Spatial transcriptome interpretation-based biomarkers for predicting immunotherapy response and their applications

Abstract

The present invention relates to a biomarker for predicting therapeutic response to immunotherapy based on spatial transcriptome interpretation, and uses thereof. Specifically, the present invention relates to a biomarker composition for predicting therapeutic response to cancer immunotherapy, a composition for predicting therapeutic response to cancer immunotherapy, a kit for predicting therapeutic response to cancer immunotherapy comprising the composition, a method for providing information for predicting therapeutic response to cancer immunotherapy, and a method for providing information for predicting survival prognosis of cancer patients. The biomarkers of the present invention were discovered by interpreting the expression levels of genes specific to cell groups based on the location information of cells in tissue sections obtained by spatial transcriptome interpretation. This improves the accuracy and precision of predicting the response to cancer immunotherapy and even enables prediction of survival prognosis, thereby determining optimal treatment strategies, improving treatment efficacy, and reducing patient pain and costs.

Description

[Technical Field]

[0001] The present invention relates to a biomarker for predicting therapeutic response to immunotherapy based on spatial transcriptome interpretation, and its use. [Background technology]

[0002] Cancer remains one of the most difficult-to-treat diseases, even with today's advances in medical technology. It has the highest mortality rate of any individual disease and is cited as the leading cause of death among Koreans. It also puts a strain on the economic situation of families and places a huge economic burden on society. The number of cancer cases and deaths is increasing year by year. The main pillars of cancer treatment are surgery, radiation therapy, and chemotherapy (anticancer drugs), but these have many side effects, including cytotoxicity, metastasis, and recurrence, so it is necessary to develop therapies that significantly reduce side effects.

[0003] In recent years, the emergence of immune checkpoint antibodies that restore immune function has brought a sudden surge in the importance of immunotherapy. Immune cell therapy, in particular, has been recognized as the fourth cancer treatment following surgery, radiation therapy, and chemotherapy (anticancer drugs), and various methods have been investigated for more than 10 types of cancer, including brain tumors, malignant melanoma, gastric cancer, urothelial cancer, head and neck cancer, liver cancer, and ovarian cancer. Many clinical trials are currently underway.

[0004] Furthermore, as a way to improve the therapeutic effect of immune cell therapy, biomarkers that predict therapeutic response are becoming increasingly important, and the development of biomarkers to determine the effectiveness of immune cell therapy is underway.

[0005] To determine the treatment strategy for cancer patients using immune cell therapy, we predict therapeutic response by analyzing mRNA expression. Immune cell therapy mainly includes T cell therapy, NK cell therapy, and immune checkpoint drugs (PD-1, PD-L1, CTLA-4), and their therapeutic response is influenced by the state of the tumor microenvironment (TME). Here, the tumor microenvironment refers to the various cells (fibroblasts, immune cells, vascular cells, etc.) present in and around tumor tissue, as well as the cellular and microcellular components that make up those cells.

[0006] However, existing immune cell therapy biomarkers selected by analyzing mRNA expression in pathology slides of cancer tissues are found by simply measuring the average gene expression level of cells present in tissues or sections without any consideration of the cellular composition of the tumor microenvironment. As a result, they are unable to accurately predict immunotherapy response and have problems such as unpredictable side effects due to therapeutic intervention.

[0007] Therefore, it is necessary to develop new biomarkers for immune cell therapy that can improve accuracy and precision in predicting therapeutic response, taking into account the tumor microenvironment. Summary of the Invention [Problem to be solved by the invention]

[0008] Therefore, the inventors selected biomarkers that can predict the therapeutic response of immune cell therapy for ovarian cancer based on spatial transcriptome interpretation using the GeoMx Digital Spatial Profiling (DSP) system, and confirmed that these biomarkers can improve the therapeutic effect of cancer patients and realize personalized therapy (individualized medicine), thereby achieving the present invention.

[0009] Therefore, one object of the present invention is to provide a biomarker composition for predicting therapeutic response to cancer immunotherapy, comprising one or more genes selected from the group consisting of NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G, or proteins encoded by the genes.

[0010] A further object of the present invention is to provide a composition for predicting therapeutic response to cancer immunotherapy, comprising a preparation capable of measuring the expression level of mRNA or protein of one or more genes selected from the group consisting of NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G.

[0011] A further object of the present invention is to provide a kit for predicting therapeutic response to cancer immunotherapy, comprising the composition according to the present invention.

[0012] A further object of the present invention is to provide an information providing method for predicting therapeutic response to cancer immunotherapy, comprising: (1) measuring the expression level of one or more genes selected from the group consisting of NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G, or proteins of said genes, in a biological sample collected from a cancer patient; and (2) predicting therapeutic response to cancer immunotherapy based on the measured expression level of said mRNA or protein.

[0013] A further object of the present invention is to provide a method for providing information for predicting the survival prognosis of a cancer patient, which comprises measuring the expression level of one or more genes selected from the group consisting of NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G, or proteins of said genes, in a biological sample collected from the cancer patient. [Means for solving the problem]

[0014] To achieve the objectives of the present invention, the present invention provides a biomarker composition for predicting therapeutic response to cancer immunotherapy, comprising one or more genes selected from the group consisting of NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G, or proteins encoded by the genes.

[0015] In one embodiment of the present invention, the cancer may be one selected from the group consisting of ovarian cancer, bile duct cancer, colon cancer, adenocarcinoma, rectal cancer, breast cancer, synovial sarcoma, chondrosarcoma, thyroid cancer, gastric cancer, thymic cancer, cervical cancer, glioma, brain cancer, melanoma, lung cancer, bladder cancer, prostate cancer, leukemia, kidney cancer, liver cancer, colorectal cancer, pancreatic cancer, lymphoma, uterine cancer, oral cancer, bronchial cancer, nasopharyngeal cancer, laryngeal cancer, skin cancer, blood cancer, parathyroid cancer, and ureteral cancer.

[0016] In one embodiment of the present invention, the immunotherapy may be one selected from the group consisting of NK cell therapy, T cell therapy, CAR-T cell therapy, DC vaccine, anti-PD-L1, anti-PD-1, and anti-CTLA-4.

