combined immune oncology treatment that can benefit a patient was assessed using consistent methodology. Groups of gene(s) with the highest SIMS score (and not below a score of 4) were considered activated and the corresponding 10 therapy recommended. If more than one gene or group of gene(s) met this criterion, a combination of immune oncology treatment was considered as a potential therapy rather than a single therapy. Immune oncology therapies targeting gene or group of gene(s) with scores lower than 4 were considered less likely to benefit the patient. In pathologies where only few patients (1 or 2) were identified to benefit from a single or combined immune oncology therapy, the patients were included in the closest therapy group from which a benefit can still be derived.
RESULTS
The inventors investigated the level of activation of PD-1, CTLA-4, LAG-3, TLR-4, VISTA, TIM-3, TIGIT, ICOS, 0X40 and IDOl by comparing the differential gene expression between the tumor and the analogous normal tissues for each patient enrolled in the WINTHER trial.
The inventors determined retrospectively insilico an activation score of the molecular pathways based on the Simplified Interventional Mapping System (SIMS) algorithm for 160 patients with a variety of advanced/metastatic cancers (non-small-cell lung carcinomas (NSCLC), head & neck adenocarcinomas (HN), colorectal carcinomas (CRC), etc.) from the WINTHER trial for whom the transcriptomic data was available (these patients were not treated with the selected 10 therapies). The inventors also analyzed retrospectively gene expression data obtained from 123 patients with primary resected NSCLC from the CHEMORES study, for whom transcriptomic information from tumor and normal lung tissues obtained from the resection specimens was also available.
Figure 1 presents the distribution of the PDL-1 expression in tumor compared with normal tissues in 32 patients with metastatic NSCLC (Figure 1A) and 123 patients with resected NSCLC (Figure IB). The percentage of patients with the gene expression-based SIMS score activated (>4) for PDL-1 is about 60%, which is in line with real world data for PDL-1 >1% expression by immunohistochemistry (IHC). But this analysis using SIMS brings additional information regarding the fraction of patients considered PDL-1 negative (with SIMS score <4) with lower expression in tumor than in normal tissue, pinpointing the risk of toxicity to normal tissues if these patients were treated with anti-PDl/Ll therapies.
Figures 2C, 2D, 2E and 2F present the distribution of SIMS scores that allows to identify specific profiles of patients that match each potential 10 therapies, as summarized in Table 3, Figure 2 and Table 6. Figure 1C shows the distribution of the 4 main groups of gene(s): i) PD-l/PDL-1/2, ii) CTLA-4, iii) LAG-3 and iv) TLR-4 (both expressed as SIMS scores and as transcriptomic expression) in the cohort of 160 patients with advanced metastatic disease including 56 with CRC, 32 with NSCLC, 26 with HN and 41 patients with other types of solid tumors (including kidney, breast, neuroendocrine, gastric, esophagus and liver tumors). It should be noted that there are significant differences in the patterns of activation according to the tumor type. For example, LAG-3 appears activated in a high fraction of HN, TLR-4 is predominantly activated in CRC whilst PDL-1/2 and CTLA-4 display complex profiles. Taken together these results suggest that only a simultaneous assessment of the activation of multiple 10 targets for each patient can lead to a more accurate selection of the optimal 10 therapeutic recommendations.
Based on the simultaneous investigation of SIMS scores of the main 4 targets, the inventors could define 10 different possible therapy choices that are presented in Figure 2A and Table 3. Details of the assignment method to one of the possible 10 therapies for each patient are presented in Material and Methods and Table 6. These 10 options could be expanded to include inhibitors of VISTA, TIM-3, TIGIT, ICOS, 0X40 and IDOl as presented in Figure 2B.
Focusing on the main targets PD-l/PDL-1, CTLA-4, LAG-3 and TLR-4, the distribution of recommended 10 treatments for the 160 WINTHER patients with advanced metastatic solid tumors based on the SIMS scores suggests 10 distinct therapeutic options that cover the vast majority of patients. Particularly notable is the high prevalence of activation of both PDL-1 and CTLA-4 (31%) in NSCLC patients, consistent with the established efficacy of the combination of anti-PD-1 and CTLA-4 agents in this population. Also, anti-TLR-4 alone (14%) and the combination of anti-PD-1 and anti-CTLA-4 (18%) in CRC seem to be worthy of exploration as well as the combination of anti-PD-1 and anti-LAG-3 in HN patients.
