Biomarkers for predicting therapeutic response involved in immune cell therapy

Abstract

To provide compositions, kits and methods for predicting patients' therapeutic response involved in immune cell therapy.SOLUTION: The gene signature and gene marker according to one aspect are remarkably excellent in predicting a patient's therapeutic response involved in immune cell therapy agents, and therefore, it is possible to select a patient group predicted to show therapeutic effects and administer appropriate treatment, thereby reducing patient pain and costs.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a biomarker for predicting therapeutic response to an immune cell therapy agent. [Background technology]

[0002] Immune anticancer drugs are anticancer drugs that have a mechanism for activating suppressed immune cells in the human body and killing cancer cells. They are third-generation anticancer drugs that improve on the side effects of first-generation chemical anticancer drugs and the resistance of second-generation targeted anticancer drugs.

[0003] An immune checkpoint inhibitor is an immune anticancer agent that shows innovative clinical effects in various cancers, and the immune checkpoint inhibitor induces T cell activation by suppressing the binding of PD-L1, which is expressed as an immune evasion mechanism in cancer cells and tumor-surrounding cells, to PD-1, which is expressed in T cells. Currently, the immune checkpoint inhibitor is being actively developed and clinically studied for more than 10 cancers, including brain tumors, melanoma, gastric cancer, urothelial cell carcinoma, head and neck cancer, liver cancer, and ovarian cancer, and the development of a biomarker for predicting the therapeutic response to the immune checkpoint inhibitor is urgently required for preventing wasteful medical expenses and for the correct use of the immune checkpoint inhibitor.

[0004] Gliomas are the most common and difficult-to-treat primary malignant brain tumors, and despite current standard treatments including surgery, chemotherapy, and radiation therapy, the survival time is known to be only about one year.

[0005] Currently, much research is being conducted on the effects of somatic mutations, such as IDH1, ATRX, EGFR, and CIC mutations, on the prognosis of glioma. Previous studies have shown the possibility that immune cell therapy may be able to effectively extend survival in malignant glioma. However, although the presence or absence of such mutations is used as a simple indicator for predicting prognosis, it is often lacking as a potential therapeutic target and predictor of therapeutic response.

[0006] Therefore, it is important to select discriminatory genes that can predict therapeutic targets and therapeutic responses in relation to immune cell therapy agents, and there is a great need for methods to accurately evaluate and reflect factors related to clinical responses and prognosis, and prediction of therapeutic prognosis according to cancer progression, and various companies, pharmaceutical companies, and large hospitals are making efforts to overcome the above-mentioned limitations. In addition, in order to expect effective immune anticancer drug therapy, there is a need to develop biomarkers including tumor microenvironment genes.

[0007] The present inventors have solved the above-mentioned problems by analyzing expression data of brain tumor patients who underwent immune cell therapy and selecting biomarkers related to treatment prognosis. Summary of the Invention [Problem to be solved by the invention]

[0008] One aspect provides a composition for predicting therapeutic responsiveness of a patient who has undergone immune cell therapy, comprising a preparation capable of measuring the expression level of mRNA or protein of one or more genes selected from the group consisting of APP, C3, CASP1, CD33, CD3E, CD5, CD86, F13A1, FCGR2A, HLA-DRA, HLA-G, ICAM2, IL10, IL10RA, IL12RB2, IL18, IL1B, IL34, IL7, IL7R, INPP5D, ITGA1, JAM3, LAMP3, LY96, NFKB1, NLRP3, NOTCH1, PDCD1, PECAM1, PSMB8, PTPRC, SAA1, SELPLG, ST6GAL1, STAT4, TICAM2, TLR1, TNFRSF18, TNFSF4, and TREM2. Another aspect provides a kit for predicting the therapeutic response of a patient receiving immune cell therapy comprising the composition.

[0009] Yet another embodiment provides a method for providing information for predicting a patient's therapeutic response, comprising the steps of: measuring an expression level of mRNA or a protein thereof of one or more genes selected from the group consisting of APP, C3, CASP1, CD33, CD3E, CD5, CD86, F13A1, FCGR2A, HLA-DRA, HLA-G, ICAM2, IL10, IL10RA, IL12RB2, IL18, IL1B, IL34, IL7, IL7R, INPP5D, ITGA1, JAM3, LAMP3, LY96, NFKB1, NLRP3, NOTCH1, PDCD1, PECAM1, PSMB8, PTPRC, SAA1, SELPLG, ST6GAL1, STAT4, TICAM2, TLR1, TNFRSF18, TNFSF4, and TREM2 from a biological sample obtained from the patient; and predicting a therapeutic response to an immune cell therapy agent based on the measured expression level of the mRNA or the protein thereof. [Means for solving the problem]

[0010] One aspect provides a composition for predicting therapeutic responsiveness of a patient who has undergone immune cell therapy, comprising a preparation capable of measuring the expression level of mRNA or protein of one or more genes selected from the group consisting of APP, C3, CASP1, CD33, CD3E, CD5, CD86, F13A1, FCGR2A, HLA-DRA, HLA-G, ICAM2, IL10, IL10RA, IL12RB2, IL18, IL1B, IL34, IL7, IL7R, INPP5D, ITGA1, JAM3, LAMP3, LY96, NFKB1, NLRP3, NOTCH1, PDCD1, PECAM1, PSMB8, PTPRC, SAA1, SELPLG, ST6GAL1, STAT4, TICAM2, TLR1, TNFRSF18, TNFSF4, and TREM2.

