Immune cycle typing method and system for solid tumors and application of immune cycle typing method and system

Abstract

The invention discloses a solid tumor immune cycle typing method, a solid tumor immune cycle typing system and application. The method comprises the following steps: acquiring tumor tissue DNA and blood cell DNA, and carrying out whole exon sequencing; tumor tissue RNA is obtained for whole transcriptome sequencing; evaluating immune cycle parameters according to the whole transcriptome sequencing data; predicting a tumor neoantigen load marker according to the whole exon sequencing data and the whole transcriptome sequencing data; performing unsupervised clustering on tumor immune cycle parameters and tumor neoantigen load markers by adopting a non-negative matrix factorization algorithm, wherein solid tumor immune cycle phenotypes are divided into tumor immune cycle I type, tumor immune cycle II type, tumor immune cycle III type and tumor immune cycle IV type; the tumor immune cycle type I shows a thermal tumor characteristic, and the tumor immune cycle type II, the tumor immune cycle type III and the tumor immune cycle type IV show cold tumor characteristics; after solid tumor patients of four immune cycle types receive anti-tumor treatment, clinical prognosis shows that the patient with the longest total lifetime is the tumor immune cycle type I, and the patient with the shortest total lifetime is the tumor immune cycle type II.

Description

Technical Field

[0001] The present invention relates to the field of tumor immunity technology, and in particular to a solid tumor immune circulation typing method and a system and application thereof. Background Art

[0002] Immune checkpoint inhibitor (ICI) therapy has brought new treatment concepts and hopes to cancer treatment. However, only some cancer patients achieve durable clinical responses to ICI treatment. In most solid tumors, the clinical response rate to ICI treatment ranges from 15% to 30%. In order to improve the durability and effectiveness of anti-tumor immune response, it is necessary to comprehensively evaluate the state of anti-tumor immune response and subsequently develop optimized, personalized anti-tumor immunotherapy strategies for patients. The tumor immune cycle theory provides a theoretical framework for a deep understanding of the body's anti-tumor immune response. In short, the anti-tumor immune response is portrayed as seven important steps: tumor antigen release, antigen-presenting cells presenting tumor antigens, initiation and activation of effector T cell responses, migration of T cells to tumors, infiltration of T cells into tumors, T cell recognition of cancer cells, and ultimately elimination of tumor cells.

[0003] Recently, the update of the tumor immune cycle theory has emphasized the iterative nature of the anti-tumor immune response to adapt to tumor evolution. Any weak link in the tumor immune cycle may become a rate-limiting bottleneck, causing the tumor to evade the attack of the immune system. At the same time, the update of the tumor immune cycle theory emphasizes the importance of tumor immune phenotype. Even tumors of the same type can be distinguished by different immune phenotypes. At the same time, it is important that solid tumors of different tissue origins may present several major immune phenotypes. Previous studies have proposed different tumor immune subtype classifications. However, there is still a lack of effective evaluation of the tumor immune cycle phenotype for solid tumors. Summary of the invention

[0004] The main purpose of the present invention is to provide a solid tumor immune circulation typing method and its system and application, so as to solve the problem of lack of effective solid tumor tumor immune circulation phenotype evaluation technology in the prior art.

[0005] According to an embodiment of the present invention, a solid tumor immune circulation typing method is proposed, which includes: obtaining tumor tissue DNA and blood cell DNA for whole exome sequencing to obtain whole exome sequencing data; obtaining tumor tissue RNA for whole transcriptome sequencing to obtain whole transcriptome sequencing data; evaluating immune circulation parameters according to the whole transcriptome sequencing data, the immune circulation parameters including: T cell immune markers, sensitization and activation markers, chemotaxis and infiltration markers, tumor cell recognition markers, inhibitory cell markers, immune checkpoint molecule expression markers and inhibitory molecule expression markers; predicting tumor neoantigen load markers according to the whole exome sequencing data and the whole transcriptome sequencing data; The non-negative matrix factorization algorithm was used to perform unsupervised clustering of tumor immune cycle parameters and tumor neoantigen load markers, and the solid tumor immune cycle phenotypes were divided into: tumor immune cycle type I, tumor immune cycle type II, tumor immune cycle type III and tumor immune cycle type IV according to the clustering results; wherein, the tumor immune cycle type I showed hot tumor characteristics, and the tumor immune cycle type II, tumor immune cycle type III and tumor immune cycle type IV showed cold tumor characteristics; wherein, the clinical prognosis of solid tumor patients with the four immune cycle types after receiving anti-tumor treatment was that the tumor immune cycle type I had the longest overall survival, and the tumor immune cycle type II had the shortest overall survival.

