Tumor microenvironment immunotyping method

Abstract

The embodiment of the invention provides a tumor microenvironment immune typing method, electronic equipment, a readable storage medium, a tumor microenvironment immune typing model obtained through the method and application of the tumor microenvironment immune typing model. The method comprises the following steps: obtaining a tumor marker pathway based on clinical phenotype, transcriptome data and tumor microenvironment characteristics of a sample; dividing the tumor marker pathway into one or more tumor microenvironment modules and calculating a tumor microenvironment module score of the sample; and determining a tumor microenvironment immunotyping of the sample based on the tumor microenvironment module score. The method is wide in application range, simple in process and high in repeatability, and supporting reference can be stably provided for clinical cancer immunotherapy.

Description

Technical Field

[0001] The present application relates to the field of bioinformatics, and specifically to a tumor microenvironment immunophenotyping method, an electronic device, a readable storage medium, a tumor microenvironment immunophenotyping model obtained by the method, and uses thereof. Background Art

[0002] Immunotherapy is an important cancer treatment strategy. The tumor microenvironment (TME) is the microenvironment in which tumor cells survive. The immune cells and their regulation methods in the TME have been widely considered to have an important impact on the occurrence and development of tumors. Analyzing the characteristics of the patient's tumor immune microenvironment can help customize personalized immunotherapy plans and achieve better clinical results.

[0003] Among the related technologies, detection of tumor genomic characteristics is more common in cancer clinical trials and standard treatments, but genomic characteristics usually rely on targeted groups with a limited number of genes and cannot reflect the complex interactions between complex tumors and the human immune system; transcriptomic analysis can be used to analyze the complexity and heterogeneity of the tumor microenvironment.

[0004] Therefore, there is an urgent need to provide a tumor microenvironment immunophenotyping method and immunophenotyping markers based on transcriptome information for the purpose of evaluating the prognosis and / or efficacy of tumor immunotherapy. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the embodiments of the present invention provide a tumor microenvironment immunophenotyping method, an electronic device, a readable storage medium, a tumor microenvironment immunophenotyping model obtained by the method, and uses thereof.

[0006] The first aspect of the embodiment of the present application provides a tumor microenvironment immunophenotyping method, the method comprising: obtaining a tumor marker pathway based on the clinical phenotype, transcriptome data and tumor microenvironment characteristics of the sample; dividing the tumor marker pathway into one or more tumor microenvironment modules and calculating the tumor microenvironment module score of the sample; and determining the tumor microenvironment immunophenotyping of the sample based on the tumor microenvironment module score. The method has a wide range of applications, a simple process, and high repeatability, and can stably provide a supportive reference for clinical cancer immunotherapy.

[0007] In some embodiments, the obtaining of a tumor marker pathway based on the clinical phenotype, transcriptome data, and tumor microenvironment characteristics of the sample comprises: dividing the sample into one or more response groups and one or more non-response groups based on the clinical phenotype of the sample; performing differential expression analysis on the transcriptome data of the samples of the response group and the non-response group to obtain differentially expressed genes; performing pathway enrichment analysis on the differentially expressed genes to obtain an initial tumor marker pathway, wherein the initial tumor marker pathway contains the differentially expressed genes; and screening the initial tumor marker pathway according to the tumor microenvironment characteristics of the sample to obtain the tumor marker pathway.

[0008] In some embodiments, the p-value of the differentially expressed gene is less than 0.05, less than 0.01, or less than 0.001, preferably less than 0.05.

[0009] In some embodiments, the clinical phenotype is at least one of tumor residual ratio, RECIST.

[0010] In some embodiments, the clinical phenotype is tumor residual ratio.

[0011] In some embodiments, the performing pathway enrichment analysis on the differentially expressed genes to obtain an initial tumor marker pathway comprises: performing one or more gene set enrichment analyses on the differentially expressed genes based on a tumor marker gene set to obtain a candidate initial tumor marker pathway; and testing the candidate initial tumor marker pathway to determine the initial tumor marker pathway, wherein the gene set enrichment analysis is a GSEA gene set enrichment analysis, and the tumor marker gene set is a MsigDB-Hallmark gene set.

[0012] In some embodiments, the q value of the initial tumor marker pathway is less than 0.05, less than 0.01 or less than 0.001, preferably less than 0.05.

[0013] In some embodiments, screening the initial tumor marker pathway according to the tumor microenvironment characteristics of the sample to obtain the tumor marker pathway comprises: screening the initial tumor marker pathway associated with the occurrence and development of solid tumors to obtain the tumor marker pathway.

[0014] In some embodiments, dividing the tumor marker pathway into one or more tumor microenvironment modules and calculating the tumor microenvironment module score of the sample specifically includes: dividing the tumor marker pathway into one or more tumor microenvironment modules; and calculating the mean score of the tumor marker pathway in the one or more tumor microenvironment modules, and using the mean score as the tumor microenvironment module score, wherein the tumor microenvironment module includes an immune enrichment (IE) module, a tumor proliferation (TP) and / or a fibroblast enrichment (FE) module, and the mean score is ssGSEAscore.

[0015] In some embodiments, said dividing said tumor marker pathway into one or more tumor microenvironment modules comprises:

[0016] HALLMARK_ALLOGRAFT_REJECTION, HALLMARK_INTERFERON_ALPHA_RESPONSE, and HALLMARK_INTERFERON_GAMMA_RESPONSE are classified into the immune enrichment (IE) module.

[0017] HALLMARK_MTORC1_SIGNALING, HALLMARK_DNA_REPAIR, HALLMARK_E2F_TARGET, HALLMARK_MYC_TARGETS_V1, HALLMARK_HYPOXIA were classified into the tumor proliferation (TP) module

[0018] The HALLMARK_KRAS_SIGNALING_UP, HALLMARK_ANGIOGENESIS, HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, HALLMARK_MYOGENESIS, HALLMARK_TGF_BETA_SIGNALING pathways were grouped into the fibroblast enrichment (FE) module.

[0019] In some embodiments, the tumor microenvironment immune subtype includes immune-enriched type (IE), tumor proliferation type (TP) and / or fibroblast-enriched type (FE), and determining the tumor microenvironment immune subtype of the sample based on the tumor microenvironment module score includes: determining the tumor microenvironment immune subtype of the sample through a decision tree model based on the tumor microenvironment module score.