[0011] As used herein, the term "gene" can refer to any nucleic acid sequence, or a portion thereof, that has a functional role in coding for a protein or in transcription or in regulating the expression of other genes. The gene may consist of all the nucleic acid that codes for a functional protein or only a portion of the nucleic acid that codes for or expresses a protein. The nucleic acid sequence may also include genetic abnormalities within exons, introns, initiation or termination regions, promoter sequences, other regulatory sequences, or unique sequences adjacent to the gene.

[0012] In one embodiment, the gene may be one or more selected from the group consisting of APP, C3, CASP1, CD33, CD3E, CD5, CD86, F13A1, FCGR2A, HLA-DRA, HLA-G, ICAM2, IL10, IL10RA, IL12RB2, IL18, IL1B, IL34, IL7, IL7R, INPP5D, ITGA1, JAM3, LAMP3, LY96, NFKB1, NLRP3, NOTCH1, PDCD1, PECAM1, PSMB8, PTPRC, SAA1, SELPLG, ST6GAL1, STAT4, TICAM2, TLR1, TNFRSF18, TNFSF4, and TRME2.

[0013] In one embodiment, the genes may be one or more selected from the group consisting of CD5, CD86, F13A1, HLA-DRA, ICAM2, IL10, IL10RA, IL12RB2, IL34, IL7, IL7R, ITGA1, PECAM1, TNFRSF18, TNFSF18, and TNFSF4.

[0014] In one embodiment, the gene may include TNFSF4, TNFRSF18, IL12RB2, or a combination thereof. Desirably, the gene may include TNFSF4, TNFRSF18, and IL12RB2.

[0015] In one embodiment, the genes can be CD5, CD86, F13A1, HLA-DRA, ICAM2, IL10, IL10RA, IL12RB2, IL34, IL7, IL7R, ITGA1, PECAM1, TNFRSF18, TNFSF18, and TNFSF4.

[0016] The gene may be involved in one or more functions selected from the group consisting of pathogen defense, interleukins, leukocyte function, chemokines, regulation, cytokines, T cell function, B cell function, cytotoxicity, toll-like receptor (TLR), TNF superfamily, antigen processing, NK cell function, microglial function, cell cycle, senescence, adhesion, complement, transporter function, cell function, degenerative disease association function, neuro-inflammation, inflammasome pathway, and macrophage function.

[0017] In one specific example, the composition contains a gene involved in the function, thereby making it possible to predict a patient's immune responsiveness to an anticancer drug and to predict the prognosis of treatment.

[0018] As used herein, the term "immunotherapy" refers to a cancer therapy that activates immune cells in the human body to kill cancer cells, and may refer to a drug that exerts a therapeutic effect on cancer through enhancing the patient's own immunity. In one embodiment, the immunotherapy may be an immune cell therapy.

[0019] As used herein, the term "immune checkpoint inhibitor" may refer to an immune anticancer agent that activates T cells to attack cancer cells by blocking the activation of immune checkpoint proteins involved in T cell suppression, such as proteins such as PD-L1 expressed in tumor cells.

[0020] In one embodiment, the immune cell therapeutic agent may be any one or more selected from the group consisting of NK cell therapeutic agents, T cell therapeutic agents, CAR immune cell therapeutic agents, DC vaccines, CTL therapeutic agents, anti-PD-L1, anti-PD-1, and anti-CTLA-4, for example, an NK cell therapeutic agent.

[0021] In one embodiment, the CAR immune cell therapy may be a CART (chimeric antigen receptor-T) or CAR-NK (chimeric antigen receptor-NK) cell therapy.

[0022] As used herein, the term "CAR (chimeric antigen receptor)" may refer to an artificial membrane-bound protein that induces immune cells against an antigen, stimulates the immune cells, and kills cells that present the antigen.

[0023] As used herein, the term "DC vaccine" may refer to a cell therapy comprising dendritic cells, and the term "CTL therapy" may refer to a cell therapy comprising activated cytotoxic T lymphocytes (CTLs).

[0024] In one embodiment, the immune cell therapeutic agent may inhibit the binding of PD-L1 to PD-1. Specifically, the immune cell therapeutic agent may be an antibody capable of binding to PD-L1 or PD-1, for example, a monoclonal antibody, which may be a human antibody, a humanized antibody, or a chimeric antibody.

[0025] In said composition, said patient may also be a cancer patient or a tumor patient, and said tumor may be malignant or benign.

[0026] The aforementioned "cancer" or "tumor" refers to a condition in which a problem occurs in the regulatory functions of normal cell division, differentiation, and death, leading to abnormal and excessive proliferation, infiltrating surrounding tissues and organs to form masses, and destroying or deforming existing structures.

[0027] In one embodiment, the cancer may be any one selected from the group consisting of microcell lung cancer, small cell lung cancer, melanoma, Hodgkin's lymphoma, gastric cancer, urothelial cell carcinoma, head and neck cancer, liver cancer, colon cancer, prostate cancer, pancreatic cancer, liver cancer, testicular cancer, ovarian cancer, endometrial cancer, cervical cancer, bladder cancer, brain cancer, breast cancer, and kidney cancer, for example, but not limited to, microcell lung cancer.