[0006] Specific implementations may include one or more of the following.

[0007] The solid tumor of the tumor immune cycle type I shows that the expression levels of the first tumor immune activation marker and the immune exhaustion marker are higher than those of the other three types of circulating phenotypes. The tumor immune cycle type I presents the characteristics of an exhausted hot tumor, wherein the first tumor immune activation marker includes: T cell immune markers, sensitization and activation markers, chemotaxis and infiltration markers; the immune exhaustion markers include: immune checkpoint molecule expression markers.

[0008] The solid tumor of tumor immune circulation type II is characterized by higher expression levels of tumor immune suppression markers than the other three types of circulation phenotypes, lower expression levels of tumor immune activation markers than the solid tumor of tumor immune circulation type I, and higher expression levels of tumor evolution rate markers than the other three types of circulation phenotypes. The tumor immune circulation type II presents aggressive cold tumor characteristics, wherein the first tumor immune suppression marker includes: inhibitory molecule expression markers; the second tumor immune activation marker includes: T cell immune markers, sensitization and activation markers, chemotaxis and infiltration markers, and tumor cell recognition markers; the tumor evolution rate markers include: tumor clone positive selection level markers.

[0009] The solid tumor of tumor immune circulation type III is characterized by a higher expression level of tumor cell recognition markers than the other three types of circulating phenotypes, and a lower expression level of the first tumor immune activation marker than that of immune circulation type I solid tumors. The tumor immune circulation type III presents identifiable cold tumor characteristics, wherein the first tumor immune activation marker includes: T cell immune markers, sensitization and activation markers, and chemotaxis and infiltration markers.

[0010] The solid tumor of the tumor immune circulation type IV is characterized by a lower expression level of the second tumor immune activation marker than the other three types of circulating phenotypes, a lower expression level of the tumor evolution rate marker than the other three types of circulating phenotypes, and a higher expression level of the tumor neoantigen load marker than the other three types of circulating phenotypes. The tumor immune circulation type IV presents the characteristics of an inert cold tumor, wherein the second tumor immune activation marker includes: T cell immune markers, sensitization and activation markers, chemotaxis and infiltration markers, and tumor cell recognition markers; the tumor evolution rate markers include: tumor clone positive selection level markers.

[0011] The method further includes: determining the first value at which the common classification correlation coefficient of the NMF algorithm begins to continuously decrease as the number of classifications of the phenotype of the tumor immune cycle. The method further includes: representing the anti-tumor immune cycle by eight-dimensional gene parameters including tumor immune cycle parameters and tumor neoantigen load markers, and visually displaying the eight-dimensional gene parameters. The method further includes: displaying the eight-dimensional gene parameters in a radar chart visualization manner.