[0017] The present invention further provides a composition for predicting therapeutic responsiveness to cancer immunotherapy, comprising a preparation capable of measuring the expression level of mRNA or protein of one or more genes selected from the group consisting of NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G.

[0018] In one embodiment of the present invention, the expression level of the gene may be measured in a region of interest in the tumor microenvironment in diseased tissue collected from a cancer patient.

[0019] In one embodiment of the present invention, the region of interest may be a tumor region where PanCK is expressed, an immune region where CD45 is expressed, or a stromal region.

[0020] In one embodiment of the present invention, the expression levels of NKG7, ULBP3, FPR2, or MYC may be measured in the immune region where CD45 is expressed, the expression levels of CXCL10 or NECTIN2 may be measured in the immune region where PanCK is expressed, and the expression levels of CD8A, HLA-DQA1, BMP2, INF-β, TNF-β, IL6, OX40-L, OX40, Tim3, HLA-C, or HLA-G may be measured in the immune region where PanCK is expressed.

[0021] In one embodiment of the present invention, the agent capable of measuring the expression level of the mRNA may be a primer or a probe that specifically binds to the gene.

[0022] In one embodiment of the present invention, the agent capable of measuring the expression level of the protein may be an antibody that specifically binds to the protein of the gene.

[0023] In one embodiment of the present invention, the cancer may be one selected from the group consisting of ovarian cancer, bile duct cancer, colon cancer, adenocarcinoma, rectal cancer, breast cancer, synovial sarcoma, chondrosarcoma, thyroid cancer, gastric cancer, thymic cancer, cervical cancer, glioma, brain cancer, melanoma, lung cancer, bladder cancer, prostate cancer, leukemia, kidney cancer, liver cancer, colorectal cancer, pancreatic cancer, lymphoma, uterine cancer, oral cancer, bronchial cancer, nasopharyngeal cancer, laryngeal cancer, skin cancer, blood cancer, parathyroid cancer, and ureteral cancer.

[0024] In one embodiment of the present invention, the immunotherapy may be one selected from the group consisting of NK cell therapy, T cell therapy, CAR-T cell therapy, DC vaccine, anti-PD-L1, anti-PD-1, and anti-CTLA-4.

[0025] The present invention further provides a kit for predicting therapeutic response to cancer immunotherapy, which comprises the composition according to the present invention.

[0026] The present invention further provides a method for providing information for predicting therapeutic response to cancer immunotherapy, comprising: (1) measuring the expression level of one or more genes selected from the group consisting of NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G, or proteins of said genes, in a biological sample collected from a cancer patient; and (2) predicting therapeutic response to cancer immunotherapy based on the measured expression level of said mRNA or protein.

[0027] In one embodiment of the present invention, the expression level in the above (1) may be measured in a region of interest in the tumor microenvironment of the biological sample.

[0028] In one embodiment of the present invention, the region of interest may be a tumor region where PanCK is expressed, an immune region where CD45 is expressed, or a stromal region.

[0029] In one embodiment of the present invention, the expression levels of NKG7, ULBP3, FPR2, or MYC may be measured in the immune region where CD45 is expressed, the expression levels of CXCL10 or NECTIN2 may be measured in the immune region where PanCK is expressed, and the expression levels of CD8A, HLA-DQA1, BMP2, INF-β, TNF-β, IL6, OX40-L, OX40, Tim3, HLA-C, or HLA-G may be measured in the immune region where PanCK is expressed.

[0030] In one embodiment of the present invention, predicting the therapeutic response to cancer immunotherapy in (2) may involve determining that the cancer patient has a high therapeutic response to cancer immunotherapy when the expression level of the NKG7 or ULBP3 is equal to or higher than a defined expression threshold, or when the expression level of the FPR2 or MYC is equal to or lower than a defined expression threshold.

[0031] In one embodiment of the present invention, predicting the therapeutic response to cancer immunotherapy in (2) may involve determining that the cancer patient has a high therapeutic response to cancer immunotherapy when the expression level of CXCL10 or MECTIN2 is equal to or lower than a defined expression threshold.

[0032] In one embodiment of the present invention, predicting the therapeutic response to cancer immunotherapy in (2) may be determining that the cancer patient has a high therapeutic response to cancer immunotherapy when the expression levels of CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, or HLA-G are equal to or higher than a defined expression threshold.

[0033] The present invention further provides a method for providing information for predicting the survival prognosis of a cancer patient, which comprises measuring the expression level of one or more genes selected from the group consisting of NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G, or proteins of said genes, in a biological sample collected from the cancer patient.

[0034] In one embodiment of the present invention, the expression level may be measured in the biological sample in a region of interest in the tumor microenvironment, such as a tumor region where PanCK is expressed, an immune region where CD45 is expressed, or a stromal region.

[0035] In one embodiment of the present invention, when the expression level of the NKG7 or ULBP3 is equal to or higher than a defined expression threshold, or the expression level of the FPR2 or MYC is equal to or lower than a defined expression threshold in the immune region where the CD45 is expressed, the progression-free survival (PFS) of the cancer patient may be determined to be high.

[0036] In one embodiment of the present invention, if the expression level of CXCL10 or MECTIN2 in the tumor area where PanCK is expressed is below a defined expression threshold, the progression-free survival rate (PFS) of the cancer patient may be determined to be high.