[0028] In one embodiment, the tumor may be a brain tumor.

[0029] Previously, it was mentioned that "brain tumor" means "brain tumor", "brain cancer" means "brain cancer", and "mixed use means". As a specific example, glioma, glioblastomas, anaplastic astrocytomas, meningiomas, pituitary tumors, schwannomas, CNS lymphomas, oligodendrogliomas, ependymomas, low-grade astrocytomas, medulloblastomas, astrocytic tumors, pilocytic astrocytoma, diffuse astrocytomas, pleomorphic xanthoastrocytomas, subependymal giant cell astrocytoma astrocytomas, anaplastic oligodendrogliomas, oligoastrocytomas, anaplastic oligoastrocytomas, myxopapillary ependymomas, subependymomas, ependymomas, anaplastic ependymomas, astroblastomas, chordoid gliomas of the third ventricle, gliomatosis cerebris, glangliocytomas, desmoplastic infantile astrocytoma astrocytomas, desmoplastic infantile gangliogliomas, dysembryoplastic neuroepithelialtumors, central neurocytomas, cerebellar liponeurocytomas, paragangliomas, ependymoblastomas, supratentorial primitive neuroectodermal tumors, choroids plexus papillomas, pineocytomas, pineoblastomas, pineal parenchymal tumors of intermediate differentiation, hemangiopericytomas, tumors of the sellar region, craniopharyngioma, capillary hemangioblastoma, or primary CNS lymphoma.

[0030] As used herein, the term "treatment or prevention of brain tumors" includes antitumor efficacy, anticancer efficacy, response rate, time to disease progression, survival rate, etc. For example, the antitumor efficacy of the treatment of brain tumors includes, but is not limited to, inhibition of brain tumor growth, delay of brain tumor growth, regression of brain tumors, reduction of brain tumor size, extension of time to brain tumor regrowth upon cessation of treatment, delay of brain tumor progression, etc. The antitumor efficacy of brain tumors includes not only currently existing treatments of brain tumors but also preventive methods.

[0031] As used herein, the term "therapeutic responsiveness" can mean whether or not a specific drug, for example, an anticancer drug, shows a therapeutic effect on the cancer of an individual patient.

[0032] As used herein, the term "prediction of therapeutic response of a cancer patient" refers to predicting in advance, before administration, whether administration of a drug will be useful for treating cancer, and may involve measuring gene expression levels and predicting therapeutic response to a drug.

[0033] As used herein, the term "prediction" can mean predicting a particular outcome, such as therapeutic response, through the identification of a characteristic, such as the expression level of a particular gene.

[0034] As used herein, the term "preparation capable of measuring the expression level of a gene" may refer to a preparation for directly or indirectly measuring the expression of the gene or the protein encoded thereby in a sample from a cancer patient to confirm the degree of expression in order to predict therapeutic responsiveness to an immunological anticancer drug. The "expression level of a gene" may be ascertainable by measuring the expression level of the mRNA of the gene or the expression level of the protein encoded by the gene. In one embodiment, the expression level of a gene may be used as a marker, and specifically, the therapeutic responsiveness of a cancer patient to an immunological anticancer drug may be judged differently depending on the expression level of the gene.

[0035] As used herein, the term "measurement of mRNA expression level" refers to measuring the amount of mRNA in a process of confirming the presence and expression level of mRNA of the gene in a subject sample. In one embodiment, the measurement of the mRNA expression level may be performed by one or more techniques selected from the group consisting of reverse transcriptase polymerase reaction (RT-PCR), competitive reverse transcriptase polymerase reaction (competitive RT-PCR), real-time reverse transcriptase polymerase reaction (realtime RT-PCR), RNase protection assay (RPA), Northern blotting, and DNA chip.

[0036] In one embodiment, the agent capable of measuring the expression level of said mRNA may be a primer or a probe that specifically binds to said gene.

[0037] As used herein, the term "primer" refers to a nucleic acid sequence having a short free 3-terminal hydroxyl group, capable of forming base pairs with a complementary template and acting as a starting point for template strand copying. The primer can initiate DNA synthesis in the presence of a reagent for polymerization reaction (i.e., DNA polymerase or reverse transcriptase) and four different nucleoside triphosphates in an appropriate buffer solution and temperature. The primer can also be a sense or antisense nucleic acid having a sequence of 7 to 50 nucleotides, and can incorporate additional features that do not change the basic property of the primer acting as a starting point for DNA synthesis. The sequence of the primer does not necessarily have to be exactly the same as that of the template, as long as it is sufficiently complementary and can hybridize with the template. The position of the primer, or the primer binding site, can refer to the target DNA segment to which the primer hybridizes.

[0038] In the present specification, the term "probe" refers to a nucleic acid fragment such as RNA or DNA, which can specifically bind to mRNA and is as short as a few bases or as long as several hundred bases, and can be labeled to confirm the presence or absence of a specific mRNA. The probe can be prepared in the form of an oligonucleotide probe, a single stranded DNA probe, a double stranded DNA probe, an RNA probe, etc. According to one embodiment, hybridization is performed using a probe complementary to the polynucleotide of a gene, and the reactivity of allulose can be predicted or assayed based on the hybridization. The selection of an appropriate probe and the hybridization conditions can be modified based on those known in the art.