The retrospective treatment recommendations derived from this in silico analysis suggest that 10 therapies could potentially benefit up to 87% of patients with a variety of advanced/metastatic cancers, if treatment is tailored to the activated 10 targets.
Examining the profiles of 10 therapy options for the treatment of patients with metastatic NSCLC (n=32) from the WINTHER dataset and those with primary resectable NSCLC (n=123) from the CHEMORES dataset, outlines important differences as shown in Figure 3 A and B. Indeed, 40 % of early-stage resected NSCLC do not display activated 10 targets, in contrast with metastatic NSCLC that could all benefit from 10 therapeutic options. The pattern of activation of 10 targets is also very different in primary and metastatic NSCLC. 31% of metastatic NSCLC could potentially benefit from a combination of anti-PD-1 and anti-CTLA-4, and 13% could benefit from a combination of anti-PD-1 and anti-LAG-3. Whilst the most prevalent 10 recommendation for primary NSCLC would be based on an anti-CTLA-4 regimen, appropriate for 16% of patients.
The classic monotherapy based on anti-PD-1 would only be recommended in 6% of metastatic NSCLC and 3% of primary NSCLC. In contrast, used in combination, anti-PD-1 would be recommended in 66% of metastatic NSCLC and 27% of primary NSCLC.
Surprisingly, the new targets LAG-3 and TLR-4 that could lead to monotherapies, display very different distributions in primary or advanced NSCLC. Indeed, anti-LAG-3 monotherapy could be proposed to treat 9% of primary NSCLC but not patients with advanced disease. In contrast, anti-TLR-4 monotherapy could potentially benefit 6% of advanced NSCLC but only 1% of patients with primary disease.
Figure 2B shows the profile of activation of the other 10 targets: VISTA, TIM-3, TIGIT, ICOS, 0X40 and IDOl, that could represent further therapeutic options, alone or in combination with anti-PD-l/PDL-1. Patients who are expected to benefit from monotherapies represent a minority, with sub-cohorts not exceeding 5%. In contrast, combination therapies would potentially benefit an important fraction of patients: co-activation of PD-l/PDL-1 and TIM-3 was observed in 25% of patients in the WINTHER cohort (160 patients with various types of metastatic solid tumors) and co-activation of PD-l/PDL-1 and IDOl was observed in 25%. Other combinations of interest were PD-l/PDL-1 and TIGIT (10%), and PD-l/PDL-1 and 0X40 (15%). The inventors observed important variations depending on tumor type. As a representative example, Figure 3C and 3D present the same type of profiles in metastatic NSCLC (Figure 3C; n=32), and early-stage resected NSCLC (Figure 3D; n=123). The fraction of patients that would benefit from a 10 therapy based on anti-PDL-1 and anti-TIGIT represents respectively 16% of all metastatic NSCLC and 26% of PDL-1 positive (>1%) patients with metastatic NSCLC. Figure 4 presents a detailed investigation, patient by patient, of the co-activation of PDL1 and TIGIT targets.
Figure 5 presents in silico modeling of CYTISCAPE based on SIMS scoring of 32 metastatic NSCLC. Distribution of transcriptomic profiles indicating activation of PDL-1 (ranked from lowest to highest activation) and display of TIGIT co-activation for each of the patients with metastatic NSCLC (n=32 patients) from WINTHER dataset; Y axis = Log2(Fold change tumor versus normal) of PDL-1; X axis= patients ranked in the order of increased PDL-1 overexpression. PDL-1 SIMS scores > 8 correspond to the group PDL-1>50% (n=8 patients); PDL-1 SIMS scores >4 correspond to PDL-1> 1% group (n=19 patients). In the group PDL-1 SIMS > 8 (n=8 patients), SIMS data predicts efficacy for 5 patients: 26, 28, 29, 31 and 32 (representing 63%); CYTISCAPE reported 55.2 % overall response rate in this subgroup. In the group PDL-1 SIMS> 4% (n=19 patients) SIMS data predicts efficacy for 7 patients: 20, 22, 26, 28, 29, 31 and 32 (representing 37%); CYTISCAPE reported 31.5% overall response rate in this subgroup.