[0020] In some embodiments, the decision tree model comprises: determining that the tumor microenvironment immunophenotyping of the sample is immune enrichment (IE) type based on the immune enrichment (IE) module score being greater than 0 and greater than 3 times the fibroblast enrichment (FE) score;

[0021] Based on the immune enrichment (IE) module score being greater than 0 and the tumor proliferation (TP) module score being less than 0, determining that the tumor microenvironment immunophenotyping of the sample is fibroblast-enriched (FE) type;

[0022] Based on the immune enrichment (IE) module score being less than 0 and the fibroblast enrichment (FE) module score being greater than 0, determining that the tumor microenvironment immunophenotyping of the sample is fibroblast enrichment (FE) type;

[0023] Based on the immune enrichment (IE) module score being less than 0 and the fibroblast enrichment (FE) module score being less than 0, the tumor microenvironment immunophenotyping of the sample is determined to be a tumor proliferation (TP) type.

[0024] In some embodiments, the tumor is a pan-solid tumor, preferably one or more of bile cancer, multiforme glioma, lung squamous cell carcinoma, lung adenocarcinoma, liver cancer, testicular cancer, cervical squamous cell carcinoma, bone marrow cancer, thyroid cancer, colon cancer, lymphoma, ovarian cancer, brain low-grade glioma, bladder cancer, skin cancer, prostate cancer, papillary renal cell carcinoma, breast cancer, soft tissue cancer, adrenal cancer, renal clear cell carcinoma, renal chromophobe cell carcinoma, esophageal cancer, head and neck squamous cell carcinoma, gastric cancer, adenoid cystic carcinoma, pleural cancer, thymic cancer, eye cancer, pancreatic cancer, rectal cancer, uterine cancer or endometrial cancer, preferably esophageal squamous cell carcinoma, gastric cancer, esophageal adenocarcinoma, metastatic urothelial carcinoma, melanoma and non-small cell lung cancer, more preferably esophageal squamous cell carcinoma.

[0025] In some embodiments, the transcriptome data includes single cell transcriptome data and / or bulk transcriptome data. In some embodiments, the transcriptome data is full-length transcriptome data.

[0026] A second aspect of the embodiments of the present application provides a tumor microenvironment immunophenotyping model, which is obtained according to the tumor microenvironment immunophenotyping method described in any embodiment of the first aspect.

[0027] In some embodiments, the model includes: an input module, which is configured to input the tumor marker pathway of the sample and the gene expression data in the tumor marker pathway; a typing module, which is configured to determine the tumor microenvironment immunotyping based on the tumor marker pathway and the gene expression data, wherein the tumor microenvironment immunotyping is immune enrichment (IE) type, fibroblast enrichment (FE) type and tumor proliferation (TP) type.

[0028] In some embodiments, the tumor marker pathway is HALLMARK_ALLOGRAFT_REJECTION, HALLMARK_INTERFERON_ALPHA_RESPONSE, HALLMARK_INTERFERON_GAMMA_RESPONSE), HALLMARK_MTORC1_SIGNALING, HALLMARK_DNA_REPAIR, HALLMARK_E2F_TARGET, HALLMARK_MYC_TARGETS_V1, HALLMARK_HYPOXIA, HALLMARK_KRAS_SIGNALING_UP, HALLMARK_ANGIOGENESIS, HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, HALLMARK_MYOGENESIS, HALLMARK_TGF_BETA_SIGNALING pathway.

[0029] In some embodiments, the gene expression data is the gene expression levels in the intersection of genes that exist simultaneously in the 13 tumor marker pathways.

[0030] The third aspect of the embodiments of the present application provides the use of the tumor microenvironment immunophenotyping model described in any embodiment of the second aspect in the prognosis and / or efficacy evaluation of immunotherapy for pan-solid tumors.

[0031] A fourth aspect of an embodiment of the present application provides an electronic device, characterized in that the electronic device comprises: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the tumor microenvironment immunophenotyping method as described in any embodiment of the first aspect is implemented.

[0032] The fifth aspect of the embodiments of the present application provides a computer-readable storage medium, characterized in that computer program instructions are stored on the computer-readable storage medium, and when the computer program instructions are executed by a processor, the tumor microenvironment immunophenotyping method described in any embodiment of the first aspect is implemented.

[0033] A sixth aspect of the embodiments of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the tumor microenvironment immunophenotyping method according to any embodiment of the first aspect.

[0034] A seventh aspect of the embodiments of the present application provides a computer program product, wherein the computer program includes computer program code, and when the computer program code is run on a computer, the computer executes the tumor microenvironment immunophenotyping method according to any embodiment of the first aspect.

[0035] In an eighth aspect, the present application provides a tumor microenvironment immunophenotyping device, the device comprising:

[0036] A pathway acquisition module, wherein the pathway acquisition module is used to obtain a tumor marker pathway based on the clinical phenotype, transcriptome data and tumor microenvironment characteristics of the sample;

[0037] a calculation module, the calculation module being used to divide the tumor marker pathway into one or more tumor microenvironment modules and calculate the tumor microenvironment module score of the sample; and

[0038] A determination module is used to determine the tumor microenvironment immunophenotyping of the sample based on the tumor microenvironment module score.

[0039] The technical solution of this application has the following technical effects:

[0040] (1) The tumor microenvironment typing method and model of the present application embodiment can be effectively applied to a variety of cancers such as esophageal squamous cell carcinoma, gastric cancer, esophageal adenocarcinoma, metastatic urothelial carcinoma, melanoma and non-small cell lung cancer, and has a wide range of applications and universality;

[0041] (2) The tumor microenvironment typing method and model process of the embodiment of the present application are simple and highly repeatable, and can stably provide supportive reference for clinical cancer immunotherapy;

[0042] (3) The tumor microenvironment typing model of the embodiment of the present application can be used for the prognosis and / or efficacy evaluation of immunotherapy of pan-solid tumors by replacing whole transcriptome sequencing with a small gene set, thereby reducing sequencing costs and accelerating data and result output. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the process of the tumor microenvironment immunophenotyping method of the embodiment of the present application.