[0037] In one embodiment of the present invention, when the expression level of CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, or HLA-G in the stromal region is equal to or greater than a defined expression threshold, the progression-free survival rate (PFS) of the cancer patient may be determined to be high. [Effects of the Invention]

[0038] The present invention relates to a biomarker that can predict the therapeutic response to immune cell therapy based on the composition of the tumor microenvironment using a biological sample collected from a cancer patient, determine the immunotherapy treatment plan, and even predict the survival prognosis of the cancer patient. The biomarker of the present invention is discovered by interpreting the expression levels of genes specific to a cell group based on the location information of cells in a tissue section obtained by employing spatial transcriptome interpretation. This improves the accuracy and precision of the prediction of the therapeutic response to cancer immunotherapy and even enables the prediction of survival prognosis, thereby determining the optimal treatment plan, improving treatment efficacy, and reducing patient pain and costs. [Brief explanation of the drawings]

[0039] [Figure 1] Schematic illustrating the process of spatial transcriptome interpretation to screen for biomarkers that can predict immunotherapy response in patients with solid tumors. [Figure 2] Spatial transcriptome interpretation of biospecimens from patients with solid tumors. (A) Pie chart showing the cell abundance in the tumor (PanCK), immune (CD45), and stromal regions, which correspond to regions of interest in the tumor microenvironment. (B) Ternary plot showing the frequency of cell types in each region of interest (dots represent types of cells). [Figure 3] CD8A expression pattern according to homology-directed repair deficiency (HRD). (A) Comparison of CD8A expression between patients with and without homology-directed repair deficiency in three regions of interest. (B) Comparison of CD8A expression between patients with and without homology-directed repair deficiency in the stromal region. (C) Comparison of CD8A expression between patients with and without homology-directed repair deficiency in NR (non-relapse group) and PS (platinum-sensitive group). (D) Correlation with other markers (STING, IFNB1, CXCLS9) (*p<0.05). [Figure 4]Analysis of transcriptome differences between the non-recurrence and recurrence groups. (A) Heat map of the top 50 differentially expressed genes in the stromal region. (B) Enriched functions identified from gene sets specific to three regions (red: non-recurrence group, blue: recurrence group). (C) Enrichment analysis results for gene sets related to interferon-γ response and epithelial-to-cell transition. [Figure 5] The results of measuring the expression signature of genes that make up the tumor microenvironment for each region of interest in the non-recurrence and recurrence groups [Figure 6] Comparison of the expression of selected genes related to immunotherapy response between non-relapse and relapse groups [Figure 7] 1 shows the results of a progression-free survival (PFS) analysis of biomarker genes predicting therapeutic response to immunotherapy according to the present invention. Expression levels of NKG7, OX40, IFN-γ, and HLA-DQA1 are classified based on the intermediate cutoff. [Figure 8] Using a gynecological cancer (cervical squamous cell carcinoma and endocervical adenocarcinoma) cohort (public dataset) obtained from the large-scale cancer gene database TCGA (The Cancer Genome Atlas), an analysis was conducted to determine the relationship between the expression level of immunotherapy response predictive biomarkers and patient survival prognosis. DETAILED DESCRIPTION OF THE INVENTION

[0040] The present invention is characterized by providing a novel biomarker that can predict the therapeutic response of cancer patients to immunotherapy.

[0041] Most conventional biomarkers were discovered by analyzing gene expression in whole cells on pathology slides of cancer tissue, but they did not take into consideration the tumor microenvironment, which resulted in low diagnostic accuracy when using biomarkers. In addition, there was the problem of unpredictable side effects occurring after therapeutic intervention was carried out based on the treatment plan decision.

[0042] In particular, in cancer therapy, simply identifying and eliminating antigens on cancer cells is often insufficient. In many cases, the complex environment surrounding cancer cells, including the various cells (basal cells, inflammatory cells, etc.), the blood vessels that pass through the cells, and the extracellular matrix, has a significant impact on their survival and elimination. Therefore, the importance of cancer treatment techniques that take into account the tumor microenvironment (TME) is increasing day by day.

[0043] Therefore, the inventors conducted research into biomarkers that can predict the therapeutic response of cancer patients to immunotherapy, taking into account the tumor microenvironment, in order to enable appropriate and accurate therapeutic intervention. As a result, by using the GeoMx platform to perform spatial transcriptome interpretation of pathological tissues and the tumor microenvironment, they were able to discover novel biomarkers that could not be discovered by conventional methods.

[0044] Here, the tumor microenvironment (TME) refers to the cellular network surrounding cancer cells, including blood vessels and immune cells that exist around cancer cells and promote tumor survival and growth, fibroblasts that support cancer cells, bone marrow-derived inflammatory cells, various lymphocytes, signaling molecules, and extracellular matrix such as collagen and fibronectin.

[0045] According to one embodiment of the present invention, In order to find biomarkers that predict the therapeutic response to cancer immunotherapy, cancer patients who received standard treatment were divided into a recurrence group and a non-recurrence group, and pathological tissue was collected from each group.

[0046] Using pathological tissues obtained from cancer patients, spatial transcriptomics was applied to interpret genes with differential expression patterns between the two groups, recurrence and non-recurrence after immunotherapy. Regions of interest in the tumor microenvironment were selected from tissue sections, and the distribution of cells and gene expression levels in each region of interest were analyzed.

[0047] Regions of interest (ROI) refer to specific regions within a pathological tissue selected for the purpose of analyzing cell type distribution and transcriptome. In the present invention, the regions of interest include tumor, immune, and stromal regions. A tumor region refers to a region that shows a positive reaction to a PanCK antibody, i.e., a region where PanCK is expressed.

[0048] The immune region refers to the region that shows a positive reaction to the C45 antibody, and the interstitial region refers to the region of the extracellular matrix containing stromal cells.

[0049] After immunotherapy, the distribution of cells in the recurrence (non-response) and non-recurrence (response) groups was analyzed for selected regions of interest, taking into account the tumor microenvironment. In the PanCK+ region, CD8 T cells accounted for 25.48% of the total cells in the non-recurrence group and 16.40% in the recurrence group. In the stromal region, in addition to the anti-tumor microenvironment, angiogenesis and fibroblast function were also higher in the non-recurrence group compared to the recurrence group.

[0050] Furthermore, a comparison was performed between the recurrent and non-recurrent groups in the region of interest to search for transcriptomes with differential expression levels. The results revealed that 17 genes, including NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G, showed significant differences.

[0051] These 17 genes are biomarkers that could not be found by conventional gene expression analysis using RNA sequencing, but were discovered by transcriptome interpretation based on spatial regions of interest according to the present invention.