[0039] The above primers or probes can be chemically synthesized using the phosphoramidite solid support method or other widely known methods. Such nucleic acid sequences can also be modified using many means known in the art. Non-limiting examples of such modifications include methylation, capping, substitution of one or more natural nucleotides with analogs, and internucleotide modifications, such as uncharged linkages such as methylphosphonates, phosphotriesters, phosphoramidates, carbamates, or charged linkages such as phosphorothioates and phosphorodithioates.

[0040] As used herein, the term "measurement of protein expression level" may refer to measuring the amount of protein in the process of confirming the presence and expression level of a protein encoded by the gene in a subject sample. In one embodiment, the measurement of the protein expression level may be performed by one or more techniques selected from the group consisting of Western blot, enzyme linked immunosorbent assay (ELISA), radioimmunoassay (RIA), radial immunodiffusion, Ouchterlony immunodiffusion, rocket immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, fluorescence activated cell sorter (FACS), and protein chip.

[0041] In one embodiment, the agent capable of measuring the expression level of said protein may be an antibody that specifically binds to the protein of said gene.

[0042] As used herein, the term "antibody" may refer to a specific protein molecule designated for an antigenic site, as a term known in the art. The form of the antibody according to one embodiment is not particularly limited, and may include polyclonal antibodies, monoclonal antibodies, or parts thereof as long as they have antigen-binding ability, all immunoglobulin antibodies, and special antibodies such as humanized antibodies. The antibody according to one embodiment may include not only a complete form having two full-length light chains and two full-length heavy chains, but also a functional fragment of an antibody molecule. The functional fragment of the antibody molecule means a fragment that retains at least an antigen-binding function, and may be, for example, Fab, F(ab'), F(ab')2, Fv, etc.

[0043] Another embodiment provides a composition for predicting the therapeutic response of a cancer patient to an immunological anticancer drug, comprising a preparation capable of measuring the expression level of the gene. In the composition, the gene, the preparation capable of measuring the expression level of the gene, the measurement of the mRNA expression level, the measurement of the protein expression level, the immunological anticancer drug, the cancer, and the prediction of the therapeutic response of a cancer patient are as described above.

[0044] Yet another embodiment provides a kit for predicting therapeutic response of a patient receiving immune cell therapy comprising the composition.

[0045] In the kit, the immune anticancer agent, immune cell therapy agent, cancer, tumor, therapeutic response, and prediction are as described above.

[0046] The kit can detect the marker by confirming the mRNA expression level or protein expression level of the gene. The kit according to one embodiment includes not only a primer, a probe, or an antibody that selectively recognizes the marker for measuring the expression level of the gene whose expression is increased or decreased due to therapeutic responsiveness to an immunological anticancer drug, but also one or more other component compositions, solutions, or devices suitable for the analytical method.

[0047] In one embodiment, the kit for measuring the mRNA expression level of the gene may be a kit containing essential elements for performing RT-PCR, such as specific primers for each gene, test tubes or other suitable containers, reaction buffer, deoxyribonucleic acid (dNTPs), enzymes such as Taq-polymerase and reverse transcriptase, DNase, RNase inhibitor, DEPC water, sterile water, etc.

[0048] In one embodiment, the kit for measuring the expression level of the protein also includes a substrate, an appropriate buffer solution, a secondary antibody labeled with a chromogenic enzyme or a fluorescent substance, and a chromogenic substrate for immunological detection of the antibody. The substrate may be a nitrocellulose membrane, a 96-well plate synthesized with a polyvinyl resin, a 96-well plate synthesized with a polystyrene resin, or a glass slide, the chromogenic enzyme may be peroxidase, alkaline phosphatase, or the like, the fluorescent substance may be FITC, RITC, or the like, and the chromogenic substrate may be 2,2'-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), o-phenylenediamine (OPD), or tetramethylbenzidine (TMB).

[0049] Yet another embodiment includes the steps of measuring the mRNA or protein expression level of one or more genes selected from the group consisting of APP, C3, CASP1, CD33, CD3E, CD5, CD86, F13A1, FCGR2A, HLA-DRA, HLA-G, ICAM2, IL10, IL10RA, IL12RB2, IL18, IL1B, IL34, IL7, IL7R, INPP5D, ITGA1, JAM3, LAMP3, LY96, NFKB1, NLRP3, NOTCH1, PDCD1, PECAM1, PSMB8, PTPRC, SAA1, SELPLG, ST6GAL1, STAT4, TICAM2, TLR1, TNFRSF18, TNFSF4, and TREM2 from a biological sample obtained from the patient;

[0050] and predicting a patient's therapeutic response to an immune cell therapy agent based on the measured expression level of the mRNA or its protein.