[0044] Figure 2 It is a schematic diagram of the structure of the tumor microenvironment immunophenotyping model of the embodiment of the present application.

[0045] Figure 3 Schematic diagram of the structure of the tumor microenvironment immunophenotyping device according to an embodiment of the present application.

[0046] Figure 4 It is a schematic diagram of the decision tree flow of the tumor microenvironment immunophenotyping method of the embodiment of the present application.

[0047] Figure 5 It is a schematic diagram of the structure of the tumor microenvironment immunophenotyping electronic device according to an embodiment of the present application.

[0048] Figure 6 It is a schematic diagram of the annotation results of tumor microenvironment characteristics based on the tumor microenvironment immunophenotyping model of the embodiment of the present application.

[0049] Figure 7 This is a schematic diagram of the proportions of IE and non-IE typing in the response group and the non-response group based on the embodiment of the present application.

[0050] Figure 8 It is a schematic diagram of the survival analysis results of IE and non-IE typing in Asian and white populations based on the embodiments of the present application.

[0051] Fig. 9 This is a schematic diagram of the proportion of IE typing in the response group and the non-response group in the pan-cancer dataset based on the embodiment of the present application.

[0052] Fig.10 It is a schematic diagram of the validation results of the tumor immune microenvironment typing model in esophageal squamous cell carcinoma based on the embodiment of the present application.

[0053] Figure 11 is a schematic diagram of the verification results of the tumor immune microenvironment typing model based on the embodiment of the present application in metastatic urothelial carcinoma, Figure 11(a) is the verification result in an external metastatic urothelial carcinoma dataset; Figure 11(b) is the verification result in another external metastatic urothelial carcinoma dataset.

[0054] Figure 12 is a schematic diagram of the verification results of the tumor immune microenvironment typing model in melanoma based on the embodiment of the present application, Figure 12(a) is the verification result in an external melanoma dataset; Figure 12(b) is the verification result in another external melanoma dataset.

[0055] Figure 13 is a schematic diagram of the verification results of the tumor immune microenvironment typing model in non-small cell lung cancer based on the embodiment of the present application, Figure 13 (a) is the verification result in the sample of non-small cell lung cancer combined with anti-PDL1 treatment; Figure 13 (b) is the verification result in the sample of non-small cell lung cancer combined with anti-PD1 treatment.

[0056] Fig.14 It is a schematic diagram of the validation results of the tumor immune microenvironment typing model in gastric cancer based on the embodiment of the present application.

[0057] Fig.15 It is a schematic diagram of the validation results of the tumor immune microenvironment typing model in esophageal adenocarcinoma based on the embodiment of the present application. DETAILED DESCRIPTION

[0058] Embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be understood as limiting the present invention.

[0059] Cancer Immunotherapy

[0060] Tumor immunotherapy refers to activating the human immune system and relying on its own immune function to kill cancer cells and tumor tissues. Currently, immunotherapy methods include the use of monoclonal antibody immune checkpoint inhibitors, therapeutic antibodies, cancer vaccines, cell therapy and small molecule inhibitors, etc., to improve the immune system's effective recognition and killing of tumor cells to achieve the purpose of treatment.

[0061] Tumor Microenvironment

[0062] Tumor microenvironment (TME) refers to the surrounding microenvironment of tumor cells, including blood vessels, immune cells, fibroblasts, bone marrow-derived inflammatory cells, various signaling molecules, and extracellular matrix around the cells. The tumor microenvironment is a complex environment in which tumor cells survive and develop. The immune cells and their regulatory methods play an important role in the occurrence and development of tumors. In tumor immunotherapy, the interaction between the tumor microenvironment and tumor cells can affect the diagnosis, survival outcome, and clinical treatment effect of tumors.

[0063] Tumor immune infiltration

[0064] The phenomenon of other types of cells besides tumor cells entering the tumor tissue is called infiltration. Among them, the infiltration of immune cells into the tumor is called tumor immune infiltration, and the complexity of the tumor microenvironment is mainly reflected in the immune infiltration. Specifically, according to the immune infiltration, that is, the abundance of immune cells in the tumor microenvironment and their spatial positioning relative to the tumor and the matrix, the tumor can be divided into three immune types, namely immune inflammation type, immune desert type or immune rejection type. The above types represent the infiltration of immune cells around the tumor; the immune cells are far away from the tumor cells and there are basically no immune cells in the microenvironment; the tumor microenvironment excludes three types of tumor microenvironments of tumor infiltrating lymphocytes.

[0065] Immunophenotyping model

[0066] In order to analyze the characteristics of patients' tumor immune microenvironment and design targeted immune personalized treatment plans, relevant studies have proposed to construct an immune typing model based on clinical data and genomic / transcriptomic data to characterize the immune tumor microenvironment (TME).

[0067] In the related technology, the TCGA typing model is based on the pan-cancer immune genome analysis of TCGA data to determine six immune subtypes, namely (C1) wound healing type, (C2) IFN-g type, (C3) inflammatory type, (C4) lymphocyte exhaustion type, (C5) immune quiescence type and (C6) TGF-b type. However, the TCGA typing model requires comprehensive sample inclusion information, and multi-dimensional information processing takes a long time. Due to limited conditions in clinical practice, this method cannot meet the actual clinical needs in terms of sampling and speed. In addition, the TCGA typing model has not yet been verified in the immunotherapy cohort, and its reliability needs to be verified.

[0068] In the related art, the Fges29 classification model is an immune classification based on the functional gene expression signatures (Fges) of each component of the TME (such as the main components of the tumor, immune cells, stromal cells and other cell populations) in the published literature, namely immune-enriched and fibrotic type; immune-enriched and non-fibrotic type; fibrotic type; immune-exhausted type. However, the source data of this classification model is not sufficient. In the validation of the immunotherapy cohort, it is only effective for melanoma, but not effective in other cancer cohorts, and cannot be used as a pan-cancer immune classification model.