[0052] Therefore, the present invention can provide a biomarker composition for predicting therapeutic response to cancer immunotherapy, comprising one or more genes selected from the group consisting of NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G, or proteins encoded by the genes.

[0053] Specifically, the names and NCBI genebank numbers of the 17 genes discovered in the present invention that function as biomarkers for predicting therapeutic response to cancer immunotherapy are as follows:

[0054] [ka]

[0055] Cancer diseases to which the biomarkers of the present invention can be applied may be, but are not limited to, cancers selected from the group consisting of ovarian cancer, bile duct cancer, colon cancer, adenocarcinoma, rectal cancer, breast cancer, synovial sarcoma, chondrosarcoma, thyroid cancer, gastric cancer, thymic cancer, cervical cancer, glioma, brain cancer, melanoma, lung cancer, bladder cancer, prostate cancer, leukemia, kidney cancer, liver cancer, colorectal cancer, pancreatic cancer, lymphoma, uterine cancer, oral cancer, bronchial cancer, nasopharyngeal cancer, laryngeal cancer, skin cancer, blood cancer, parathyroid cancer, and ureteral cancer, and are preferably ovarian cancer.

[0056] In the present invention, "predicting therapeutic responsiveness to cancer immunotherapy" means predicting whether immunotherapy (e.g., immunoanticancer drugs) will be effective for a cancer patient, predicting the risk of resistance to immunotherapy, and predicting the prognosis of a patient after immunotherapy, i.e., recurrence, metastasis, survival, etc.

[0057] Furthermore, "immunotherapy" in the present invention refers to an anticancer drug that activates the body's immune cells to attack cancer cells, and may be a drug that enhances the patient's immune system and provides a therapeutic effect against cancer, i.e., an immunotherapy drug.

[0058] The immune therapy may be immune cell therapy, which may be one selected from the group consisting of NK cell therapy, T cell therapy, CAR-T cell therapy, DC vaccine, anti-PD-L1, anti-PD-1, and anti-CTLA-4, but is not limited thereto.

[0059] Furthermore, the present invention can provide a composition for predicting therapeutic responsiveness to cancer immunotherapy, comprising a preparation capable of measuring the expression level of mRNA or protein of one or more genes selected from the group consisting of NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G.

[0060] The expression level of the gene is measured in a region of interest in the tumor microenvironment of diseased tissue collected from a cancer patient, which may be a tumor region where PanCK is expressed, an immune region where CD45 is expressed, or a stromal region.

[0061] According to one embodiment of the present invention, a comparison was made between a recurrent group and a non-recurrent group that underwent immunotherapy to identify genes with differential expression levels in each region of interest. As a result, it was confirmed that there were differential expression levels for the NKG7, ULBP3, FPR2, and MYC genes in the immune region, CXCL10 and NECTIN2 in the tumor region, and CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G in the stromal region.

[0062] In the present invention, a "preparation capable of measuring the expression level of a gene" may be a preparation that directly or indirectly measures the expression of the above-mentioned gene or the protein encoded by the gene in a specimen from a cancer patient in order to predict therapeutic response to immunotherapy (immunotherapy anticancer drug). The "expression level of a gene" may be confirmed by measuring the expression level of the mRNA of the gene or the expression level of the protein encoded by the gene. The expression level of a gene can be used as a biomarker, and specifically, the assessment of therapeutic response to cancer immunotherapy may differ depending on the expression level of the gene.

[0063] Furthermore, "measuring the expression level of mRNA" refers to a process of confirming the presence of mRNA of the gene and its expression level in a biological sample of a subject, and may be measuring the amount of mRNA. "A preparation capable of measuring the expression level of mRNA" may be a primer or probe that specifically binds to the gene.

[0064] The expression level of mRNA can be measured by any known method, such as reverse transcription polymerase chain reaction (RT-PCR), competitive RT-PCR, real-time RT-PCR, RNase protection assay (RPA), Northern blotting, DNA chip, etc. In one embodiment of the present invention, the GeoMx digital spatial profiling (DSP) method is used.

[0065] "Measuring the expression level of a protein" refers to a process of confirming the presence and expression level of a protein encoded by the gene in a biological sample of a subject, and may involve measuring the amount of the protein. Any known method for measuring the expression level of a protein may be used, including, for example, Western blotting, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), radial immunodiffusion, Ouchterlony double immunodiffusion, rocket immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, fluorescence activated cell sorting (FACS), and protein chip. A "preparation capable of measuring the expression level of a protein" may be an antibody that specifically binds to the protein of the gene.

[0066] Furthermore, the present invention can provide a kit for predicting therapeutic response to cancer immunotherapy, which comprises the composition of the present invention.

[0067] The kit can detect biomarkers by determining the mRNA expression level or protein expression level of the gene. The kit may further include primers, probes, and antibodies that selectively recognize biomarkers for measuring the expression level of the gene, the expression level of which increases or decreases depending on the therapeutic response to immunotherapy, as well as compositions, solutions, or devices of one or more components suitable for the analytical method.

[0068] For example, a kit for measuring the expression level of mRNA of the gene may be a kit including an RT-PCR set, and the RT-PCR kit may include, in addition to primers that specifically bind to the gene, test tubes, other necessary containers as appropriate, a reaction buffer solution, deoxynucleotides (dNTPs), enzymes such as Taq polymerase and reverse transcriptase, DNase, RNase inhibitor, DEPC-water, sterilized water, etc.

[0069] The kit for measuring the expression level of the protein may contain a substrate, an appropriate buffer solution, a secondary antibody labeled with a chromogenic enzyme or a fluorescent substance, a chromogenic substrate, and the like for immunological detection of antibodies. Examples of the substrate include a nitrocellulose membrane, a 96-well plate made of polyvinyl resin, a 96-well plate made of polystyrene resin, and a glass slide. Examples of the chromogenic enzyme include peroxidase and alkaline phosphatase, and examples of the fluorescent substance include FITC and RITC. Examples of the chromogenic substrate solution include ABTS (2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)), OPD (o-phenylenediamine), and TMB (tetramethylbenzidine).

