Marker combination for predicting therapeutic effect of immune checkpoint inhibitor and application of marker combination

Abstract

The invention belongs to the technical field of in-vitro detection, and particularly relates to a marker combination for predicting the treatment effect of an immune checkpoint inhibitor and application of the marker combination, and the marker combination comprises genes or proteins of INTS6, SRRM2, RASAL3, GTPBP1, JUND, ARHGEF1, BRD2, UNB, TPST2, DEF6, TBC1D10C, IER2, ARHGAP4, PABPC4, CD1D, INPP5D, LAT, GLS, CD1D, SEC24D, LTB, SKAP1, ANKRD12, CD37, CCNL1, PSAP, MCL1, NFKBIZ, IL7R or VCP, or 5, 10, 20 or 30 variant markers having at least 70% of identity with The marker provided by the invention can reflect the tumor microenvironment state, can more accurately predict the curative effect of immune checkpoint inhibitor treatment of a solid tumor patient, can better guide clinical medication, and has a relatively great application prospect.

Description

Technical Field

[0001] The present invention belongs to the technical field of in vitro detection, and particularly relates to a biomarker combination for predicting the therapeutic efficacy of immune checkpoint inhibitors and its application. Background Art

[0002] Immune checkpoint inhibitors are currently the first-line drugs for a variety of advanced solid tumors, including anti-PD-1 monoclonal antibodies, anti-PD-L1 monoclonal antibodies, anti-CTLA-4 monoclonal antibodies, etc. Under normal physiological conditions, immune checkpoint molecules are used to regulate the body's immunity and avoid excessive autoimmune reactions; while in the tumor microenvironment, they are mostly expressed on the surface of immune cells or tumor cells, causing immune cell exhaustion and being exhausted from attacking tumor cells, thus leading to tumor cell immune escape and progression. Immune checkpoint inhibitors have been proven to kill tumor cells by inducing systemic immunity and tumor-specific T cell expansion in a variety of malignant tumors, such as melanoma, breast cancer, lung cancer, and colon cancer. However, as immune checkpoint inhibitors (such as PD-1 / PD-L1 antibodies) are used in more and more different types of cancer patients, researchers have found that immune checkpoint blockade therapy is not effective for all cancer patients. Immune checkpoint inhibitors (such as PD-1 / PD-L1 antibodies) are only effective for a part of patients with each type of cancer. For example, in lung cancer, they are only effective for about 30% of lung cancer patients. If the patient's tumor tissue is PD-L1 positive, the effective rate is further increased to about 50%; while in patients with Hodgkin lymphoma, the effective rate is close to 90%, the effective rate of melanoma reaches 40-50%, the effective rate of bladder cancer is 50%, and the effective rates of digestive tract tumors such as gastric cancer, esophageal cancer, and liver cancer are also about 30%. The different responses of immunotherapy in different tumors may be related to the patient's own disease background and immune microenvironment characteristics. Therefore, using the tumor characteristics of the patient's pretreatment baseline to predict patients who respond to immunotherapy before treatment is beneficial to accurately guiding treatment decisions.

[0003] Currently, there are no biomarkers that are widely applicable to a variety of solid tumors for predicting the efficacy of immune checkpoint inhibitors. The reported biomarkers are mostly related to T cells, such as the proportion of exhausted T cells, PD-1 / PD-L1 expression, etc. However, there are still some patients with less T cell expansion who respond well to immune checkpoint inhibitors. With the development of single-cell level transcriptome sequencing technology, it has been found that in addition to T cells, other immune microenvironment components such as B cells and fibroblasts also play important roles in anti-tumor immunity. For example, B cells can activate T cells by forming tertiary lymphoid structures with T cells and form plasma cells that can secrete anti-tumor antibodies. However, the exploration of the predictive efficacy of the overall immune microenvironment on the efficacy of immune checkpoint inhibitors is very limited. Therefore, the present invention starts from the overall tumor microenvironment and helps to identify patients who may respond to immune checkpoint inhibitor treatment from a new perspective, guiding clinical precision medication. SUMMARY OF THE INVENTION

[0004] The main object of the present invention is to provide a novel biomarker combination and its application in predicting the prognosis of solid tumor patients treated with immune checkpoint inhibitors. By using a clinical research cohort of liver cancer patients treated with immune checkpoint inhibitors, the present invention selects gene biomarkers in a reasonable manner to provide a direction for predicting the prognosis of immune checkpoint inhibitor treatment in patients with various solid tumors.

[0005] Specifically, the present invention provides the following technical solutions: A biomarker combination for predicting responsiveness to immune checkpoint inhibitor treatment, wherein the biomarker combination is: INTS6, SRRM2, RASAL3, GTPBP1, JUND, ARHGEF1, BRD2, UNB, TPST2, DEF6, TBC1D10C, IER2, ARHGAP4, PABPC4, CD1D, INPP5D, LAT, GLS, JUN, SEC24D, LTB, SKAP1, ANKRD12, CD37, CCNL1, PSAP, MCL1, NFKBIZ, IL7R or VCP gene or protein, or 5, 10, 20 or 30 of the variant biomarkers having at least 70% identity to the biomarker gene fragments.