Figure 6 presents SIMS immunogram of potential efficacy of the combination of 10 agents in metastatic NSCLC patients (n=32). The figure presents the investigation of the co-activation status of the most frequently activated genes governing sensitivity to 10 treatments in metastatic NSCLC with PDL-1 SIMS > 4 (n = 19 patients) and with PDL-1 SIMS < 4 (n = 13 patients) enabling to determine the most prevalent therapeutic options. The Table presents the most prevalent therapeutic (i) combination for patients with PDL-1 SIMS > 4 with an anti-PD-l/PDL-1 (backbone of the combinations) associated with one of the inhibitors of co-activated 10 targets. 37
Table 5. Distribution of the recommended 10 options for: A. Treatment of patients with advanced metastatic solid tumors; B. Neo-adjuvant (or adjuvant) treatment of patients with primary NSCLC tumors.
Figure imgf000038_0001
Figure imgf000039_0001
Abbreviations: CRC = colorectal carcinomas; NSCLC = non-small-cell lung carcinomas, HN heac and neck carcinomas; ADC = adenocarcinomas; SCC = squamous cell carcinomas
DISCUSSION
Despite impressive durable responses, immune checkpoint inhibitors do not provide a long-term benefit to the majority of patients with cancer. The current biomarkers used to assess, predict, correlate with outcome and stratify patients include TMB, PDL-1 status and MSI-H. A high TMB correlates with a higher likelihood of neo-antigen presentation, most likely in "hot tumors" with a high level of infiltrating T cells. The T lymphocytes (LyT) that infiltrate the tumor (TILS) recognize the presented tumor neo-antigens that are identified as "non-self" (they are modified proteins because of mutations). The clone of LyT that recognizes the neo-antigen is activated and proliferates. The recruitment of activated LyT that recognize the tumor is a complex process that involves different antigen presentation mechanisms. Antigen Presenting Cells (APC) present the neo-antigen associated to the major histocompatibility complex II (CMH2) recognized by LyT CD4+ that differentiate in LyT Helper 1 (Ly Thl) and Helper 2 (Ly Th2). Ly Thl are key in recruitment of naive LyT CD8+ and induce their activation. CytotoxicT Lymphocytes (CD8+) and Natural Killer cells (NK) also recognize the neo-antigen restricted to CMH1 (Histocompatibility complex 1) and are subsequently activated, and can directly destroy tumor cells presenting the neo-antigen.
The process of recruitment and activation of CD8+ cells is controlled by different mechanisms of negative blockade. Understanding the genomics behind response or resistance to checkpoint blockade is critical to enhance the benefits of therapies targeting PDL-1 and CTLA-4. However, the complexity of 10 mechanisms is such that, beyond PDL-1 and CTLA-4, other regulators of immune activity such as LAG-3, TLR-4, VISTA, TIM-3, TIGIT, ICOS, 0X40 and IDOl may play an important role that should not be ignored.
Since there are already drugs in clinical development against both LAG-3 and TLR-4, with acceptable toxicity profiles from phase I trials, the inventors have focused the in-silico research on those two targets that could potentially increase the current 10 therapeutic armamentarium, provided a biomarker strategy is available. Their analysis has also provided insight into other therapeutic options targeting VISTA, TIM-3, TIGIT, ICOS, 0X40 and IDOl alone or combined with anti-PD-l/PDL-1.
The biomarker strategy was based on the SIMS scoring. SIMS is an algorithm (i) taking into consideration genomic alterations of the tumor and (ii) assessing the difference between gene expression levels in the tumor compared to expression levels in the analogous normal organ tissues. A dual biopsy of the tumor tissue and the analogous normal corresponding tissue is therefore necessary. The WINTHER trial demonstrated the feasibility of this dual biopsy approach that was well accepted by patients without significant comorbidity [20]. For NSCLC, the analogous normal tissue biopsy consisted in normal bronchial mucosa obtained with a bronchoscopy; for colorectal cancer, the normal tissue was colonic mucosa obtained by endoscopy (see Material and Methods).
This SIMS biomarker strategy enabled assessment of each of the selected 10 targets (PDL-1, CTLA-4, LAG-3, TLR-4) individually, in order to compare and select the more highly activated targets to be treated with single agents or combinations.