[0070] Furthermore, the present invention can provide a method for providing information for predicting therapeutic response to cancer immunotherapy, which comprises measuring the expression level of the biomarker gene according to the present invention or the protein of the gene.

[0071] Specifically, the method for providing information for predicting therapeutic response to cancer immunotherapy includes (1) measuring the expression level of one or more genes selected from the group consisting of NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G, or the protein of said genes, in a biological sample collected from a cancer patient, and (2) predicting therapeutic response to cancer immunotherapy based on the measured expression level of said mRNA or protein.

[0072] In one embodiment of the present invention, a TMA was constructed using pathological tissue collected from a cancer patient, taking into account the tumor microenvironment, and biomarkers with significant differences in expression between the recurrence and non-recurrence groups were selected for each region of interest. In the immune region where CD45 is expressed, the non-recurrence group (responder) showed higher expression of NK37 and ULBP3, and lower expression of FPR2 and MYC, compared to the recurrence group (non-responder).

[0073] Furthermore, in tumor areas where PanCK was expressed, it was confirmed that the expression levels of CXCL10 and NECTIN2 were lower in the non-recurrence group (response group) than in the recurrence group (non-response group).

[0074] In the stroma region, another area of ​​interest, higher expression of CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G was observed in the non-recurrence group (response group) compared to the recurrence group (non-response group).

[0075] Therefore, based on the above results, the inventors can predict the therapeutic response to cancer immunotherapy by interpreting the expression level and expression pattern of the above genes in a specific region of interest, and can also determine a treatment strategy for immunotherapy.

[0076] Therefore, when the expression level of NKG7 or ULBP3 in the immune region is equal to or higher than a defined expression threshold, or when the expression level of FPR2 or MYC is equal to or lower than a defined expression threshold, the cancer patient can be determined to have a high therapeutic response to cancer immunotherapy, and therefore, immunotherapy (immunoanticancer drugs) may be considered as a treatment option for the patient.

[0077] Furthermore, when the expression level of CXCL10 or MECTIN2 in the tumor area is below a defined expression threshold, the cancer patient can be determined to have a high therapeutic response to cancer immunotherapy, and therefore, the patient may be considered for immunotherapy (immunotherapy-based anticancer drugs) as a treatment option.

[0078] Furthermore, when the expression level of CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, or HLA-G in the stromal region is equal to or greater than a defined expression threshold, the cancer patient can be determined to have a high therapeutic response to cancer immunotherapy, and therefore, immunotherapy (immunoanticancer drugs) may be considered as a treatment option for the patient.

[0079] In the present invention, the term "defined expression threshold" refers to a threshold that indicates the optimal expression level of a specific marker gene that can distinguish between a recurrence group and a non-recurrence group. The threshold is a value that maximizes the sensitivity and specificity determined by logistic regression. A preferred threshold should be determined by analyzing a sufficient sample size, taking into account the statistical variability of the prediction model.

[0080] In the present invention, the therapeutic response of cancer patients to immunotherapy can be predicted by comparing the expression levels of biomarker genes with defined expression thresholds.

[0081] In one embodiment of the present invention, the expression thresholds defined for the 17 biomarker genes are summarized in Table 3.

[0082] For example, if the expression level of the NKG7 gene in the immune region is greater than 6.138, which corresponds to a defined expression threshold, the patient will be highly responsive to immunotherapy, and immunotherapy may be considered as a treatment option.

[0083] As another example, if the expression level of the CXCL10 gene in the tumor area is less than 3.478, which corresponds to a defined expression threshold, the patient will be highly responsive to immunotherapy, and immunotherapy may be considered as a treatment option.

[0084] Furthermore, the present invention can provide a method for providing information for predicting the survival prognosis of a cancer patient, which comprises measuring the expression level of one or more genes selected from the group consisting of NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G, or proteins of the genes, in a biological sample collected from the cancer patient.

[0085] By interpreting the expression level of the gene in a specific region of interest, it is possible to predict not only the therapeutic response to immunotherapy but also the survival prognosis of cancer patients.

[0086] In the prediction of survival prognosis for a cancer patient according to the present invention, when the expression level of the NKG7 or ULBP3 is equal to or higher than a defined expression threshold, or the expression level of the FPR2 or MYC is equal to or lower than a defined expression threshold in the immune region where the CD45 is expressed, the cancer patient can be determined to have a high progression-free survival (PFS) rate.

[0087] Furthermore, when the expression level of CXCL10 or MECTIN2 in the tumor area where PanCK is expressed is below a defined expression threshold, it can be determined that the cancer patient has a high progression-free survival rate (PFS).

[0088] Furthermore, when the expression level of CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, or HLA-G in the stromal region is equal to or higher than a defined expression threshold, the progression-free survival rate (PFS) of the cancer patient can be determined to be high.

[0089] According to one embodiment of the present invention, the correlation between the expression level of the gene biomarkers according to the present invention and the survival rate of cancer patients was analyzed, and it was found that, similar to the therapeutic response to immunotherapy, a high expression level of the NKG7 gene in the CD45 expression region was associated with a high progression-free survival rate and a good survival prognosis for cancer patients. In another embodiment, it was found that a high expression level of the OX40, INF-b, and HLA-DOA1 genes in the stromal region was also associated with a high progression-free survival rate and a good survival prognosis for cancer patients.

[0090] Based on the above results, the inventors confirmed that the biomarkers discovered through spatial transcriptome interpretation take into account the tumor microenvironment, and can improve the accuracy and precision of predicting therapeutic response to cancer immunotherapy, and even enable survival prognosis prediction. [Example]

[0091] The present invention will be explained in more detail below with reference to examples and comparative examples, but the present invention is not limited to these examples in any way.