[0006] The information of each biomarker is shown in Table 1 below: Table 1 Information of each biomarker sequence database Marker Full name Database information INTS6 Integrator Complex Subunit 6 NM_001039937 SRRM2 Serine / Arginine Repetitive Matrix 2 NM_016333 RASAL3 RAS Protein Activator Like 3 NM_001348027 GTPBP1 GTP Binding Protein 1 NM_004286 JUND JunD Proto-Oncogene NM_001286968 ARHGEF1 Rho Guanine Nucleotide Exchange Factor 1 NM_001396000 BRD2 Bromodomain Containing 2 NM_001113182 JUNB JunB Proto-Oncogene NM_002229 TPST2 Tyrosylprotein Sulfotransferase 2 NM_001008566 DEF6 DEF6 Guanine Nucleotide Exchange Factor NM_022047 TBC1D10C TBC1 Domain Family Member 10C NM_001256508 IER2 Immediate Early Response 2 NM_004907 ARHGAP4 Rho GTPase Activating Protein 4 NM_001164741 PABPC4 Poly(A) Binding Protein Cytoplasmic 4 NM_001135653 CD1D Antigen-Presenting Glycoprotein CD1d NM_001319145 INPP5D Inositol Polyphosphate-5-Phosphatase D NM_001017915 LAT Linker For Activation Of T Cells NM_001014987 GLS Glutaminase NM_001256310 JUN Jun Proto-Oncogene NM_002228 SEC24D SEC24 Homolog D NM_001318066 LTB Lymphotoxin Beta NM_002341 SKAP1 Src Kinase Associated Phosphoprotein 1 NM_001075099 ANKRD12 Ankyrin Repeat Domain 12 NM_001083625 CD37 Leukocyte Antigen CD37 NM_001040031 CCNL1 Cyclin L1 NM_001308185 PSAP Prosaposin Sphingolipid Activator Protein-1 NM_001042465 MCL1 MCL1 Apoptosis Regulator NM_001197320 NFKBIZ NFKB Inhibitor Zeta NM_001005474 IL7R Interleukin 7 Receptor NM_001410734 VCP Valosin Containing Protein NM_001354927 In some embodiments, the polynucleotide sequence of the biomarker has a sequence with one or more base substitutions, deletions or additions (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more base substitutions, deletions or additions) compared to the sequence shown in Table 1.

[0007] Those skilled in the art are aware that due to the degeneracy of the genetic code, most amino acids are encoded by multiple different codons. The genetic code is the process in living organisms of translating genetic information on DNA or mRNA into proteins, where each amino acid is encoded by one or more specific trinucleotide sequences (codons). Based on this property, different nucleic acid sequences (codons) can be transcribed and translated into the same amino acid.

[0008] In some embodiments, the immune checkpoint includes PD-1, PD-L1 or CTLA-4. In a specific treatment process, to administer an immune checkpoint binding inhibitor to a subject, the inhibitor reduces, blocks, inhibits, eliminates or interferes with signal transduction derived from the interaction between the immune checkpoint and its binding ligand. Specifically, the PD-1, PD-L1 or CTLA-4 binding inhibitor is a molecule that inhibits the binding of PD-1, PD-L1 or CTLA-4 to its binding partner. In some embodiments, the inhibitor includes anti-PD-1, PD-L1 or CTLA-4 antibodies, their antigen-binding fragments, immunoadhesins, fusion proteins, oligopeptides, and other molecules that reduce, block, inhibit, eliminate or interfere with signal transduction generated by the interaction between PD-1, PD-L1 or CTLA-4 and its binding ligand. In some embodiments, the anti-PD-1 antibody can be, for example, tislelizumab ( Tislelizumab ), nivolumab ( nivolumab ), pembrolizumab ( pembrolizumab ), atezolizumab (Tecentriq ), durvalumab ( durvalumab ), or avelumab ( avelumab ), etc.; the anti-PD-L1 antibody can be, for example, Atezolizumab, Durvalumab, or Avelumab, etc.; the anti-CTLA-4 antibody can be, for example, ipilimumab, tremelimumab, abatacept, etc.; the bispecific antibody can be, for example, MEDI5752 (simultaneously targeting PD-1 and CTLA-4), KN046 (simultaneously targeting PD-L1 and CTLA-4), etc.