Overall, based on the current 10 portfolio of drugs (i) commercially approved, anti PD-1 or PDL-1 (pembrolizumab, nivolumab, avelumab, durvalumab, atezolizumab), anti-CTLA-4 (ipilimumab) or (ii) in clinical development, anti-LAG-3, anti-TLR-4, bispecific antibody anti-PD-1 and anti-CTLA-4 (MEDI5752); the inventors identified a total of 10 major therapeutic options. Allocation of patients to each of the 10 SIMS profiles corresponding to the therapeutic 10 options was based on transcriptomic investigations from 160 patients' data from the WINTHER trial (Rodon et al. Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial. Nature Medicine 2019), and 123 patients with primary NSCLC data (http://www.eortc.be/services/doc/EUprojects/Chemores.html).
The data identify important differences in the target activation profiles in primary and metastatic NSCLC that may influence the design of future clinical trials. Indeed, 40% of primary NSCLC have no activation of 10 targets, pointing to a potentially reduced efficacy of 10 in the neo-adjuvant setting that might explain the recent results of trials in this setting. In contrast, the vast majority of advanced metastatic NSCLC could benefit from 10 regimens.
Among conventional biomarkers, a high TMB could potentiate the benefit of any of the 10 therapeutic options, as it would enhance the tumor neoantigens presentation and increase the likelihood of activation of TCR receptors. A meta-analysis evaluating the usefulness of the PDL-1 status as a biomarker in 7,617 cancer patients from 14 randomized clinical trials, suggested that the practice of dichotomizing the range of PDL-1 expression scores is inadequate for patient stratification. However, the determination of the PDL-1/2 status (inhibiting PD-1) remains essential if complemented with the determination of the status of activation of CTLA-4, LAG-3 and TLR-4 in order to assess whether patients would benefit from 10 therapies.
The same approach assessing the relative expression of VISTA, TIM-3, TIGIT, ICOS, 0X40 and IDOl may increase even more the therapeutic options. Several trials have explored inhibitors of these targets in combination with anti-PD-l/PDL-1 antibodies, reporting varying levels of efficacy. All studies used PDL-1 status as a criterion for eligibility and stratification, most commonly >1%, or distinguishing PDL-1 status low (>1% <49%) and PDL-1 status high (>50%).
In the context of clinical trial observations, the inventors highlight two recent examples of closed trials that reported contrasting efficacy results.
The phase II CITYSCAPE trial - NCT03563716 (https://clinicaltrials.gov/ct2/show/NCT03563716), was the first randomized study evaluating the efficacy and safety of tiragolumab (anti-TIGIT) plus atezolizumab (anti-PDL- 1) compared with atezolizumab alone as an initial (first-line) treatment for patients with PDL-l-positive metastatic NSCLC. The trial reported positive results with both primary endpoints met at the 6 months evaluation in the intention-to treat (ITT) population with PDL1>1%: improvement in the objective response rate (ORR) (31.3% vs. 16.2%) and a 43% reduction in the risk of disease worsening or death (progression-free survival; PFS) (median PFS=5.4 vs. 3.6 months; hazard ratio (HR)=0.57, 95% Cl: 0.37-0.90) in the cohort treated with the combination of anti-TIGIT plus atezolizumab compared with atezolizumab alone. An exploratory analysis in patients with high levels of PDL-1 (TPS >50%) showed a clinically meaningful improvement in ORR (55.2% vs. 17.2%) and a 67% reduction in the risk of disease worsening or death.
Figure 3C shows that in the 60% of metastatic NSCLC with SIMS score for PDL-1 activated (<4), corresponding to the intention-to-treat population (ITT) in the CYTISCAPE trial, the fraction of patients with a profile with co activation of TIG IT and PD-l/PDL-1 target is 26 % which aligns with the findings of the CYTISCAPE trial. To get a more in-depth investigation of the correlation between SIMS in silico results and the real data from CYTISCAPE trial, the inventors performed a customized investigation, exploring for each individual patient with metastatic NSCLC the simultaneous activation of PDL1 and TIG IT targets. Figure 4 presents the detailed investigation.