[0051] Yet another embodiment includes the steps of measuring the mRNA or protein expression level of a gene selected from the group consisting of APP, C3, CASP1, CD33, CD3E, CD5, CD86, F13A1, FCGR2A, HLA-DRA, HLA-G, ICAM2, IL10, IL10RA, IL12RB2, IL18, IL1B, IL34, IL7, IL7R, INPP5D, ITGA1, JAM3, LAMP3, LY96, NFKB1, NLRP3, NOTCH1, PDCD1, PECAM1, PSMB8, PTPRC, SAA1, SELPLG, ST6GAL1, STAT4, TICAM2, TLR1, TNFRSF18, TNFSF4, and TREM2 in a biological sample isolated from the patient;

[0052] The present invention provides a method for predicting the prognosis of a patient administered an immune cell therapeutic agent, comprising a step of comparing the measured expression level with the expression level of the gene's mRNA or its protein in a normal control group.

[0053] In the above method, the genes, patients, cancers, tumors, measurement of mRNA expression levels, measurement of protein expression levels, immunological anticancer agents, immune cell therapeutic agents, and prediction of therapeutic response of cancer patients are as described above.

[0054] As used herein, the term "biological sample" refers to a sample obtained from a subject. The biological sample may include whole blood, plasma, serum, red blood cells, white blood cells (e.g., peripheral blood mononuclear cells), mammary gland fluid, ascites, pleural efflux, nipple aspirate, lymph (e.g., disseminated tumor cells in lymph nodes), bone marrow aspirate, saliva, urine, stool (i.e., excreta), sputum, bronchial lavage, tears, fine needle aspirate (e.g., collected by random breast fine needle aspiration), any other bodily fluid, tissue sample (e.g., tumor tissue), tumor biopsy (e.g., needle biopsy), lymph node (e.g., sentinel lymph node biopsy), tissue sample (e.g., tumor tissue), surgical resection of tumor, and cell extracts thereof. In one embodiment, the biological sample may be whole blood, or a portion thereof, such as plasma, serum, or cell pellet. In one embodiment, the sample can be obtained by isolating circulating cells of a solid tumor from whole blood or a cellular fraction thereof using any technique known in the art. In one embodiment, the sample can be, for example, a formalin-fixed paraffin-embedded (FFPE) tumor tissue sample from a solid tumor. In one embodiment, the sample can be a tumor lysate or extract prepared from frozen tissue obtained from an individual with cancer.

[0055] In the present specification, the term "obtain" may refer to obtaining a nucleic acid sample or a polypeptide sample from a biological sample. Obtaining the nucleic acid sample may be performed by a conventional nucleic acid isolation method. For example, the target nucleic acid may be amplified via polymerase chain reaction (PCR), ligase chain reaction (LCR), transcription mediated amplification, or real-time nucleic acid sequence based amplification (NASBA), and purified to obtain the nucleic acid. Alternatively, the target nucleic acid may be obtained as crudely isolated nucleic acid from a disruption of a biological sample. Obtaining the polypeptide sample may be performed by a conventional protein extraction or isolation method.

[0056] The control group is also used interchangeably with the term “negative control group.” The control group may be one that is non-responsive or poorly responsive to the immunological anti-cancer agent.

[0057] The method may further include determining that if the expression level is elevated compared to the expression level measured in a control group, the individual is likely to be responsive to an immune cell therapy agent or to exhibit a prognosis of recovery following administration of an immune cell therapy agent.

[0058] In one embodiment, the method may further include a step of determining whether or not to administer an immunotherapy to the cancer patient based on the predicted therapeutic response. In one embodiment, the step of predicting therapeutic response may determine that the cancer patient has a high therapeutic response to an immunotherapy if the measured mRNA or protein expression level of the gene is elevated compared to other cancer patients. In one embodiment, the step of determining whether or not to administer an immunotherapy to the cancer patient may determine that the cancer patient will receive an immunotherapy if the cancer patient is predicted to have a high therapeutic response.

[0059] In one embodiment, the patient is also a tumor patient, and the tumor may be a benign or malignant tumor. The tumor may be a brain tumor. For example, the brain tumor may be a glioma.

[0060] The patient may be a mammal. Specifically, the patient may be a human, dog, cat, ferret, hamster, mouse, horse, cow, monkey, chimpanzee, etc.

[0061] In one embodiment, the patient may have been or is receiving standard of care. For example, the method may predict whether the patient will respond favorably to an immune cell therapy when expression of the gene or protein is measured from a biological sample obtained from the patient receiving standard of care, and plan subsequent treatment.

[0062] In yet another embodiment, a system for predicting therapeutic response to an immune anticancer drug based on gene expression analysis data of a patient, comprising: an information receiving unit for collecting genetic analysis data of a patient; A prediction unit that predicts a patient's immune response to an anticancer drug using the information input through the information receiving unit, The gene is one or more selected from the group consisting of APP, C3, CASP1, CD33, CD3E, CD5, CD86, F13A1, FCGR2A, HLA-DRA, HLA-G, ICAM2, IL10, IL10RA, IL12RB2, IL18, IL1B, IL34, IL7, IL7R, INPP5D, ITGA1, JAM3, LAMP3, LY96, NFKB1, NLRP3, NOTCH1, PDCD1, PECAM1, PSMB8, PTPRC, SAA1, SELPLG, ST6GAL1, STAT4, TICAM2, TLR1, TNFRSF18, TNFSF4, and TREM2.

[0063] Yet another aspect provides a recording medium including the system.