[0009] As an embodiment, the present invention provides a product for detecting the expression level of the biomarker combination. In some embodiments, the product is an in vitro diagnostic product; preferably, the product includes a kit, reagent, test strip or gene chip. The kit in the present invention has a physical form. For example, the kit can be a container with one or more spaces for accommodating controls, standards, materials or devices for detection reagents. Reagents, tools and / or instructions for performing the methods described herein can be provided in the kit. The kit can also include one or more reagents for performing biomarker expression analysis, such as reagents for performing RT-PCR, qPCR, northern blot, proteomic analysis or immunohistochemistry to determine the expression level of biomarkers in patient samples.

[0010] In other embodiments, the product contains reagents for detecting the expression level of the biomarker combination in a subject sample at the gene level and / or protein level.

[0011] In some other embodiments, the product comprises reagents for detecting the combined expression levels of markers in a subject sample by immunohistochemistry, in situ molecular hybridization histochemistry, Western blotting, Northern blotting, PCR, RT-PCR, or biochip detection techniques; preferably, the immunohistochemistry includes: immunofluorescence analysis, immunoenzymatic assay, or immunogold assay.

[0012] Further, the subject is a mammal, preferably, the subject includes mice, rabbits, pigs, non-human primates, or humans.

[0013] Further, the sample is from a tissue or cell sample of the subject.

[0014] As another embodiment, the present invention provides a computer model for predicting the response to immune checkpoint inhibitor therapy. The model analyzes, identifies, or simulates the expression level data of each marker in the marker combination, and performs mathematical correlation on the data to obtain a score. In one embodiment, the present invention uses the ssGSEA (single-sample gene set enrichment analysis) algorithm model to perform mathematical correlation on the expression level data of each marker gene in the markers of the present invention and obtain a score; in some other alternative embodiments, optionally, the model includes a Cox regression model, a random forest model, a neural network model, a convolutional neural network (CNN), a recurrent neural network (RNN) model, etc.

[0015] Further, the present invention provides the application of the marker combination, the product according to any one of the above, and the computer model in any of the following: a1) Preparing a product for predicting the response of a solid tumor patient to immune checkpoint inhibitor therapy; a2) Preparing a product for prognostic evaluation of a solid tumor patient's response to immune checkpoint inhibitor therapy; a3) Preparing a product for enriching the population of solid tumor patients who respond to immune checkpoint inhibitor therapy; a4) Preparing a product for analyzing the survival rate of a solid tumor patient's response to immune checkpoint inhibitor therapy; a5) Preparing a product for stratifying subjects of a solid tumor patient's response to immune checkpoint inhibitor therapy.

[0016] Further, in some embodiments, the solid tumor is a solid tumor that has been guided to use immune checkpoint inhibitors in clinical applications. Preferably, the solid tumor includes liver cancer, melanoma, breast cancer, lung cancer, non-small cell lung cancer, or esophageal cancer. The solid tumors that have been guided to use immune checkpoint inhibitors in clinical applications as referred to in the present invention can, in specific applications, refer to the types of solid tumors for which immune inhibitors can be used as clearly recorded in guidelines published by authoritative institutions such as the "Clinical Application Guidelines for Immune Checkpoint Inhibitors" or the "Diagnosis and Treatment Guidelines for Tumor Drug-Related Liver Injury".

[0017] In one embodiment, the present invention provides a method for predicting a subject's response to immune checkpoint inhibitor treatment for non-diagnostic and non-therapeutic purposes, comprising the following steps: S1: Detect, extract, or identify the expression level data of the biomarker combination; S2: Provide the data as input data to the model; output a score; S3: Compare the score with a threshold value. If it is higher than the threshold value, the patient benefits from the treatment; if it is lower than the threshold value, the patient does not benefit from the treatment.

[0018] In one embodiment, the present invention provides a computer program product comprising a computer program / instructions, and when the computer program / instructions are executed, the method described above is implemented or the model is used.

[0019] In one embodiment, the present invention provides a computer device / system comprising a processor and a memory, and the computer program product that can run on the processor is stored on the memory.

[0020] In one embodiment, the present invention provides a computer-readable storage medium that stores the computer program product.

[0021] The present invention has the following beneficial effects: Among the current predictive biomarkers for the efficacy of immune checkpoint inhibitor treatment, there is no biomarker proposed for the overall immune microenvironment. The present invention provides a molecular biomarker composed of 30 genes such as INTS6, SRRM2, and RASAL3. The biomarker can reflect the state of the tumor microenvironment, can more accurately predict the efficacy of immune checkpoint inhibitor treatment for solid tumor patients, better guide clinical medication, and has great application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1. Results of the correlation between the molecular marker combination of the present invention and the efficacy of neoadjuvant anti-PD-1 monoclonal antibody therapy in 17 liver cancer patients in the training cohort of the present invention. The results showed that the molecular marker score before treatment was higher in patients with good prognosis during anti-PD-1 monoclonal antibody treatment than in patients with poor prognosis during treatment. Among them, Non-progress is the group with good treatment prognosis; Progress is the group with poor treatment prognosis.