Another example analyzed was the trial NCT02205333
(https://clinicaltrials.gov/ct2/show/NCT02205333?term=NCT02205333&rank=l) a non-randomized phase lb/11, open-label study to evaluate the safety and tolerability of MEDI6469 in combination with immune therapeutic agents or therapeutic monoclonal antibodies in subjects with selected advanced solid tumors or aggressive B-cell lymphomas. This trial investigated the combination of MEDI6469 (anti-OX40) and durvalumab (anti-PDL-1) in 3 arms with different therapeutic schemas. Patients were not stratified on the PDL-1 status. The only inclusion criteria were the absence of prior exposure to immunotherapy (either as a single agent or in combination) including but not limited to CD137 or 0X40 agonists, anti-CTLA-4, anti-PD-1, or anti-PD-Ll, anti-PD-L2 antibody or pathway-targeting agents.
The trial was stopped after enrollment of 48 patients. Data showed in Figure 3A, that across various types of metastatic solid tumors, the co-activation of PDL-1 and 0X40 was 15 % with high differences between tumor types. It is possible that the trial would have benefited from a stratification or post hoc testing of the level of activation of the targets, to avoid an early stopping of the trial.
In light of the in-silico estimate, that aligns with recent clinical trial data, together with other reports, a rational stratification of patients based on the degree of activation of the targets (through genomic and transcriptomic correlations with responses) may allow a better understanding of the results of these preliminary studies. The method reported here is an attempt to offer a global view of all the targets simultaneously.
One of the most encouraging findings from this in silico analysis is the identified fraction of patients who could benefit from a rational stratification and orientation to one of the 10 classes of 10 therapeutic options: 87% of patients with advanced solid tumors (NSCLC, CRC, HN) and up to 60% of primary NSCLC.
A program for testing hypothesis prospectively would be of major interest for patients around the world, in particular those who have exhausted all therapeutic options. The invention offers an existing platform for such endeavor that will require cooperation between academic and pharmaceutical industries. Such studies will enable to compare the real impact of the different biomarker strategies, and inform the most adequate decision support tools to help physicians for a rational treatment of cancer patients.
Table 6: SIMS score allocation to 10 therapy options for 160 patients with advanced metastatic cancers
Figure imgf000044_0001
Figure imgf000045_0001
Figure imgf000046_0001
Figure imgf000046_0002
Figure imgf000047_0001
Figure imgf000048_0001
I
Figure imgf000048_0002

Claims

Claims
1. A method for selecting an optimal immune checkpoint blockade therapy for treating a subject suffering from cancer, wherein the method comprises:
- providing mRNA expression level in a tumor sample and a histologically matched normal sample of a set of genes comprising the following genes or groups of genes: 1) a group of genes comprising PD-1, PD-L1, and optionally PD-L2; 2) a group of genes comprising CTLA-4, CD28, and optionally CD80 and CD86; 3) LAG3; 4) TLR4 and optionally one or more of the following genes 5) VISTA, TIM-3, TIGIT, TGF , ICOS, 0X40 and IDOl; and wherein the tumor and histologically matched normal samples are from the same subject;
- determining a mRNA fold change of Tumor versus Normal (Fc TvN) for each gene of the set of genes; and calculating a mean FcTvN fold change for each group of genes;
- normalizing the Fc TvN fold changes or the mean Fc TvN fold changes for the group of genes;
- ranking the normalized Fc TvN fold changes and the normalized mean Fc TvN fold changes, the highest value of the normalized Fc TvN fold changes and mean Fc TvN fold changes being ranking first and being predictive of a highest efficacy of an immune checkpoint blockade therapy for treating the cancer of the subject; and
- optionally selecting the optimal immune checkpoint blockade therapy based on the ranked genes or group of genes.
2. A method for determining if a subject suffering from a cancer is susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy, wherein the method comprises:
- providing mRNA expression level in a tumor sample and a histologically matched normal sample of a set of genes comprising the following genes or groups of genes: 1) a group of genes comprising PD-1, PD-L1, and optionally PD-L2; 2) a group of genes comprising CTLA-4, CD28, and optionally CD80 and CD86; 3) LAG3; 4) TLR4 and optionally one or more of the following genes 5) VISTA, TIM-3, TIGIT, ICOS, 0X40 and IDOl; and wherein the tumor and histologically matched normal samples are from the same subject;
- determining a mRNA fold change of Tumor versus Normal (Fc TvN) for each gene of the set of genes; and calculating a mean FcTvN fold change for each group of genes;
- normalizing the Fc TvN fold changes or the mean Fc TvN fold changes for the group of genes;
- ranking the normalized Fc TvN fold changes and the normalized mean Fc TvN fold changes, the highest value of the normalized Fc TvN fold changes and mean Fc TvN fold changes being ranking first and being predictive of the susceptibility of the subject to benefit from the treatment with an immune checkpoint blockade therapy; and - optionally selecting the immune checkpoint blockade therapy most susceptible to benefit to the subject based on the ranked genes or group of genes.