[0012] According to an embodiment of the present invention, a solid tumor immune circulation typing system is also proposed, which includes: an acquisition module, used to acquire tumor tissue DNA and blood cell DNA for whole exome sequencing to obtain whole exome sequencing data; acquire tumor tissue RNA for whole transcriptome sequencing to obtain whole transcriptome sequencing data; an immune circulation parameter evaluation module, used to evaluate immune circulation parameters according to the whole transcriptome sequencing data, and the immune circulation parameters include: T cell immune markers, sensitization and activation markers, chemotaxis and infiltration markers, tumor cell recognition markers, inhibitory cell markers, immune checkpoint molecule expression markers and inhibitory molecule expression markers; a tumor neoantigen load marker prediction module, used to evaluate the immune circulation parameters according to the whole exome sequencing data and the whole transcriptome sequencing data, predicting tumor neoantigen load markers; a typing module, used to perform unsupervised clustering of tumor immune circulation parameters and tumor neoantigen load markers using a non-negative matrix decomposition algorithm, and classifying the solid tumor immune circulation phenotypes into: tumor immune circulation type I, tumor immune circulation type II, tumor immune circulation type III and tumor immune circulation type IV according to the clustering results; wherein, the tumor immune circulation type I exhibits hot tumor characteristics, and the tumor immune circulation type II, tumor immune circulation type III and tumor immune circulation type IV exhibit cold tumor characteristics; wherein, the clinical prognosis of solid tumor patients with the four immune circulation typings after receiving anti-tumor treatment is that the tumor immune circulation type I has the longest overall survival, and the tumor immune circulation type II has the shortest overall survival.

[0013] According to an embodiment of the present invention, an application of the above-mentioned solid tumor immune circulation typing method in tumor personalized immune assessment is also proposed.

[0014] According to the technical solution of the present invention, by analyzing the immune circulation characteristics of multiple solid tumors and multiple tumor tissue samples, the non-negative matrix decomposition (NMF) algorithm is used to classify the immune circulation of solid tumors, and on this basis, the immune microenvironment characteristics, tumor clone evolution selection mode, immune editing and other tumor immune and molecular characteristics of the four types of solid tumors are further analyzed in detail, and the characteristics and clinical prognosis characteristics of the solid tumors of the four immune circulation types are comprehensively described. This application shows the four main tumor immune circulation patterns in solid tumors, reveals the main weak links of the four types of solid tumors in the tumor immune circulation, helps to deeply understand the interaction between tumors and the body's immune system, and provides a comprehensive and detailed theoretical support and data basis for the subsequent formulation of targeted solid tumor immunotherapy strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0016] Figure 1A is a flow chart of a method for tumor immune circulation typing of solid tumors according to an embodiment of the present invention;

[0017] Figure 1B is a flow chart of solid tumor tumor characteristics, immune characteristics, and clinical prognosis analysis according to four immune cycle classifications according to an embodiment of the present invention;

[0018] Figure 2 is a schematic diagram showing a radar chart of a solid tumor immune cycle according to an embodiment of the present invention;

[0019] Figure 3 is a common classification correlation coefficient diagram for selecting NMF clustering parameters according to an embodiment of the present invention;

[0020] Figure 4 is an NMF clustering heat map according to an embodiment of the present invention;

[0021] Figure 5 It is the clustering of immune circulation phenotypes of solid tumor patients and their characteristic scores according to an embodiment of the present invention;

[0022] Figure 6 is a radar chart of the four types of immune circulation in patients with solid tumors according to an embodiment of the present invention;

[0023] 7A to 7E 1. Characteristic diagram of solid tumor immune cells according to four immune circulation classifications according to an embodiment of the present invention;

[0024] FIG. 8A to FIG. 8E is an expression diagram of immune effector molecules of solid tumors according to four immune cycle typings according to an embodiment of the present invention;

[0025] Fig. 9 It is a graph showing the expression levels of MHC-I class molecules, MHC-II class molecules, immune co-stimulatory molecules, and co-inhibitory molecules of solid tumors according to four immune circulation typing methods according to an embodiment of the present invention;

[0026] FIG. 10A to FIG. 10D It is a graph showing the expression levels of solid tumor immunosuppressive molecules TGF-β1, TGF-β2, TGF-β, and IL-10 according to four immune cycle typings according to an embodiment of the present invention;

[0027] FIG. 11A to FIG. 11F It is a solid tumor tumor clone selection pattern, genetic tumor heterogeneity (MATH), immune editing ability, and tumor neoantigen load diagram of four immune circulation typings according to an embodiment of the present invention;

[0028] FIG. 12A to FIG. 12C is a calculation iteration graph of tumor-related genes and randomly sampled gene clone selection according to an embodiment of the present invention;