[0023] Figure 2 . Results of the correlation between the molecular marker of the present invention and the efficacy of anti-PD-1 monoclonal antibody therapy in melanoma patients (validation set 1). The results showed that the molecular marker score before treatment was higher in patients with a response to anti-PD-1 monoclonal antibody treatment than in patients without a response. Among them, Non-progress is the treatment response group; Progress is the treatment non-response group.

[0024] Figure 3 . Results of the correlation between the molecular marker of the present invention and the efficacy of anti-PD-1 monoclonal antibody therapy in breast cancer patients (validation set 2). The results showed that the molecular marker score before treatment was higher in patients with a response to anti-PD-1 monoclonal antibody treatment than in patients without a response. Among them, Efficacy is the treatment response group; Non-efficacy is the treatment non-response group.

[0025] Figure 4 . Results of the correlation between the molecular marker of the present invention and the efficacy of anti-PD-1 monoclonal antibody or anti-CTLA4 therapy in melanoma patients (validation set 3). The results showed that the molecular marker score before treatment was higher in patients with a response to treatment than in patients without a response. Among them, Response is the treatment response group; Non-response is the treatment non-response group.

[0026] Figure 5 . Results of the correlation between the molecular marker of the present invention and the efficacy of anti-PD-1 monoclonal antibody therapy in non-small cell lung cancer patients (validation set 4). The results showed that the molecular marker score before treatment was higher in patients with a response to anti-PD-1 monoclonal antibody treatment than in patients without a response. Among them, Response is the treatment response group; Non-response is the treatment non-response group.

[0027] Figure 6 . Results of the correlation between the molecular marker of the present invention and the efficacy of anti-PD-1 monoclonal antibody combined with chemotherapy in esophageal cancer patients (validation set 5). The results showed that the molecular marker score before treatment was higher in patients with a response to treatment than in patients without a response. Among them, Responder is the treatment response group; Non-responder is the treatment non-response group.

[0028] Figure 7Analysis diagram of the predictive efficacy of the molecular marker of the present invention for anti-PD-1 monoclonal antibody or anti-CTLA4 treatment in melanoma patients (validation set 3). Among them, (A) is the overall survival (OS) analysis, and (B) is the progression-free survival (PFS) analysis; the K-M curve shows that the PFS and OS times of patients with high marker scores are significantly longer than those of patients with low scores.

[0029] Figure 8 Analysis diagram of the predictive efficacy of the molecular marker of the present invention for anti-PD-1 monoclonal antibody in non-small cell lung cancer patients (validation set 4). Among them, (A) is the progression-free survival (PFS) analysis, and (B) is the overall survival (OS) analysis; the K-M curve shows that the PFS and OS times of patients with high marker scores are significantly longer than those of patients with low scores. Detailed implementation manners

[0030] The following will clearly and completely describe the concept and technical effects generated by this application in combination with the embodiments to fully understand the purpose, features, and effects of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all embodiments. Based on the embodiments of this application, other embodiments obtained by those skilled in the art without creative efforts all fall within the scope of protection of this application.

[0031] For the purchased commodities in the test methods, if not specified in the text, they are carried out according to the conventional conditions or the conditions recommended by the manufacturer. For the reagents or instruments used without indicating the manufacturer, they are all conventional products that can be obtained through commercial purchase.

[0032] In the present invention, unless otherwise specified, the scientific and technical terms used herein have the meanings commonly understood by those skilled in the art. Moreover, the operation steps such as genomics, proteomics, immunohistochemistry, flow cytometry, molecular biology, biochemistry, cell culture, and cell biology used herein are all conventional steps widely used in the corresponding fields. At the same time, to better understand the present invention, the definitions and explanations of relevant terms are provided below.

[0033] The term "solid tumor" refers to a benign or malignant tumor with solid tissue formed by abnormal proliferation of normal cells, or can also be a tangible mass detected by clinical examinations (such as B-ultrasound, X-ray radiography, CT scan, or palpation), including but not limited to lung cancer (such as non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC)), melanoma, breast cancer, colon cancer, colorectal cancer, gastric cancer, pancreatic cancer, esophageal cancer, kidney cancer, bladder cancer, prostate cancer, ovarian cancer, endometrial cancer, thyroid cancer, skin cancer, nasopharyngeal cancer, laryngeal cancer, or sarcoma, etc.

[0034] The term "subject" includes patients and non - patients. "Subject" and "individual" are used interchangeably. The term "patient" refers to an individual who has or may have a medical condition such as an inflammation or inflammatory disease, while a "non - patient" refers to an individual who does not have and is not likely to have the medical condition. "Non - patients" include healthy individuals, individuals without disease, and / or individuals without the medical condition. In some embodiments, the term "subject" includes mammals, specifically humans and animals. Animals include rodents, rabbits, pigs, non - human primates, etc. "Rodent" refers to any mammal from the family Muridae, such as mice, rats, etc.