[0069] In the related art, the Pan-cancer T typing model is an eight-group typing determined based on the single-cell pan-cancer T cell atlas. Specifically, C1 and C2 have high frequencies of terminal Tex cells, and tumors from C3 to C8 have low frequencies of Tex and high rates of CD8+ZNF683+CXCR6+Trm cells. However, the Pan-cancer T typing model has poor reproducibility and only extracts a single-dimensional feature within the microenvironment, namely the "T cell" component, so its utility is limited.

[0070] To this end, embodiments of the present invention provide a tumor microenvironment immunophenotyping method, an electronic device, a readable storage medium, a tumor microenvironment immunophenotyping model obtained by the method, and uses thereof.

[0071] The first aspect of the present application embodiment provides a tumor microenvironment immunophenotyping method. In some specific embodiments, the present application collects tumor samples of patients with esophageal squamous cell carcinoma (ESCC) receiving immunotherapy and extracts RNA sequencing to obtain transcriptome data. After conventional pretreatment of the transcriptome data, the samples are divided into response groups and non-response groups according to the tumor residual ratio of 33% as a threshold. Then, the gene expression of the response group and the non-response group is compared by differential gene expression analysis to obtain significantly differentially expressed genes (DEGs). Gene set enrichment analysis is performed according to DEGs, and a specific gene set selects a tumor marker pathway, and a tumor marker pathway significantly related to prognosis is obtained. Functional modules are divided according to the tumor marker pathway and the module score is calculated, and the immunophenotyping of the sample is finally judged based on the decision tree. In the embodiment of the present application, IE immunophenotyping is significantly related to a good tumor prognosis. The method provided in the embodiment of the present application has a wide range of applications, a simple process, and high repeatability, and can stably provide a supportive reference for clinical cancer immunotherapy. It can be understood that according to the tumor microenvironment immunophenotyping method proposed in the examples of the present application and the model based on this method, the tumor microenvironment can be effectively immunophenotyped, and clinical cancer can be immunotherapeutic with reference to the typing results.

[0072] In some embodiments, the method includes: obtaining a tumor marker pathway based on the clinical phenotype, transcriptome data and tumor microenvironment characteristics of the sample; dividing the tumor marker pathway into one or more tumor microenvironment modules and calculating the tumor microenvironment module score of the sample; and determining the tumor microenvironment immunotyping of the sample based on the tumor microenvironment module score.

[0073] In some embodiments, the obtaining of a tumor marker pathway based on the clinical phenotype, transcriptome data, and tumor microenvironment characteristics of the sample comprises: dividing the sample into one or more response groups and one or more non-response groups based on the clinical phenotype of the sample; performing differential expression analysis on the transcriptome data of the samples of the response group and the non-response group to obtain differentially expressed genes; performing pathway enrichment analysis on the differentially expressed genes to obtain an initial tumor marker pathway, wherein the initial tumor marker pathway contains the differentially expressed genes; and screening the initial tumor marker pathway according to the tumor microenvironment characteristics of the sample to obtain the tumor marker pathway.

[0074] In some embodiments, the p-value of the differentially expressed gene is less than 0.05, less than 0.01, or less than 0.001.

[0075] In some embodiments, the p-value is less than 0.05.

[0076] In some embodiments, the clinical phenotype is at least one of tumor residual ratio, RECIST.

[0077] In some embodiments, the clinical phenotype is tumor residual ratio.

[0078] It should be noted that, in some embodiments, the sample is divided into a response group and a non-response group by using the tumor residual ratio greater than 33% as a standard. In some other embodiments, the tumor residual ratio can be multiple levels such as 0-25%, 25%-50%, 50%-75%, 75%-100%, and accordingly, the response group can also be multiple. Those skilled in the art can select the division criteria of clinical phenotypes according to specific practices, and apply them to the tumor microenvironment immunophenotyping method provided in the embodiments of the present application. In some embodiments, RECIST (response evaluation criteria in solid tumors, RECIST) is used as a standard to divide samples into response groups and non-response groups. RECIST is a standard scoring method for evaluating the efficacy of solid tumors based on imaging.

[0079] In some embodiments, the performing pathway enrichment analysis on the differentially expressed genes to obtain an initial tumor marker pathway comprises: performing one or more gene set enrichment analyses on the differentially expressed genes based on a tumor marker gene set to obtain a candidate initial tumor marker pathway; and testing the candidate initial tumor marker pathway to determine the initial tumor marker pathway, wherein the gene set enrichment analysis is a GSEA gene set enrichment analysis, and the tumor marker gene set is a MsigDB-Hallmark gene set.

[0080] In some embodiments, the q value of the initial tumor marker pathway is less than 0.05, less than 0.01, or less than 0.001.

[0081] In some embodiments, the q value is preferably less than 0.05.

[0082] In some embodiments, screening the initial tumor marker pathway according to the tumor microenvironment characteristics of the sample to obtain the tumor marker pathway includes screening the initial tumor marker pathway associated with the occurrence and development of solid tumors to obtain the tumor marker pathway.

[0083] In some embodiments, dividing the tumor marker pathway into one or more tumor microenvironment modules and calculating the tumor microenvironment module score of the sample specifically includes: dividing the tumor marker pathway into one or more tumor microenvironment modules; and calculating the mean score of the tumor marker pathway in the one or more tumor microenvironment modules, and using the mean score as the tumor microenvironment module score, wherein the tumor microenvironment module includes an immune enrichment (IE) module, a tumor proliferation (TP) and / or a fibroblast enrichment (FE) module, and the mean score is ssGSEAscore.

[0084] In some embodiments, said dividing said tumor marker pathway into one or more tumor microenvironment modules comprises:

[0085] HALLMARK_ALLOGRAFT_REJECTION, HALLMARK_INTERFERON_ALPHA_RESPONSE, and HALLMARK_INTERFERON_GAMMA_RESPONSE are classified into the immune enrichment (IE) module.

[0086] HALLMARK_MTORC1_SIGNALING, HALLMARK_DNA_REPAIR, HALLMARK_E2F_TARGET, HALLMARK_MYC_TARGETS_V1, HALLMARK_HYPOXIA were classified into the tumor proliferation (TP) module

[0087] The HALLMARK_KRAS_SIGNALING_UP, HALLMARK_ANGIOGENESIS, HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, HALLMARK_MYOGENESIS, HALLMARK_TGF_BETA_SIGNALING pathways were grouped into the fibroblast enrichment (FE) module.