[0064] In the system, the genes, patients, cancers, tumors, measurement of mRNA expression levels, measurement of protein expression levels, immunological anticancer agents, immune cell therapeutic agents, and prediction of therapeutic response of cancer patients are as described above. Effect of the Invention

[0065] The genetic marker according to one embodiment is remarkably excellent in predicting therapeutic response to immune cell therapy agents, and enables selection of patient groups predicted to show therapeutic effects and administration of appropriate treatment, thereby reducing patient pain and costs. [Brief description of the drawings]

[0066] [Figure 1] This is a conceptual diagram showing the process of selecting genes associated with groups that showed therapeutic effects and constructing a predictive model. [Diagram 2] FIG. 1 shows heat maps of expression patterns of three gene biomarkers in a random forest model created with TNFSF4, TNFRSF18, and IL12R2B. [Diagram 3] This graph (left) shows the classification of responders and non-responders using the predicted values ​​in a random forest model created with TNFSF4, TNFRSF18, and IL12R2B, and this graph (right) shows the predicted immune cell therapy response rate results in the control group that received standard treatment. [Figure 4] FIG. 1 shows the expression characteristics of 770 genes according to gene functional groups in the responder and non-responder groups. [Diagram 5] FIG. 1 shows the expression characteristics of 16 predicted genes selected for each function in the responder and non-responder groups. [Figure 6A] This figure shows the results of distinguishing between responders and non-responders using a random forest model that uses 16 genes selected according to function. [Figure 6B] This figure shows predicted immunotherapy response in a patient group that did not receive immunotherapy but received standard treatment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0067] The present invention will be described in more detail below with reference to examples. However, these examples are merely for illustrative purposes and the scope of the present invention is not limited to these examples. (Example)

[0068] Example 1. Selection of research subjects and derivation of biomarker candidates 1.1 Research subject selection In order to select biomarkers for brain tumors, brain tumor patients were selected as study subjects. A total of 20 glioma patients were selected, of which the experimental group consisted of 12 glioma patients who received NK cell therapy, and the control group consisted of 8 glioma patients who received standard treatment. The experimental group consisted of 5 NK cell therapy responders and 7 non-responders. From the patients, 5μl (100 to 300ng) RNA samples were obtained before FFPE (fromalin-fixed paraffin-embedded) treatment and genetic analysis was carried out. All patient samples used in the study were approved by the Institutional Review Board (IRB file No. 2012-12-172-077).

[0069] Specifically, 5 μl of the extracted RNA was used to prepare samples and measure expression levels using the NanoString nCounter Analysis System (NanoString Technologies, Inc.). QC used in the experiment was confirmed using Nanostring's nSolver software and Nanostring QC Pro package, and data normalization was also performed using the nSolver software.

[0070] Figure 1 is a conceptual diagram showing the process of selecting genes associated with groups that showed therapeutic benefit and constructing a predictive model.

[0071] Table 1 shows information on the brain tumor patients selected for the study. [Table 1]

[0072] As a result, as shown in Table 1 above, it was confirmed that the responding group of patients in the study had a lower recurrence rate, a later recurrence time, and a longer overall survival time than the non-responding group.

[0073] Furthermore, we were able to derive expression profile data for 770 genes (hereafter referred to as biomarker candidate groups) that showed significant differences in expression between the experimental and control groups.

[0074] 1.2 Biomarker selection and model building methods To select gene expression markers that are significantly associated with responsiveness to NK cell therapy from the group of biomarker candidates derived above, we analyzed the transcriptome and prognosis data of 12 brain tumor patients, as well as the data of primary brain tumor patients from TCGA.

[0075] Specifically, two-group t-test and AUC analysis were performed. Next, overall survival and progression-free survival analyses were performed. Then, a brain tumor random forest model was constructed using the R package in random forest. The number of trees in the model was 500, and the remaining parameters were all set to default values. The leave-one-out crossvalidation (LOOCV) method was used to measure the predictive power.

[0076] The derived values ​​were then subjected to statistical analysis using violin plots and survival plots. The violin plots were drawn using the ggplot2 R package, and the survival plots were analyzed using the survminer R package. P values ​​were calculated using the log-rank test. AUC was analyzed using the ROCR package.

[0077] Table 2 shows the 41 genes selected for gene expression data analysis.

[0078] [Table 2] JPEG2025028861000004.jpg248166

[0079] As a result, as shown in Table 2, 41 gene markers associated with responsiveness to NK cell therapy or treatment prognosis were selected.

[0080] 1.3 Statistical analysis Statistical analysis was performed on the 41 genes in Table 2 according to the method described in Example 1.3.

[0081] Table 3 shows the AUC, t-test p-value, overall survival p-value, and disease-free survival p-value of the statistical analysis results.

[0082] [Table 3] JPEG2025028861000006.jpg15164

[0083] As a result, as shown in Table 3, the AUC value of the genes was 0.7 or more, indicating high predictive power. Such results indicate that the 41 selected biomarkers are statistically significant.

[0084] Example 2. Construction of a random forest model using each gene list and confirmation of predictive power 2.1 Construction of a random forest model based on molecular biological association and confirmation of predictive power In order to select gene signatures that best predict the therapeutic response of patients treated with immune cell therapy agents, the 41 gene expression data from Example 1 were classified into seven types based on biological association, and random forest models were constructed for each type to confirm the model with the best predictive power. The seven model names were inflammasome pathway, inflammation, TNF superfamily, adhesion signaling, cytokine, degenerative disease association factor, and SUM.