[0035] The terms "sample", "biological sample", and "test sample" are used interchangeably herein and refer to any material, biological fluid, tissue, or cell obtained from or otherwise derived from an individual, such as tissue samples, cell samples, etc. The source of a tissue sample or cell sample can be solid tissue such as from fresh, frozen, and / or preserved organ tissue samples, biopsies, and / or aspirates. Tissue samples can contain compounds that are not naturally commingled with the tissue in nature, such as preservatives, anticoagulants, buffers, fixatives, etc.

[0036] The terms "marker" and "biomarker" are used interchangeably herein and refer to DNA, RNA, protein, polypeptide, carbohydrate, glycolipid, or cell - based molecular markers whose expression or presence in a subject's sample can be detected by standard methods (or methods disclosed herein). Such biomarkers include, but are not limited to, the genes or proteins claimed in the present invention.

[0037] The term "expression level" is generally used interchangeably with "level of expression", "amount of expression", or "expression information" and generally refers to the amount of a biomarker in a biological sample. "Expression" generally refers to the process by which information (such as genetic coding and / or epigenetic information) is transformed into a structure that exists and functions in a cell. Thus, as used herein, "expression" can refer to transcription into polynucleotides, translation into polypeptides, or even modification of polynucleotides and / or polypeptides (such as post - translational modification of polypeptides). Fragments of transcribed polynucleotides, translated polypeptides, or modified polynucleotides and / or polypeptides (such as post - translational modification of polypeptides) should also be considered expressed, whether they are derived from transcripts generated by alternative splicing or degraded transcripts, or from post - translational processing of polypeptides, such as by proteolysis. "Expressed genes" include those that are transcribed into polynucleotides as mRNA and then translated into polypeptides, as well as those that are transcribed into RNA but not translated into polypeptides (such as transfer and ribosomal RNA).

[0038] The term "detect" includes any means of detection, including direct or indirect detection.

[0039] The terms "threshold" and "cut-off value" are used interchangeably and refer to a value determined for a specific variable value, where the value gives a certain meaning when the change value is greater than or less than the value.

[0040] The term "score" refers to a numerical value that reflects the expression level of a combination of markers related to the response to treatment. The expression level of the markers can be determined by statistical methods known to those skilled in the art and also disclosed herein, which include, for example, calculating the median or mean of the expression levels of all markers. In some cases, each marker may have a designated weight score, and the mean of the weighted expression levels of all markers can be determined by combining the weight scores to obtain a score for the expression levels of multiple markers. The score for the expression levels of the markers is compared with the expression level before weighting to make, for example, diagnostic, predictive, prognostic, and / or treatment decisions. And / or a pre-specified value (e.g., a threshold previously determined to meet the following conditions: the threshold is based on a significant difference above and / or below the threshold between the responsiveness of an individual to immunotherapy checkpoint inhibitor treatment and the responsiveness of the individual to non-immunotherapy checkpoint inhibitor treatment, such as significantly (e.g., statistically significantly) distinguishing a first subset of patients who have received treatment with an immunotherapy checkpoint inhibitor (such as an anti-PD-1 antibody) from a second subset of patients who have received treatment without an immunotherapy checkpoint inhibitor (such as an anti-PD-1 antibody) in the same reference population, where the responsiveness of the patients to immunotherapy checkpoint inhibitor (such as an anti-PD-1 antibody) treatment is significantly (e.g., statistically significantly) improved relative to the responsiveness of the patients to non-immunotherapy checkpoint inhibitor (such as an anti-PD-1 antibody) treatment above the threshold, and / or the responsiveness of the patients to non-immunotherapy checkpoint inhibitor (such as an anti-PD-1 antibody) treatment is significantly (e.g., statistically significantly) improved relative to the responsiveness of the patients to anti-PD-1 antibody treatment below the threshold. Those skilled in the art will recognize that the numerical values of the reference marker expression levels can vary depending on the indication (e.g., breast cancer, lung cancer, kidney cancer, or bladder cancer), the method used to detect the expression level (e.g., immunohistochemistry or RT-qPCR, etc.), and the statistical method used to generate the score.

[0041] The terms "response" or "efficacy" or "benefit" should be used interchangeably and can be evaluated using any endpoint indicating a benefit to an individual, including but not limited to: (1) inhibiting the progression of cancer to a certain extent, including slowing down and completely blocking; (2) reducing the tumor size; (3) inhibiting (i.e., reducing, slowing down, or completely terminating) the invasion of cancer cells into adjacent surrounding organs and / or tissues; (4) inhibiting (i.e., reducing, slowing down, or completely terminating) metastasis; (5) reducing one or more symptoms related to cancer to a certain extent; (6) extending the length of survival (including overall survival and progression-free survival); and / or reducing the mortality rate at a given time point after treatment.