[0092] <Experimental Method> Patient groups and tissue microarray (TMA) construction In this study, 15 ovarian cancer patients and subjects who had received standard treatment were included. Based on their response to standard therapy, they were classified into non-recurrence (no recurrence) (NR, n = 4), platinum-sensitive (PS, n = 8), platinum-resistant (PR, n = 1), and refractory (RF, n = 2) groups. Tissue microarrays (TMAs) were stained with hematoxylin and eosin to select representative tumor areas, and tumor locations were reviewed by a pathologist. The selection of tumor core areas was not based on a specific tumor segment or location. Table 1 below summarizes the clinicopathological characteristics of the patients who participated in this study.

[0093] [Table 1]

[0094] Digital spatial profiling (DSP) Analysis of tissue microarrays (TMAs) collected from patients with gynecological cancer was performed using the Human Whole Transcriptome Atlas Nanostring GeoMx Profiling Reagents (Nanostring Technologies, Seattle, WA, USA). Target-specific RNA probes were covalently linked to the TMA (in situ hybridization, ISH) and reacted with specific morphological markers conjugated to photocleavable (PC) index oligos. In the present example, the TMA-specific morphological markers used were: TMA1: PanCK, SMA, PTPRC (CD45), DNA; TMA2: KRT5, ACTA2, PTPRC (CD45), DNA. The labeled TMs were scanned using a GeoMx DSP system (NanoString Technologies, USA) to confirm the overall tissue composition.

[0095] Regions of interest (ROIs) were fluorescently labeled or automatically positioned for multiplex profiling. After automated tissue segmentation, oligonucleotides in the selected ROIs were released by local UV irradiation. The photocleaved oligos were collected by aspiration through a microcapillary tube and dispensed into a 96-well plate. UV irradiation and oligo collection were repeated for each ROI. The oligos collected from the UV-irradiated area of ​​illumination (AOI) were amplified using a PCR system. AMPure XP beads (Beckman Coulter Diagnostics, CA, USA) were used to purify the PCR reaction products. The purified libraries were then collected and sequenced.

[0096] Qualitative and quantitative evaluation of the libraries was performed using Qubit 4.0 (Thermofisher Scientific, Waltham, MA, USA) and Tapesation 4200 (Agilent Technologies, Santa Clara, CA, USA), and sequencing was performed using NovaSeq 6000 (Illumina, San Diego, CA, USA).

[0097] Data Processing Sequencing data from digital spatial profiling was processed using the GeoMx® NGS pipeline. After sequencing, reads were aligned to display probe-specific IDs. PCR duplicates were removed using the region of each read's unique identifier, and reads were converted to digital counts. Gene presence was defined as the limit of quantitation (LOQ), which is the geometric mean of negative probes, multiplied by the square of the geometric standard deviation. Of the 18,677 genes analyzed, 16,312 (86.4%) exceeded the LOQ in 10% of AOIs. Variation within AOIs was also accounted for using Q3 regularization (75% of each sample) for downstream interpretation.

[0098] Gene Expression Interpretation To perform cell abundance analysis for spatial gene expression datasets in each region of interest, we employed the R package "SpatialDecon," which performs mixture deconvolution using a constrained log-normal regression model (Grisworld, 2022).

[0099] For deconvolution, we used the SafeTME matrix, a cell profile matrix of immune and stromal cell types found in tumors (Danaher, 2022). We identified differentially expressed genes using the R package "edgeR" (Robinson), and identified differentially expressed gene sets between response groups using the camera function in the R package "limma" (Ritchie, 2015). We used the Hallmark gene set from MSigDB (Liberzon, 2015).

[0100] For data visualization, we used the R package "ggplot2" to create pie charts, and the "ggtern" package to generate ternary plots to show cell abundance and abundance in the AOI (Hamilton, 2018).

[0101] The relative expression levels of each functional gene expression profile (Fges) in the tumor microenvironment were interpreted by applying the single-sample gene set enrichment analysis (ssGSEA) algorithm to each sample (Bagaev, 2021).

[0102] statistical analysis Survival analysis was performed using the Cox proportional hazards model in the survival R package, and the Wilcoxon rank sum test was used for comparison between two groups.

[0103] Example 1 Generating gene profiles of regions of interest based on spatial transcriptome interpretation To identify genetic biomarkers associated with immunogenetic responses in ovarian cancer patients (biomarkers predictive of immunotherapy response), we prepared FFPE (fromalin-fixed paraffin-embedded) slides from 15 patients who received standard treatment. TMAs were constructed, and the NanoString GeoMx digital spatial profiler was used to visualize tumor, immune, and stromal regions using fluorescent markers (morphological markers) for PanCK+, CD45, and stroma antibodies. Next, one to two regions of interest (ROIs) were selected for each patient segment, and RNA sequencing was performed on a total of 75 ROIs. Table 2 below summarizes the prevalence of each cell type for PanCK+, CD45, and stroma.

[0104] [Table 2]

[0105] The cell type abundances in the ovarian cancer ROIs were interpreted using deconvolution. As shown in Figure 2a and Table 2, the CD45 region contained significant amounts of CD8 T cells and macrophages (35.91% and 29.105%, respectively). The PanCK+ region contained more CD4 T cells, CD8 T cells, and neutrophils than other cell types (22.75%, 19.17%, and 27.24%, respectively). Meanwhile, the stromal region contained relatively more fibroblasts, endothelial cells, and CD8 T cells (27.63%, 10.54%, and 18.97%, respectively).

[0106] In addition, for each cell type in each region of interest (PanCK+, CD45, Stroma), a comparison was made between the non-recurrence group (no recurrence, response group) and the recurrence group (recurrence, non-response group). As shown in Figure 2B, the presence rate of CD8- T cells in the PanCK+ region was 25.48% of the total in the non-recurrence group, but only 16.40% in the recurrence group.

[0107] <Example 2> Spatial information-based selection of immunotherapy response biomarkers We interpreted biomarkers associated with the tumor microenvironment based on homologous recombination repair deficiency (HRD) status.

[0108] The results confirmed that CD8A expression showed significant differences depending on HRD status in the interstitial region (P = 0.019) (Figures 3A and 3B). No significant differences in CD8A expression were observed in other regions. This difference was more pronounced in patients in the non-relapse group than in patients in the platinum-sensitive group (Figure 3C). Furthermore, CD8A expression was found to be highly correlated with genes related to the STING pathway (STING, IFNB1, CXCL9) (Figure 3D).