[0088] In some embodiments, the tumor microenvironment immune subtype includes immune-enriched type (IE), tumor proliferation type (TP) and / or fibroblast-enriched type (FE), and determining the tumor microenvironment immune subtype of the sample based on the tumor microenvironment module score includes: determining the tumor microenvironment immune subtype of the sample through a decision tree model based on the tumor microenvironment module score.

[0089] In some embodiments, the decision tree model is Figure 4As shown, including: based on the immune enrichment (IE) module score being greater than 0 and greater than 3 times the fibroblast enrichment (FE) score, determining that the tumor microenvironment immunophenotyping of the sample is immune enrichment (IE) type;

[0090] Based on the immune enrichment (IE) module score being greater than 0 and the tumor proliferation (TP) module score being less than 0, determining that the tumor microenvironment immunophenotyping of the sample is fibroblast-enriched (FE) type;

[0091] Based on the immune enrichment (IE) module score being less than 0 and the fibroblast enrichment (FE) module score being greater than 0, determining that the tumor microenvironment immunophenotyping of the sample is fibroblast enrichment (FE) type;

[0092] Based on the immune enrichment (IE) module score being less than 0 and the fibroblast enrichment (FE) module score being less than 0, the tumor microenvironment immunophenotyping of the sample is determined to be a tumor proliferation (TP) type.

[0093] In some embodiments, the tumor is a pan-solid tumor, preferably one or more of bile cancer, multiforme glioma, lung squamous cell carcinoma, lung adenocarcinoma, liver cancer, testicular cancer, cervical squamous cell carcinoma, bone marrow cancer, thyroid cancer, colon cancer, lymphoma, ovarian cancer, brain low-grade glioma, bladder cancer, skin cancer, prostate cancer, papillary renal cell carcinoma, breast cancer, soft tissue cancer, adrenal cancer, renal clear cell carcinoma, renal chromophobe cell carcinoma, esophageal cancer, head and neck squamous cell carcinoma, gastric cancer, adenoid cystic carcinoma, pleural cancer, thymic cancer, eye cancer, pancreatic cancer, rectal cancer, uterine cancer or endometrial cancer, preferably esophageal squamous cell carcinoma, gastric cancer, esophageal adenocarcinoma, metastatic urothelial carcinoma, melanoma and non-small cell lung cancer, more preferably esophageal squamous cell carcinoma.

[0094] In some embodiments, the transcriptome data includes single cell transcriptome data and / or bulk transcriptome data.

[0095] In some embodiments, the transcriptome data is full-length transcriptome data.

[0096] A second aspect of the embodiments of the present application provides a tumor microenvironment immunophenotyping model, which is obtained according to the tumor microenvironment immunophenotyping method described in any embodiment of the first aspect.

[0097] In some embodiments, the model includes: an input module, which is configured to input the tumor marker pathway of the sample and the gene expression data in the tumor marker pathway; a typing module, which is configured to determine the tumor microenvironment immunotyping based on the tumor marker pathway and the gene expression data, wherein the tumor microenvironment immunotyping is immune enrichment (IE) type, fibroblast enrichment (FE) type and tumor proliferation (TP) type.

[0098] In some embodiments, the tumor marker pathway is HALLMARK_ALLOGRAFT_REJECTION, HALLMARK_INTERFERON_ALPHA_RESPONSE, HALLMARK_INTERFERON_GAMMA_RESPONSE, HALLMARK_MTORC1_SIGNALING, HALLMARK_DNA_REPAIR, HALLMARK_E2F_TARGET, HALLMARK_MYC_TARGETS_V1, HALLMARK_HYPOXIA, HALLMARK_KRAS_SIGNALING_UP, HALLMARK_ANGIOGENESIS, HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, HALLMARK_MYOGENESIS, HALLMARK_TGF_BETA_SIGNALING pathway.

[0099] In some embodiments, the gene expression data is the gene expression levels in the intersection of genes that exist simultaneously in the 13 tumor marker pathways.

[0100] The third aspect of the embodiments of the present application provides the use of the tumor microenvironment immunophenotyping model described in any embodiment of the second aspect in the prognosis and / or efficacy evaluation of immunotherapy for pan-solid tumors.

[0101] A fourth aspect of an embodiment of the present application provides an electronic device, characterized in that the electronic device comprises: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the tumor microenvironment immunophenotyping method as described in any embodiment of the first aspect is implemented.

[0102] The fifth aspect of the embodiments of the present application provides a computer-readable storage medium, characterized in that computer program instructions are stored on the computer-readable storage medium, and when the computer program instructions are executed by a processor, the tumor microenvironment immunophenotyping method described in any embodiment of the first aspect is implemented.

[0103] A sixth aspect of the embodiments of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the tumor microenvironment immunophenotyping method according to any embodiment of the first aspect.

[0104] A seventh aspect of the embodiments of the present application provides a computer program product, wherein the computer program includes computer program code, and when the computer program code is run on a computer, the computer executes the tumor microenvironment immunophenotyping method according to any embodiment of the first aspect.

[0105] In an eighth aspect, the present application provides a tumor microenvironment immunophenotyping device, the device comprising:

[0106] A pathway acquisition module, wherein the pathway acquisition module is used to obtain a tumor marker pathway based on the clinical phenotype, transcriptome data and tumor microenvironment characteristics of the sample;

[0107] a calculation module, the calculation module being used to divide the tumor marker pathway into one or more tumor microenvironment modules and calculate the tumor microenvironment module score of the sample; and

[0108] A determination module is used to determine the tumor microenvironment immunophenotyping of the sample based on the tumor microenvironment module score.

[0109] Example 1

[0110] This example collects transcriptome data of samples of esophageal squamous cell carcinoma (ESCC) patients receiving immunotherapy, divides functional modules and calculates module scores through differential gene expression analysis and tumor marker pathway enrichment analysis, and finally determines the immunophenotyping of the samples. The patients in this example are specifically 30 patients with locally advanced resectable ESCC, who received Adebrezumab (intravenously injected once every three weeks at a dose of 20 mg per kilogram of body weight) on day 1 of a 21-day cycle, and finally received two injections before surgery.