[0042] The term "antibody" as used herein is used in the broadest sense and encompasses various antibody structures, including but not limited to monoclonal antibodies, polyclonal antibodies, multispecific antibodies (e.g., bispecific antibodies), and antibody fragments, so long as they exhibit the desired antigen-binding activity.

[0043] The term "monoclonal antibody (abbreviation: mAb)" refers to an antibody obtained from a substantially homogeneous population of antibodies, i.e., aside from possible variant antibodies (e.g., containing naturally occurring mutations or arising during the production of the monoclonal antibody preparation, such variants typically being present in small amounts), the individual antibodies comprising the population are identical and / or bind the same epitope. In contrast to polyclonal antibody preparations, which generally include different antibodies directed against different determinants (epitopes), each monoclonal antibody in a monoclonal antibody preparation is directed against a single determinant on an antigen. Thus, the modifier "monoclonal" indicates that the antibody is characterized by being obtained from a substantially homogeneous population of antibodies and should not be construed as requiring that the antibody be made by any particular method. For example, monoclonal antibodies useful in accordance with the present invention can be prepared by a variety of techniques, including but not limited to the hybridoma method, recombinant DNA methods, phage display methods, and methods using transgenic animals that contain all or part of the human immunoglobulin locus.

[0044] The term "treatment" refers to a clinical intervention that attempts to alter the natural course of a subject being treated and can be for prophylaxis or during the course of a clinical pathology. Desired therapeutic effects include but are not limited to the prevention or occurrence or recurrence of a disease, such as lung cancer, liver cancer, melanoma, breast cancer, etc., alleviation of symptoms, reduction of any direct or indirect pathological consequences of the disease, prevention of metastasis, reduction in the rate of disease progression, improvement or alleviation of the condition, and remission or improvement of the prognosis.

[0045] The term "subject stratification" refers to a surrogate marker that can distinguish patient populations with the same diagnosis but different natural courses of the disease without treatment intervention. By using the marker, subjects are stratified according to the differential responses of the marker, thereby reducing the heterogeneity of the subjects and the interference of confounding factors on the test results, reducing bias between groups, and improving the reliability of the results.

[0046] The term "enriched population" refers to a marker that can help screen out a patient population that is more likely to experience the clinical event of interest. Therefore, in conducting a clinical trial with a clinical event as the endpoint, the marker is used to screen and enrich the high-benefit population.

[0047] The present invention utilizes a prospective neoadjuvant anti-PD-1 monoclonal antibody therapy clinical research cohort consisting of 17 hepatocellular carcinoma patients, collects paired tumor samples from the 17 patients before and after treatment, prepares them into single-cell suspensions, and then performs single-cell transcriptome sequencing. The transcriptome sequencing data at the single-cell level is convolved into the sample level to obtain the transcriptome information of the overall immune microenvironment. According to the prognosis of the 17 patients during anti-PD-1 monoclonal antibody treatment (whether there is tumor progression or recurrence during the 1-year treatment period), they are divided into a good prognosis group and a poor prognosis group. The transcriptome data of the overall immune microenvironment of the two groups of patients are compared to obtain the genes highly expressed specifically in the responsive group of patients. Further, by screening the AUC values for efficacy prediction in 17 liver cancer patients, genes with AUC > 0.6 are selected. Finally, 30 genes such as INTS6, SRRM2, and RASAL3 are obtained, and the gene set scores are calculated using the ssGSEA method to obtain the prediction model scores.

[0048] Further verification in the immunotherapy checkpoint inhibitor treatment cohorts of multiple cancer types shows that the gene set scores can be used to predict the efficacy of immunotherapy checkpoint inhibitor treatment in solid tumor patients, that is, the data is mathematically correlated in the corresponding cohorts to obtain scores, and the scores of patients responsive to immunotherapy checkpoint inhibitor treatment are significantly higher than those of non-responsive patients.

[0049] The following describes exemplary methods and materials, but methods and materials similar or equivalent to those described herein can also be used in the practice and testing of the present invention disclosure: Example 1 1. Data collection In the example, we selected 17 liver cancer patients as the training set (from the First Affiliated Hospital of Sun Yat-sen University, cohort 1). These patients were subjects of a phase II prospective clinical study (study registration number: NCT04615143). The inclusion criteria were: 1) clinically evaluated as surgically resectable by experts; 2) recurrence after radical treatment; 3) the recurrent lesions had not received systemic treatment or local treatment in the past; 4) there was at least 1 intrahepatic lesion that could be evaluated by the RECIST 1.1 standard, that is, the lesion diameter > 1 cm; 5) no extrahepatic metastasis or vascular invasion.