[0109] In addition, differentially expressed genes were identified in the response groups of each segment (Figure 4A), and the distribution of gene functions was also confirmed from characteristic gene sets for the three different segments (Figure 4B). Gene sets related to "interferon α / γ response" were present in significant amounts in all segments. While the interferon α / γ response gene set was activated in the non-recurrence group, activation of "epithelial-metastatic transition (EMT)" was observed in the recurrence group (Figure 4C).

[0110] Furthermore, the functional components of the tumor microenvironment, including anti-tumor microenvironment, angiogenesis, and fibroblasts, were stronger in the responding group (non-recurrence group) than in the non-responding group (recurrence group) (Figure).

[0111] Example 3 Selection of immunotherapy response biomarkers by area of ​​interest The inventors selected biomarker candidates for each region of interest (CD45, PanCK, Stroma) from among genes involved in immune responses. Specifically, biomarkers showing a statistically significant difference in expression between the non-recurrence group and the recurrence group were selected for each region of interest from biological samples of cancer patients from which normal tissue had been removed, and statistical tests were performed using the Wilcoxon rank sum test. The analysis results are shown in Table 3 below.

[0112] [Table 3]

[0113] The "Expression Ratio" column in Table 3 indicates whether the gene expression level is higher in the non-recurrence group compared to the recurrence group, and the "P-value" column is the defined expression threshold, which corresponds to the reference value indicating the optimal expression level that can distinguish between the two groups (recurrence and non-recurrence). This reference value is the value that maximizes the sensitivity and specificity determined by logistic regression. The "Function" column relates to the function that each biomarker plays in the tumor microenvironment.

[0114] As a result of the analysis, as shown in Table 3 and Figure 6, four genes (NKG7, ULBP3, FPR2, MYC) showed different expression levels between the two groups in the CD45 region, the CXCL10 and NECTIN2 genes showed lower expression levels in the non-recurrence group in the PanCK+ region, and 11 genes (CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, HLA-G) showed higher expression levels in the stromal region in the recurrence group.

[0115] The 17 biomarkers selected based on the above results are: This is a biomarker that shows a significant difference in expression between recurrence and non-recurrence groups in specific regions of interest in tissues collected from ovarian cancer patients, and we have succeeded in identifying a biomarker that could not be discovered using conventional RNA-seq methods. The identified novel biomarker has been confirmed to be useful for predicting therapeutic response to immunotherapy for solid tumors, particularly ovarian cancer.

[0116] Example 4 Validation of survival using biomarkers predicting response to selected immunotherapy treatments Furthermore, the present inventors analyzed survival rates for the immunotherapy treatment response predictive biomarkers discovered in "Example 3" to confirm whether they can also be used to predict the prognosis of immunotherapy.

[0117] Figure 7 summarizes progression-free survival (PFS) rates according to the expression levels of NKG7, OX40, INF-b, and HLA-DOA1. Patients with high NKG7 expression in the CD45 region (above the median) had a higher survival rate and better prognosis at a significance level of P=0.0059. Patients with high expression of OX40, INF-b, and HLA-DOA1 in the stromal region had a better prognosis at significance levels of P=0.0062, 0.027, and 0.018, respectively.

[0118] Furthermore, the present inventors verified the association between the biomarkers according to the present invention and survival prognosis using a gynecological cancer (cervical squamous cell carcinoma and endocervical adenocarcinoma) cohort (public dataset) obtained from TCGA (The Cancer Genome Atlas), a large-scale cancer gene database (Figure 8).

[0119] As a result, as mentioned above, the NKG7, OX40, INF-b, and HLA-DOA1 genes showed a good prognosis, i.e., a high survival rate, in the group with high expression levels at significance levels of P=0.059, 0.014, 0.33, and 0.016, respectively.

[0120] From the above results, it can be seen that the inventors have succeeded in selecting biomarkers that can predict the therapeutic response of cancer patients to immune cell therapy, taking into account the composition of the tumor microenvironment, thereby improving the accuracy and precision of predicting the therapeutic response of immunotherapy and predicting patient survival prognosis, and providing biomarkers that can be useful in determining treatment strategies.

[0121] The present invention has been described above with reference to preferred embodiments. Those skilled in the art will understand that numerous changes and modifications can be made to the embodiments, and that such changes and modifications can be made within the scope of the present invention. The scope of the present invention is defined by the claims, not the above description, and is intended to include all modifications within the meaning and scope of the claims.

Claims

1. A biomarker composition for predicting therapeutic response to cancer immunotherapy, comprising one or more genes selected from the group consisting of NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G, or proteins encoded by the genes.

2. 2. The biomarker composition for predicting therapeutic response to cancer immunotherapy according to claim 1, wherein the cancer is one selected from the group consisting of ovarian cancer, bile duct cancer, colon cancer, adenocarcinoma, rectal cancer, breast cancer, synovial sarcoma, chondrosarcoma, thyroid cancer, gastric cancer, thymic cancer, cervical cancer, glioma, brain cancer, melanoma, lung cancer, bladder cancer, prostate cancer, leukemia, kidney cancer, liver cancer, colorectal cancer, pancreatic cancer, lymphoma, uterine cancer, oral cancer, bronchial cancer, nasopharyngeal cancer, laryngeal cancer, skin cancer, blood cancer, parathyroid cancer, and ureteral cancer.

3. 2. The biomarker composition for predicting therapeutic response to cancer immunotherapy according to claim 1, wherein the immunotherapy is one selected from the group consisting of NK cell therapy, T cell therapy, CAR-T cell therapy, DC vaccine, anti-PD-L1, anti-PD-1, and anti-CTLA-4.

4. A composition for predicting therapeutic responsiveness to cancer immunotherapy, comprising a preparation capable of measuring the expression level of mRNA or protein of one or more genes selected from the group consisting of NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G.

5. 5. The composition for predicting therapeutic response to cancer immunotherapy according to claim 4, characterized in that the expression level of the gene is measured in a region of interest in the tumor microenvironment of diseased tissue collected from a cancer patient.