3. The method according to claim 1 or 2, wherein the Fc TvN for a particular group of genes is the average or arithmetic mean of the fold changes of genes belonging to the group of genes and having a Fc TvN of 1.3 or more.
4. The method according to any one of claims 1-3, wherein the method further comprises the multiplication of the Fc TvN by expression intensity of the gene (ln), either in the tumor sample (ln T) or in the histologically matched normal sample (ln N).
5. The method according to any one of claims 1-4, wherein the normalization step is performed using a calibrator consisting of Fc TvN for each gene or group of genes of a set of subjects.
6. The method according to any one of claims 1-5, wherein the mRNA expression level in the tumor sample and in the normal histologically matched sample is further corrected with the expression of miRNA targeting the transcript.
7. The method according to any one of claims 1-6, wherein mRNA expression level of other genes is studied in the method but the total number of genes is no more than 16 genes.
8. The method according to any one of claims 1-7, wherein the normalization step is in the form of deciles and the score is from 1 to 10, with 10 being the highest Fc TvN and 1 the lowest one.
9. The method according to claim 8, wherein a score superior to 4 is predictive of an efficacy of an immune checkpoint blockade therapy for treating a cancer in the subject or of a susceptibility of the subject to benefit from an immune checkpoint blockade therapy.
10. The method according to any one of claims 1-9, wherein the method further comprises the selection of the optimal immune checkpoint blockade therapy based on the ranked genes or group of genes, preferably the one, two or three gene(s) or group(s) of genes having the highest scores.
11. The method according to any one of claims 1-10, wherein the immune checkpoint blockade therapy is a monotherapy, a bitherapy or a tritherapy.
12. The method according to any one of claims 1-11, wherein the immune checkpoint blockade therapy is selected from the group consisting of an anti-PD-1 antibody, an anti-PD-Ll antibody, an anti-CTLA-4 antibody, an anti-LAG3 antibody, an anti-TLR4 antibody and any combination thereof, and optionally one or several among an anti-OX40, an anti-IDOl, an anti-TIME3, an anti-TIGIT and an anti-VISTA.
13. The method according to claim 12, wherein the immune checkpoint blockade therapy is selected from the group consisting of pembrolizumab, nivolumab, atezolizumab, durvalumab, avelumab, ipilimumab, NI-0101, BMS-986016, Sym022, GSK2831781, AZ MEDI5752, and optionally AZ MEDI6469, GSK3174998, INCB024360, MBG453, MTIG7192A , CI-8993 and tiragolumab.
14. The method according to any one of claims 1-13, wherein the cancer is selected from the group consisting of prostate cancer, bladder cancer, breast cancer, colon cancer, colorectal cancer, Esophagus cancer, hypopharynx cancer, gastric cancer, rectum cancer, head and neck cancer, liver cancer, brain cancer, hepatocarcinoma, kidney cancer, ovarian cancer, cervical cancer, pancreatic cancer, sarcoma, lung cancer, lymphoma, osteosarcoma, melanoma, neuroendocrine cancer, pleural cancer, Small Intestine cancer, endometrial cancer, soft tissue cancer, non-small cell lung carcinomas (NSCLC), metastatic non-small cell lung cancer, muscle cancer, adrenal cancer, thyroid cancer, uterine cancer, advanced renal cell carcinoma (RCC), and sub ependymal giant cell astrocytoma (SEGA) associated with tuberous sclerosis (TS), preferably colorectal cancer, head and neck cancer, lung cancer, non-small cell lung carcinomas (NSCLC), bladder cancer, breast cancer, esophagus cancer, gastro intestinal cancer, hepatocarcinoma, kidney cancer, leyomiosarcoma, liposarcoma, lymphoma, melanoma, soft tissue cancer, neuroendocrine cancer and rhabdomyosarcoma.
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