[0050] The treatment plan for the patients was as follows: After passing the screening and signing the informed consent form, percutaneous biopsy of the lesion was performed under ultrasound guidance. After the biopsy, the patients would receive 2 cycles of neoadjuvant anti-PD-1 monoclonal antibody treatment, 1 cycle every 3 weeks; at the 6th week after enrollment, the patients would undergo surgery to radically resect the lesion. One month after the operation, after the patients had good liver and kidney function and good surgical healing upon reexamination, they would receive adjuvant treatment for 1 year, and the treatment medications were the same as before surgery.

[0051] After the tumor puncture tissue of the enrolled patients is separated into single-cell suspensions in tissue dissociation enzymes, it is used for single-cell sequencing to obtain transcriptome data at the single-cell level. After mixing the single-cell transcriptome data of 17 patients, intratumoral immune cells and non-immune cells are obtained according to unsupervised clustering, and part of the immune cell data is convolved into transcriptome data at the sample level using the Pseudobulk function.

[0052] 2. Screening and Application of Predictive Model Genes In cohort 1, 17 patients were labeled with the prognosis of patients during the perioperative anti-PD-1 monoclonal antibody treatment, and the patients were divided into a good prognosis group and a poor prognosis group. The good prognosis group means that the tumor did not progress or recur from enrollment to 1 year after surgery; the poor prognosis group means that the tumor recurred or progressed from enrollment to 1 year after surgery. The gene expression profiles of the two groups were screened for genes using the following rules: (1) The p-value in the differential test is less than 0.05; (2) The fold change between the two groups is greater than 2; (3) The AUC of the gene is greater than 0.6.

[0053] Further, they were sorted in descending order of the fold change in expression between groups, and the top 30 genes with the highest expression levels in the response group were selected as the biomarker combination, namely INTS6, SRRM2, RASAL3, GTPBP1, JUND, ARHGEF1, BRD2, UNB, TPST2, DEF6, TBC1D10C, IER2, ARHGAP4, PABPC4, CD1D, INPP5D, LAT, GLS, JUN, SEC24D, LTB, SKAP1, ANKRD12, CD37, CCNL1, PSAP, MCL1, NFKBIZ, IL7R, VCP.

[0054] Furthermore, the above biomarker combination was scored for each patient using the ssGSEA method, and the biomarker scores of the response group and non-response group patients were compared in cohort 1 ( Figure 1 ) The results showed that the score of this biomarker combination in the good prognosis group of patients in cohort 1 (validation set) was significantly higher than that in the poor prognosis group. It is suggested that this biomarker combination can effectively distinguish the efficacy of anti-PD-1 monoclonal antibody treatment in liver cancer patients.

[0055] 3. Predictive Efficacy of Gene Biomarkers in the Treatment of Immune Checkpoint Inhibitors in Other Solid Tumors We collected cohorts of patients with different solid tumors who received immune checkpoint inhibitor therapy. The tumor sequencing data of the patients before treatment in each cohort were separately standardized, and using the above steps, the expression scores of 30 gene marker combinations were calculated in the expression matrix of each cohort by the ssGSEA method. The patients were divided into a treatment response group and a non-response group according to the degree of tumor shrinkage after treatment. A tumor size reduction of 30% or more was considered a treatment response, otherwise it was a non-response. The scores were compared between the two groups of patients, and the results are as Figures 2 - 6 . The results suggest that this gene marker combination has good predictive efficacy in different types of solid tumors and different immune checkpoint inhibitor therapies, that is, there is a statistically significant difference in the scores between the two groups using the Wilcox statistical method, that is, p < 0.05. The detailed information of the cohorts is as follows: Table 2 Predictive effects of 30 marker combinations in different solid tumor cohorts Cohort number Solid tumor type Treatment method Number of cases Sequencing data set AUC Training set Hepatocellular carcinoma Anti-PD-1 monoclonal antibody 17 / 0.893 Validation set 1 Melanoma Anti-PD-1 monoclonal antibody 121 31792460 0.610 Validation set 2 Triple-negative breast cancer Anti-PD-1 monoclonal antibody 11 EGAS00001004809 0.958 Validation set 3 Melanoma Anti-PD-1 / CTLA-4 monoclonal antibody 73 PRJEB23709 0.730 Validation set 4 Non-small cell lung cancer Anti-PD-1 monoclonal antibody 16 GSE126044 0.945 Validation set 5 Esophageal cancer Anti-PD-1 monoclonal antibody, chemotherapy 40 / 0.714 Table 3 Predictive effects of different marker combinations in different solid tumor cohorts