[0085] Table 4 shows the predictive power of the random forest models constructed with each gene list.

[0086] [Table 4]

[0087] As a result, as shown in Table 4, the combination of TNFSF4, TNFRSF18 and IL12RB2 genes showed the best predictive power with a sensitivity of 1.0 and specificity of 1.0. However, considering that the selected genes are related genes that are related to NK cell therapy and that related genes affect each other, it can be concluded that not only these three genes but all 41 genes play an important role in predicting treatment response.

[0088] 2.2 Validation of the predictive power of the gene list selected by the random forest model To verify the predictive power of the gene list selected by the random forest model, TNFSF4, TNFRSF18, and IL12RB2, which had the highest predictive power in Example 2.1, were used to create a heatmap using the heatmap package.

[0089] FIG. 2 shows the heat map of expression patterns of the three gene biomarkers in the random forest model created with TNFSF4, TNFRSF18, and IL12R2B.

[0090] Figure 3 shows a graph (left) showing classification of responders and non-responders using predicted values ​​from a random forest model created with TNFSF4, TNFRSF18, and IL12R2B, and a graph (right) predicting the response rate results of immune cell therapy in a control group that received standard treatment.

[0091] As a result, as shown in Figure 2, the expression patterns of the three gene biomarkers were confirmed, and it was found that the expression patterns of the three genes were higher than those of the non-responder group.

[0092] In addition, as shown in Figure 3, the probability of patients who received immune cell therapy responding to the immune cell therapy is shown in a bar graph, and it was found that the random forest model can effectively classify the patients into responders and non-responders.

[0093] Furthermore, in the control group, which was a group of patients who did not receive immune cell therapy but received standard treatment, the probability of responding to immune cell therapy was shown as a response rate. As a result, it was confirmed that in the control group, patients shown in red in Figure 3 were more likely to respond to treatment and survive longer if they received immune cell therapy than if they received standard treatment.

[0094] This means that in the case of the gene biomarkers selected in the present invention, patients who are adverse to immune cell therapy can be effectively selected.

[0095] Example 3. Functional classification of biomarker candidates and selection of biomarkers 3.1. Functional classification and gene selection of biomarker candidates In order to analyze the functional expression characteristics of the 770 biomarker candidates derived in Example 1.1 above, the expression of the 770 genes in the responder and non-responder groups was analyzed by functional group.

[0096] FIG. 5 shows the expression characteristics of 770 genes according to gene function groups in the responder and non-responder groups.

[0097] As a result, as shown in FIG. 5, it was confirmed that the expression of immune-related genes was higher in the reaction group.

[0098] Next, based on the functional classification of the 770 biomarker candidates, genes related to immune cell therapy response prediction were selected. Specifically, genes with excellent predictive power (AUC 0.85 or more) and associated with treatment prognosis (OS p value less than 0.05 or PFS p value less than 0.05) were selected from the 770 biomarker candidates. Then, the selected genes were classified by function, and the importance rank was calculated to determine which genes in the functional group were more important for prediction. The importance rank was derived using a random forest modeling method.

[0099] Table 5 shows 16 genes that are important for predicting immunotherapy response for each functional group.

[0100] [Table 5]

[0101] FIG. 5 shows the expression characteristics of 16 predictive genes selected for each function in the responder and non-responder groups.

[0102] As a result, as shown in Figure 5, it was confirmed that the expression levels of the genes selected for each function were higher in the responding group (green) than in the non-responding group (red), which means that the 16 selected genes may function as markers to effectively determine whether a patient will respond to immune cell therapy.

[0103] 3.2 Confirmation of the predictive ability of selected biomarkers for immunotherapy response In order to verify whether the above-mentioned selected 16 genes can effectively predict the response of patients to immunotherapy, a random forest model was constructed using the above-mentioned selected 16 genes by using the method described in Example 2.

[0104] Specifically, we predicted whether immunotherapy would be more effective than standard treatment in patients who did not receive NK cell immunotherapy. Among patients who received standard treatment, patients who died early are displayed in orange, those who died after the average period of time are displayed in yellow, and those who lived longer than the average are displayed in blue.

[0105] Figures 6A and 6B show the results of distinguishing between responders and non-responders using a random forest model with 16 functionally selected genes (Figure 6A), and the results of predicting immunotherapy responsiveness in a patient group that did not receive immunotherapy but received standard treatment (Figure 6B).

[0106] As a result, as shown in FIG. 6A and FIG. 6B, it was confirmed that the random forest model constructed with the selected 16 genes can distinguish all responders and non-responders. Specifically, when the baseline probability of therapeutic response was set to 50%, three patients out of eight control group patients were predicted to have a good therapeutic response, and two of the three patients had a poor prognosis when standard treatment was administered, so it was confirmed that NK cell immunotherapy may have a better prognosis than standard treatment. In addition, as a result of predicting immunotherapy responsiveness in patients who received standard treatment without immunotherapy, it was confirmed that there were patients who appeared to respond to immunotherapy regardless of the prognosis after treatment. Such results mean that for patients who belong to a group of patients who die early or show an average prognosis, immunotherapy is more useful than general treatment, and such usefulness can be easily determined by using the selected gene biomarkers of the present invention.