[0111] 1.1 Acquisition of transcriptome data

[0112] (1) Information on patients with esophageal squamous cell carcinoma (ESCC) was collected. The patients met the following criteria: at least 18 years old; had locally advanced resectable ESCC, such as histologically confirmed ESCC according to the Response Evaluation Criteria in Solid Tumors (RECIST) and an Eastern Coast Collaborative Cancer Clinical Research Measurement Scale (ECOGPS) score of 0 or 1; had at least one measurable / evaluable lesion; had adequate hematological, liver, kidney, and coagulation function; and had not received prior treatment for PD-1 or PD-L1, active brain or leptomeningeal metastasis, active autoimmune disease, or other active malignancies within 2 years before randomization.

[0113] (2) Tumor samples were collected from the above patients within 30 minutes after biopsy or surgery, and areas showing obvious necrosis, fibrosis, or hemorrhage were removed.

[0114] (3) Total RNA from the above tumor samples was extracted using the RNeasy FFPE kit (Cat#73504, QIAGEN).

[0115] (4) The concentration and integrity of the extracted RNA were assessed using the Qubit RNA HS Kit (Thermo Fisher Scientific, catalog number Q32852) and the Agilent RNA 6000pico Kit (Agilent, catalog number 5067-1513).

[0116] (4) Based on the qualified RNA quality, the RNA sample library was prepared using the MGI rRNA removal kit (Cat#100005953, MGI) and the MGIEasyRNA library preparation kit (Cat#1000006383, MGI).

[0117] (5) The quality of RNA sample library was assessed using the Agilent DNA 1000 kit (Cat#5067-1504, Agilent).

[0118] (6) Based on the qualified quality of the RNA sample library, the above library was sequenced on an MGISEQ2000 sequencer (MGI, China).

[0119] (7) Filtering the sequencing data, wherein the preprocessing includes removing adapters and low-quality sequences.

[0120] (8) The filtered sequences were aligned with the GRCh37 reference genome using STAR v2.5.1 software, and repetitive sequences were filtered using Picard Tools.

[0121] (9) The filtered aligned sequence data are normalized using RSEM.

[0122] Finally, the FPKM data of the transcriptome that can be used for differential gene expression analysis is obtained to complete the acquisition of transcriptome data.

[0123] 1.2 Construction of classification model

[0124] (1) At least 21 days after drug administration, patients were divided into a response group and a non-response group according to pathological criteria (tumor residual ratio 33%).

[0125] (2) Deseq2 v1.30.1 was used to perform differential gene expression analysis on the transcriptome data of the response group and the non-response group. The criteria for significant differentially expressed genes (DEGs) were Log2 (Fold Change) > 1 and the corrected P value < 0.05.

[0126] (3) ClusterProfiler v3.18.1 was used to perform gene set enrichment analysis (GSEA) on the above DEGs, and the tumor marker (Hallmark) gene set in the molecular signature database (MsigDB v7.4) was selected.

[0127] (4) After Kolmogorov–Smirnov correction of the tumor marker pathways enriched by the above DEGs, the pathways with q values ​​< 0.05 were taken as the initial tumor marker pathways.

[0128] (5) The initial pathways were further screened based on the characteristics of the tumor microenvironment and 13 tumor marker pathways were obtained.

[0129] (6) Use the single-sample GSEA (ssGSEA) method to calculate the ssGSEA score for each pathway.

[0130] (7) According to XX, the above pathways were divided into three modules: immune-enriched (IE), tumor proliferation (TP), and fibroblast-enriched (FE), including three IE pathways (HALLMARK_ALLOGRAFT_REJECTION, HALLMARK_INTERFERON_ALPHA_RESPON SE, and HALLMARK_INTERFERON_GAMMA_RESPONSE), four TP pathways (HALLMARK_MTORC1_SIGNALING, HALLMARK_DNA_REPAIR, HALLMARK_E2F_TA RGETS, and HALLMARK_MYC_TARGETS_V1), and six FE pathways (HALLMARK_HYPOXIA, HALLMARK_KRAS_SIGNALING_UP, HALLMARK_ANGIOGENESIS, HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, HALLMARK_MYOGENESITICS_V2). ENESIS,HALLMARK_TGF_BETA_SIGNALING).

[0131] (8) The mean of the ssGSEA score of the pathway within the module was used as the tumor microenvironment module score, namely the immune enrichment module score (IE score), tumor proliferation module score (TP score), and fibroblast enrichment module score (FEscore).

[0132] (9) Determine the tumor microenvironment classification of the sample according to the tumor microenvironment module score. The specific criteria are as follows: Figure 4 Shown are:

[0133] Based on the immune enrichment module score being greater than 0 and greater than 3 times the fibroblast enrichment score, determining that the tumor microenvironment immunophenotyping of the sample is immune enriched (IE) type;

[0134] Based on the immune enrichment module score being greater than 0 and the tumor proliferation module score being less than 0, the tumor microenvironment immunophenotyping of the sample is determined to be fibroblast-enriched;

[0135] Based on the immune enrichment module score being less than 0 and the fibroblast enrichment module score being greater than 0, determining that the tumor microenvironment immunophenotyping of the sample is fibroblast-enriched; and

[0136] Based on the fact that the immune enrichment module score is less than 0 and the fibroblast enrichment (FE) module score is less than 0, the tumor microenvironment immunophenotyping of the sample is determined to be a tumor proliferation type.

[0137] This example completes the construction of the typing model, and the results are as follows Figure 5 and Figure 6 shown.

[0138] Figure 5 This is a schematic diagram of the structure of the tumor microenvironment immunophenotyping model of this embodiment. By inputting a small gene set, i.e., gene expression data in the tumor marker pathway, into the tumor microenvironment immunophenotyping model of this application, the tumor microenvironment immunophenotyping can be completed instead of whole transcriptome sequencing, and used for immunotherapy prognosis and / or efficacy evaluation of pan-solid tumors, which has the excellent technical effect of reducing sequencing costs and accelerating data and result output.