[0056] In Table 3, Marker combination 1: INTS6, BRD2, PABPC4, GLS, CCNL1 Marker combination 2: INTS6, SRRM2, BRD2, JUNB, PABPC4, GLS, SEC24D, CCNL1, NFKBIZ, VCP Marker combination 3: INTS6, SRRM2, GTPBP1, JUND, BRD2, TPST2, TBC1D10C, IER2, CD1D, INPP5D, LAT, GLS, SEC24D, LTB, SKAP1, ANKRD12, CD37, CCNL1, NFKBIZ, VCP 4. Predictive efficacy of gene markers in the prognosis of patients with solid tumors treated with immune checkpoint inhibitors Further, in cohorts 4 and 5, the patients were ranked from high to low according to the gene marker scores, and using the median of each cohort as the cut-off point, the patients were divided into two groups with high predictive scores and low predictive scores. The progression-free survival (PFS) and overall survival (OS) of the two groups of patients were compared using the Log-rank test, and the results are as Figures 7 - 8 . The results suggest that this gene marker combination still has good predictive efficacy for the long-term prognosis of solid tumor immune checkpoint inhibitor therapy, that is, the PFS and OS of patients with scores higher than the median are significantly better than those of patients with scores lower than the median (p < 0.05).

[0057] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A marker combination for predicting response to immune checkpoint inhibitor therapy, characterized in that: The marker combination is: INTS6, SRRM2, RASAL3, GTPBP1, JUND, ARHGEF1, BRD2, UNB, TPST2, DEF6, TBC1D10C, IER2, ARHGAP4, PABPC4, CD1D, INPP5D, LAT, GLS, JUN, SEC24D, LTB, SKAP1, ANKRD12, CD37, CCNL1, PSAP, MCL1, NFKBIZ, IL7R or VCP genes or proteins, or 5, 10, 20 or 30 of the variant markers having at least 70% identity with the marker gene fragments.

2. The marker combination according to claim 1, characterized in that: The immune checkpoints include PD-1, PD-L1 or CTLA-4; preferably, the immune checkpoint inhibitors include anti-PD-1 antibodies, anti-PD-L1 antibodies or anti-CTLA-4 antibodies.

3. A product for predicting response to immune checkpoint inhibitor therapy, characterized in that The product is a product for detecting the expression level of any one of the marker combinations described in claims 1-2.

4. The product according to claim 3, characterized in that The product is an in vitro diagnostic product; preferably, the product includes a kit, a reagent, a test paper or a gene chip.

5. The product according to claim 3, characterized in that The product comprises reagents for detecting the expression level of a marker combination in a sample of a subject at the gene level and / or protein level.

6. The product according to claim 3, characterized in that The product comprises reagents for detecting the expression level of the marker combination in the subject's sample by immunohistochemistry, in situ molecular hybridization histochemistry, Western blotting, Northern blotting, PCR, RT-PCR or biochip detection technology; preferably, the immunohistochemistry method includes: immunofluorescence analysis, immunoenzyme labeling method or immunocolloidal gold method.

7. The product according to claim 5 or 6, characterized in that The subject is a mammal, preferably, the subject includes mice, rabbits, pigs, non-human primates or humans.

8. The product according to claim 7, characterized in that The sample is a tissue or cell sample from a subject.

9. A computer model for predicting response to immune checkpoint inhibitor therapy, characterized in that The model analyzes, identifies or simulates the expression level data of each marker in the marker combination of claim 1, and mathematically associates the data to obtain a score.

10. Use of the marker combination according to any one of claims 1 to 2, or the product according to any one of claims 3 to 8, or the computer model according to claim 9 in any of the following: a1) Prepare products for predicting the response of patients with solid tumors to immune checkpoint inhibitors; a2) Preparation of products for prognostic assessment of patients with solid tumors responding to immune checkpoint inhibitor therapy; a3) Preparation of products for enrichment of solid tumor patients responding to immune checkpoint inhibitors; a4) Preparation of products for analysis of survival of patients with solid tumors treated with immune checkpoint inhibitors; a5) Prepare products for stratifying patients with solid tumors for response to immune checkpoint inhibitor therapy.

11. The use according to claim 10, characterized in that: The solid tumor is a solid tumor that has been guided to be treated with immune checkpoint inhibitors in clinical applications. Preferably, the solid tumor includes liver cancer, melanoma, breast cancer, lung cancer, non-small cell lung cancer or esophageal cancer.

12. A method for predicting a subject's response to immune checkpoint inhibitor therapy for non-diagnostic and non-therapeutic purposes, characterized in that: The following steps are involved: S1: Detecting, extracting or identifying the expression level data of any marker combination of claims 1-2; S2: providing the data as input data to the model according to claim 9; outputting a score; S3: The score is compared with a threshold. If the score is higher than the threshold, the patient benefits from the treatment; if the score is lower than the threshold, the patient does not benefit from the treatment.

13. A computer program product comprising a computer program / instructions, characterized in that The computer program / instructions implement the method of claim 12 or use the model of claim 11 when executed.

14. A computer device / system comprising a processor and a memory, characterized in that: The memory stores the computer program product of claim 13 which can be executed on the processor.

15. A computer storable medium, characterized in that The computer-readable storage medium stores the computer program product of claim 13 .

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