Claims

1. A composition for predicting the therapeutic response of a patient undergoing immunotherapy, comprising a preparation capable of measuring the expression levels of mRNA or proteins of the genes TNFRSF18, TNFSF4, and IL12RB2.

2. 2. The composition of claim 1, wherein the immunotherapeutic agent is any one selected from the group consisting of an NK cell therapeutic agent, a T cell therapeutic agent, a CAR immune cell therapeutic agent, a DC vaccine, anti-PD-L1, anti-PD-1, and anti-CTLA-4.

3. The composition of claim 1 , wherein the patient is a tumor patient.

4. The composition of claim 3 , wherein the tumor is a brain tumor.

5. Preface: Brain swellings include gliomas, glioblastomas, anaplastic astrocytomas, meningiomas, pituitary tumors, schwannomas, CNS lymphomas, oligodendrogliomas, ependymomas, low-grade astrocytomas, medulloblastomas, astrocytic tumors, pilocytic astrocytoma, diffuse astrocytomas, pleomorphic xanthoastrocytomas, and subependymal giant cell astrocytoma. astrocytomas, anaplastic oligodendrogliomas, oligoastrocytomas, anaplastic oligoastrocytomas, myxopapillary ependymomas, subependymomas, ependymomas, anaplastic ependymomas, astroblastomas, chordoid gliomas of the third ventricle, gliomatosis cerebris, glangliocytomas, desmoplastic infantile astrocytoma astrocytomas), desmoplastic infantile gangliogliomas, dysembryoplastic neuroepithelial tumors, central neurocytomas, cerebellar5. The composition of claim 4, wherein the tumor is any one or more selected from the group consisting of: liponeurocytomas, paragangliomas, ependymoblastomas, supratentorial primitive neuroectodermal tumors, choroid plexus papillomas, pineocytomas, pineal tumors, pineal parenchymal tumors of intermediate differentiation, hemangiopericytomas, tumors of the sellar region, craniopharyngioma, capillary hemangioblastoma, and primary CNS lymphoma.

6. The composition of claim 1, wherein the agent capable of measuring the expression level of the mRNA is a primer or probe that specifically binds to the gene.

7. The composition according to claim 1, wherein the agent capable of measuring the expression level of the protein is an antibody that specifically binds to the protein of the gene.

8. The composition of claim 1, further comprising a preparation capable of measuring the expression level of mRNA or protein of one or more genes selected from the group consisting of APP, C3, CASP1, CD33, CD3E, CD5, CD86, F13A1, FCGR2A, HLA-DRA, HLA-G, ICAM2, IL10, IL10RA, IL18, IL1B, IL34, IL7, IL7R, INPP5D, ITGA1, JAM3, LAMP3, LY96, NFKB1, NLRP3, NOTCH1, PDCD1, PECAM1, PSMB8, PTPRC, SAA1, SELPLG, ST6GAL1, STAT4, TICAM2, TLR1, and TREM2.

9. A kit for predicting the therapeutic response of a patient undergoing immunotherapy, comprising the composition of claim 1.

10. measuring the expression levels of the mRNAs or proteins of the genes TNFRSF18, TNFSF4, and IL12RB2 from a biological sample obtained from the patient; and predicting a patient's therapeutic response to an immunotherapeutic agent based on the measured mRNA or protein expression levels.

11. and measuring the expression level of mRNA or protein of one or more genes selected from the group consisting of APP, C3, CASP1, CD33, CD3E, CD5, CD86, F13A1, FCGR2A, HLA-DRA, HLA-G, ICAM2, IL10, IL10RA, IL18, IL1B, IL34, IL7, IL7R, INPP5D, ITGA1, JAM3, LAMP3, LY96, NFKB1, NLRP3, NOTCH1, PDCD1, PECAM1, PSMB8, PTPRC, SAA1, SELPLG, ST6GAL1, STAT4, TICAM2, TLR1, and TREM2 from a biological sample obtained from the patient. The method of claim 10, further comprising predicting therapeutic responsiveness to an immune cell therapy agent based on the measured mRNA or protein expression level.

12. The method of claim 10, wherein the step of predicting therapeutic response comprises determining that the patient has a high therapeutic response to an immunotherapeutic agent if the measured mRNA or protein expression level of the gene is elevated compared to other control patients.

13. The method of claim 10 , wherein the patient is a tumor patient.

14. 14. The method of claim 13, wherein the tumor is a brain tumor.

15. 11. The method of claim 10, wherein the patient has received or is receiving standard of care.

16. 11. The method of claim 10, wherein the measurement of the mRNA expression level is performed by one or more techniques selected from the group consisting of reverse transcriptase polymerase reaction (RT-PCR), competitive reverse transcriptase polymerase reaction (competitive RT-PCR), real-time reverse transcriptase polymerase reaction (real-time RT-PCR), RNase protection assay (RPA), Northern blotting, and DNA chip.

17. 11. The method of claim 10, wherein the measurement of the protein expression level is performed by one or more techniques selected from the group consisting of Western blot, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), radial immunodiffusion, Ouchterlony immunodiffusion, rocket immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, flow cytometry (FACS), and protein chip.

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