[0139] Figure 6It is a schematic diagram of the annotated results of the tumor microenvironment characteristics based on the tumor microenvironment immunotyping model of the embodiment of the present application. The figure shows the tumor immune microenvironment typing of patients with esophageal squamous cell carcinoma obtained by the tumor microenvironment immunotyping model of the embodiment of the present application and the clinical characteristics such as PD-L1 expression, CAF score, IFN / EMT score, immune cell and stromal cell composition of each patient, and enrichment characteristics in pre-treated tumor samples. It can be seen that in the response group, IE typing accounts for a high proportion, and the corresponding cell composition is mainly immune cells, and the expression of the enriched pathways also decreases significantly. The figure shows that the IE typing in the tumor microenvironment immunotyping of the embodiment of the present application is significantly correlated with a good prognosis of the tumor, and can be used to provide a supportive reference for tumor immunotherapy prognosis and / or efficacy evaluation.

[0140] Example 2

[0141] This example uses the TCGA-ESCC cohort and the external immunotherapy cohort as a pan-cancer data set to verify the effect of the tumor microenvironment immunophenotyping model.

[0142] (1) The transcriptome data of the TCGA-ESCC cohort and the external immunotherapy cohort were collected. The data sources of the external immunotherapy cohort are shown in Table 1.

[0143] Table 1

[0144]

[0145] (2) The above cohort was divided into a response group and a non-response group according to the same pathological criteria (tumor residual ratio 33%).

[0146] (3) Determine the tumor microenvironment immune typing of each sample in the above cohort, the specific steps are the same as Example 1.2, that is, typing according to the 13 tumor marker pathways.

[0147] (4) Survival analysis was performed on the IE and non-IE type cohorts separately.

[0148] Figure 7 This is a schematic diagram of the proportion of IE and non-IE typing in the response group and the non-response group based on this example. The results are consistent with those in Example 1, and the proportion of IE typing in the response group is relatively high.

[0149] Figure 8 This is a schematic diagram of the survival analysis results of IE and non-IE typing in Asian and white populations based on this embodiment. The results show that among cancer patients receiving immunotherapy, whether Asian or white population, the IE typing accounted for a higher proportion in the immune response group (P<0.00057), and had better survival results than other typings.

[0150] Fig.10 It is a schematic diagram of the validation results of the tumor immune microenvironment typing model in esophageal squamous cell carcinoma based on this example.

[0151] Figure 11 is a schematic diagram of the verification results of the tumor immune microenvironment typing model based on the embodiment of the present application in metastatic urothelial carcinoma, Figure 11(a) is the verification result in an external metastatic urothelial carcinoma dataset; Figure 11(b) is the verification result in another external metastatic urothelial carcinoma dataset.

[0152] Figure 12 is a schematic diagram of the verification results of the tumor immune microenvironment typing model in melanoma based on the embodiment of the present application, Figure 12(a) is the verification result in an external melanoma dataset; Figure 12(b) is the verification result in another external melanoma dataset.

[0153] Figure 13 is a schematic diagram of the verification results of the tumor immune microenvironment typing model in non-small cell lung cancer based on the embodiment of the present application, Figure 13 (a) is the verification result in the sample of non-small cell lung cancer combined with anti-PDL1 treatment; Figure 13 (b) is the verification result in the sample of non-small cell lung cancer combined with anti-PD1 treatment.

[0154] Fig.14 It is a schematic diagram of the validation results of the tumor immune microenvironment typing model in gastric cancer based on the embodiment of the present application.

[0155] Fig.15 It is a schematic diagram of the validation results of the tumor immune microenvironment typing model in esophageal adenocarcinoma based on the embodiment of the present application.

[0156] Figure 10-15 The results show that among all the cancer data set verification results mentioned above, the IE typing accounts for a high proportion in the immune response group and has better survival results than the non-IE typing. This result shows that the tumor microenvironment typing method and its model of the embodiment of the present application can be effectively applied to a variety of cancers such as esophageal squamous cell carcinoma, gastric cancer, esophageal adenocarcinoma, metastatic urothelial carcinoma, melanoma, and non-small cell lung cancer. It has a wide range of applications and universality. It can be used for the prognosis and / or efficacy evaluation of immunotherapy of pan-solid tumors, and stably provides supportive references for clinical cancer immunotherapy.

[0157] In the description of the present invention, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically limited. In the present invention, the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples without contradiction.

[0158] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for immunophenotyping of tumor microenvironment, characterized in that: include: Obtain tumor marker pathways based on the clinical phenotype, transcriptome data, and tumor microenvironment characteristics of the samples; Dividing the tumor marker pathway into one or more tumor microenvironment modules and calculating a tumor microenvironment module score for the sample; and The tumor microenvironment immunophenotyping of the sample is determined based on the tumor microenvironment module score.

2. The tumor microenvironment immunophenotyping method according to claim 1, characterized in that: The tumor marker pathways obtained based on the clinical phenotype, transcriptome data and tumor microenvironment characteristics of the sample include: Based on the clinical phenotype of the sample, the sample is divided into one or more response groups and one or more non-response groups; Performing differential expression analysis on the transcriptome data of the samples of the response group and the non-response group to obtain differentially expressed genes, optionally, the p-value of the differentially expressed genes is less than 0.05, less than 0.01 or less than 0.001, preferably less than 0.05; performing pathway enrichment analysis on the differentially expressed genes to obtain an initial tumor marker pathway, wherein the initial tumor marker pathway contains the differentially expressed genes; and The initial tumor marker pathway is screened according to the tumor microenvironment characteristics of the sample to obtain the tumor marker pathway.

3. The tumor microenvironment immunophenotyping method according to claim 2, characterized in that: The step of performing pathway enrichment analysis on the differentially expressed genes to obtain an initial tumor marker pathway comprises: Based on the tumor marker gene set, perform one or more gene set enrichment analyses on the differentially expressed genes to obtain candidate initial tumor marker pathways; and The candidate initial tumor marker pathway is tested to determine the initial tumor marker pathway, optionally, the q value of the initial tumor marker pathway is less than 0.05, less than 0.01 or less than 0.001, preferably less than 0.05, The gene set enrichment analysis is GSEA gene set enrichment analysis. The tumor marker gene set is the MsigDB-Hallmark gene set.