6. The composition for predicting therapeutic responsiveness to cancer immunotherapy described in claim 5, characterized in that the area of ​​interest is a tumor area where PanCK is expressed, an immune area where CD45 is expressed, or a stromal area.

7. The expression level of NKG7, ULBP3, FPR2, or MYC is measured from an immune region where CD45 is expressed; The expression level of CXCL10 or NECTIN2 is measured from an immune region where PanCK is expressed, The composition for predicting therapeutic responsiveness to cancer immunotherapy described in claim 4, characterized in that the expression levels of CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C or HLA-G are measured from immune regions where PanCK is expressed.

8. The composition for predicting therapeutic responsiveness to cancer immunotherapy described in claim 4, characterized in that the preparation capable of measuring the expression level of the mRNA is a primer or probe that specifically binds to the gene.

9. A composition for predicting therapeutic responsiveness to cancer immunotherapy described in claim 4, characterized in that the preparation capable of measuring the expression level of the protein is an antibody that specifically binds to the protein of the gene.

10. 5. The composition for predicting therapeutic responsiveness to cancer immunotherapy according to claim 4, wherein the cancer is one selected from the group consisting of ovarian cancer, bile duct cancer, colon cancer, adenocarcinoma, rectal cancer, breast cancer, synovial sarcoma, chondrosarcoma, thyroid cancer, gastric cancer, thymic cancer, cervical cancer, glioma, brain cancer, melanoma, lung cancer, bladder cancer, prostate cancer, leukemia, kidney cancer, liver cancer, colorectal cancer, pancreatic cancer, lymphoma, uterine cancer, oral cancer, bronchial cancer, nasopharyngeal cancer, laryngeal cancer, skin cancer, blood cancer, parathyroid cancer, and ureteral cancer.

11. 5. The composition for predicting therapeutic responsiveness to cancer immunotherapy according to claim 4, wherein the immunotherapy is one selected from the group consisting of NK cell therapy, T cell therapy, CAR-T cell therapy, DC vaccine, anti-PD-L1, anti-PD-1, and anti-CTLA-4.

12. A kit for predicting therapeutic response to cancer immunotherapy, comprising the composition of claim 1 or claim 4.

13. (1) measuring the expression level of one or more genes selected from the group consisting of NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G, or the proteins of the genes, in a biological sample collected from a cancer patient; (2) A method for providing information for predicting therapeutic response to cancer immunotherapy, comprising predicting therapeutic response to cancer immunotherapy based on the measured expression level of the mRNA or protein.

14. 14. The information providing method for predicting therapeutic response to cancer immunotherapy described in claim 13, characterized in that the expression level in (1) is measured in a region of interest in the tumor microenvironment of the biological sample.

15. The information providing method for predicting therapeutic response to cancer immunotherapy described in claim 14, characterized in that the region of interest is a tumor region where PanCK is expressed, an immune region where CD45 is expressed, or a stromal region.

16. The expression level of NKG7, ULBP3, FPR2, or MYC is measured from an immune region where CD45 is expressed; The expression level of CXCL10 or NECTIN2 is measured from an immune region where PanCK is expressed, The information providing method for predicting therapeutic responsiveness to cancer immunotherapy described in claim 14, characterized in that the expression levels of CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C or HLA-G are measured from immune regions where PanCK is expressed.

17. The method for providing information for predicting the therapeutic response to cancer immunotherapy described in claim 16, characterized in that predicting the therapeutic response to cancer immunotherapy in (2) comprises determining that the cancer patient has a high therapeutic response to cancer immunotherapy when the expression level of the NKG7 or ULBP3 is equal to or higher than a defined expression threshold, or when the expression level of the FPR2 or MYC is equal to or lower than a defined expression threshold.

18. The information providing method for predicting the therapeutic response to cancer immunotherapy described in claim 16, characterized in that predicting the therapeutic response to cancer immunotherapy in (2) comprises determining that the cancer patient has a high therapeutic response to cancer immunotherapy when the expression level of CXCL10 or MECTIN2 is below a defined expression threshold.

19. 17. The information providing method for predicting the therapeutic response to cancer immunotherapy described in claim 16, characterized in that predicting the therapeutic response to cancer immunotherapy in (2) comprises determining that the cancer patient has a high therapeutic response to cancer immunotherapy when the expression level of CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C or HLA-G is equal to or higher than a defined expression threshold.

20. A method for providing information for predicting the survival prognosis of a cancer patient, comprising measuring the expression level of one or more genes selected from the group consisting of NKG7, ULBP3, FPR2, MYC, CXCL10, NECTIN2, CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C, and HLA-G, or the proteins of said genes, in a biological sample collected from the cancer patient.

21. 21. The method for providing information for predicting the survival prognosis of cancer patients according to claim 20, wherein the expression level is measured in a tumor region where PanCK is expressed, an immune region where CD45 is expressed, or a stromal region, which correspond to regions of interest in the tumor microenvironment, for the biological sample.

22. 22. The method of providing information for predicting the survival prognosis of a cancer patient according to claim 21, wherein the cancer patient is judged to have a high progression-free survival (PFS) if the expression level of NKG7 or ULBP3 is equal to or higher than a defined expression threshold, or the expression level of FPR2 or MYC is equal to or lower than a defined expression threshold, in the immune region where CD45 is expressed.

23. 22. The method of providing information for predicting the survival prognosis of a cancer patient described in claim 21, characterized in that if the expression level of CXCL10 or MECTIN2 in the tumor area where PanCK is expressed is below a defined expression threshold, the cancer patient is judged to have a high progression-free survival rate (PFS).

24. 22. The method of providing information for predicting survival prognosis of a cancer patient according to claim 21, wherein the cancer patient is judged to have a high progression-free survival rate (PFS) when the expression level of CD8A, HLA-DQA1, BMP2, INF-b, TNF-b, IL6, OX40-L, OX40, Tim3, HLA-C or HLA-G in the stromal region is equal to or higher than a defined expression threshold.

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