4. The tumor microenvironment immunophenotyping method according to claim 2, characterized in that: The screening of the initial tumor marker pathway according to the tumor microenvironment characteristics of the sample to obtain the tumor marker pathway comprises: The initial tumor marker pathway related to the occurrence and development of solid tumors is screened to obtain the tumor marker pathway.

5. The tumor microenvironment immunophenotyping method according to claim 1, characterized in that: The dividing the tumor marker pathway into one or more tumor microenvironment modules and calculating the tumor microenvironment module score of the sample specifically includes: Dividing the tumor marker pathway into one or more tumor microenvironment modules; and Calculating the mean score of the tumor marker pathway in the one or more tumor microenvironment modules, and using the mean score as the tumor microenvironment module score, The tumor microenvironment module includes an immune enrichment (IE) module, a tumor proliferation (TP) and / or a fibroblast enrichment (FE) module, The score mean is ssGSEA score.

6. The tumor microenvironment immunophenotyping method according to claim 5, characterized in that: The dividing the tumor marker pathway into one or more tumor microenvironment modules comprises: Classify HALLMARK_ALLOGRAFT_REJECTION, HALLMARK_INTERFERON_ALPHA_RESPONSE, and HALLMARK_INTERFERON_GAMMA_RESPONSE into the immune enrichment (IE) module; HALLMARK_MTORC1_SIGNALING, HALLMARK_DNA_REPAIR, HALLMARK_E2F_TARGET, HALLMARK_MYC_TARGETS_V1, and HALLMARK_HYPOXIA were classified into the tumor proliferation (TP) module; The HALLMARK_KRAS_SIGNALING_UP, HALLMARK_ANGIOGENESIS, HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, HALLMARK_MYOGENESIS, HALLMARK_TGF_BETA_SIGNALING pathways were grouped into the fibroblast enrichment (FE) module.

7. The tumor microenvironment immunophenotyping method according to claim 5, characterized in that: The tumor microenvironment immunophenotyping includes immune enrichment type (IE), tumor proliferation type (TP) and / or fibroblast enrichment type (FE), and the tumor microenvironment immunophenotyping of the sample determined based on the tumor microenvironment module score includes: Based on the tumor microenvironment module score, the tumor microenvironment immunophenotyping of the sample is determined by a decision tree model, Optionally, the decision tree model includes: Determining that the tumor microenvironment immunophenotyping of the sample is immune enrichment (IE) type based on the immune enrichment (IE) module score being greater than 0 and greater than 3 times the fibroblast enrichment (FE) score; Based on the immune enrichment (IE) module score being greater than 0 and the tumor proliferation (TP) module score being less than 0, determining that the tumor microenvironment immunophenotyping of the sample is fibroblast-enriched (FE) type; Based on the immune enrichment (IE) module score being less than 0 and the fibroblast enrichment (FE) module score being greater than 0, determining that the tumor microenvironment immunophenotyping of the sample is fibroblast enrichment (FE) type; Based on the immune enrichment (IE) module score being less than 0 and the fibroblast enrichment (FE) module score being less than 0, the tumor microenvironment immunophenotyping of the sample is determined to be a tumor proliferation (TP) type.

8. The tumor microenvironment immunophenotyping method according to claim 1, characterized in that: The tumor is a pan-solid tumor, preferably one or more of bile cancer, multiforme glioma, lung squamous cell carcinoma, lung adenocarcinoma, liver cancer, testicular cancer, cervical squamous cell carcinoma, bone marrow cancer, thyroid cancer, colon cancer, lymphoma, ovarian cancer, brain low-grade glioma, bladder cancer, skin cancer, prostate cancer, papillary renal cell carcinoma, breast cancer, soft tissue cancer, adrenal cancer, renal clear cell carcinoma, renal chromophobe cell carcinoma, esophageal cancer, head and neck squamous cell carcinoma, gastric cancer, adenoid cystic carcinoma, pleural cancer, thymic cancer, eye cancer, pancreatic cancer, rectal cancer, uterine cancer or endometrial cancer, preferably esophageal squamous cell carcinoma, gastric cancer, esophageal adenocarcinoma, metastatic urothelial carcinoma, melanoma and non-small cell lung cancer, more preferably esophageal squamous cell carcinoma.

9. A tumor microenvironment immunophenotyping model, characterized in that: Obtained according to the tumor microenvironment immunophenotyping method according to any one of claims 1 to 8.

10. The tumor microenvironment immunophenotyping model according to claim 9, characterized in that: The model includes: An input module, the input module being configured to input a tumor marker pathway of a sample and gene expression data in the tumor marker pathway; a typing module, wherein the typing module is configured to determine the tumor microenvironment immunotyping according to the tumor marker pathway and the gene expression data, The tumor microenvironment immunophenotyping is divided into immune enriched (IE), fibroblast enriched (FE) and tumor proliferation (TP) types. Preferably, the tumor marker pathway is HALLMARK_ALLOGRAFT_REJECTION, HALLMARK_INTERFERON_ALPHA_RESPONSE, HALLMARK_INTERFERON_GAMMA_RESPONSE), HALLMARK_MTORC1_SIGNALING, HALLMARK_DNA_REPAIR, HALLMARK_E2F_TARGET, HALLMARK_MYC_TARGETS_V1, HALLMARK_HYPOXIA, HALLMARK_KRAS_SIGNALING_UP, HALLMARK_ANGIOGENESIS, HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, HALLMARK_MYOGENESIS, HALLMARK_TGF_BETA_SIGNALING pathway, Preferably, the gene expression data is the gene expression levels in the intersection of genes that exist simultaneously in the 13 tumor marker pathways.

11. Use of the tumor microenvironment immunophenotyping model according to claim 9 or 10 in the prognosis and / or efficacy evaluation of immunotherapy for pan-solid tumors.

12. An electronic device, characterized in that: The electronic device comprises: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the tumor microenvironment immunophenotyping method according to any one of claims 1 to 8 is implemented.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the tumor microenvironment immunophenotyping method according to any one of claims 1 to 8 is implemented.

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