[0029] 13 is a KM survival analysis curve diagram of solid tumor immune circulation phenotype and patient overall survival (OS) according to an embodiment of the present invention;

[0030] Fig.14 This is a summary diagram of the main characteristics of solid tumors according to four immune cycle classifications according to an embodiment of the present invention;

[0031] FIG15 is a KM survival analysis curve diagram of the immune circulation phenotype of metastatic urothelial carcinoma and the overall survival rate after treatment with immune checkpoint inhibitors according to an embodiment of the present invention;

[0032] Fig.16 This is a graph showing the immune circulation typing of metastatic gastric cancer and the percentage of objective response rate to PD-1 inhibitor (pembrolizumab) treatment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The technical solutions provided by various embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0034] This application analyzes the immune circulation characteristics of 32 solid tumors and 8460 tumor tissue samples. The unsupervised clustering method non-negative matrix factorization (NMF) algorithm was used to classify the immune circulation of solid tumors, and on this basis, the immune microenvironment characteristics, tumor clone evolution selection patterns, immune editing and other molecular characteristics of the four types of solid tumors were further analyzed in detail, and the characteristics and clinical prognosis characteristics of the four types of immune circulation types of solid tumors were comprehensively described. This study demonstrated the four main tumor immune circulation patterns in solid tumors, revealing the main weak links in the tumor immune circulation of the four types of solid tumors, which helps to deeply understand the interaction between tumors and the body's immune system, and provides a comprehensive and detailed theoretical support and data basis for the subsequent formulation of targeted solid tumor immunotherapy strategies.

[0035] According to an embodiment of the present invention, a solid tumor immune circulation typing method is provided, referring to Figure 1A , the method comprises at least the following steps:

[0036] Step S102, collecting tumor tissue and blood cell samples from patients with solid tumors;

[0037] Step S104, extracting or isolating tumor tissue RNA;

[0038] Step S106, performing whole transcriptome sequencing on the tumor tissue RNA to obtain whole transcriptome sequencing data;

[0039] Step S108, evaluate the following seven immune cycle parameters (IGS1, IGS3-IGS8) based on the whole transcriptome sequencing data, wherein the seven immune cycle parameters include: T cell immune markers (IGS1), sensitization and activation markers (IGS3), chemotaxis and infiltration markers (IGS4), tumor cell recognition markers (IGS5), inhibitory cell markers (IGS6), immune checkpoint molecule expression markers (IGS7) and inhibitory molecule expression markers (IGS8).

[0040] The T cell immune markers include genes BATF, BTLA, CCL11, CCL17, CCL22, CCL3, CCL5, CCL7, CCR1, CCR3, CCR4, CCR8, CD1A, CD1B, CD1C, CD1D, CD1E, CD247, CD28, CD3D, CD3E, CD3G, CD69, CD8A, CXCR1, CXCR4, CXCR5, CXCR6, FGFBP2, FLT3, FOSL1, GRAP2, HLA-DRA, HLA-DRB1, HLA-DRB5, HRH2, IFNG, IL10, IL12B, IL12RB1, IL12RB2, IL18R 1. IL18RAP, IL1B, IL21, IL21R, IL24, IL26, IL27, IL2RA, IL6, IL7, IRF4, IRF8, ITGAL, ITK, JAK2, JAK3, LAIR1, LAT, LAX1, LCK, LCP2, LILRA3, LILRA4, LILR B1, LILRB2, LILRB3, LILRB4, MS4A4A, NAPSB, NFATC1, NFKB2, P2RY14, PIK3CD, POU2F2, PRF1, RUNX3, SPI1, STAT1, STAT4, TNF, TPRG1, TSLP, UBASH3B and AP70;

[0041] The sensitization and activation markers include genes PLA1A, HESX1, MMP12, TNFAIP6, MSC, CCL8, DHX58, IDO, RSAD2, IFI44L, HTR2B, PTGIR, TREM2, ST3GAL6, CYP27A1, FAM70A, KYNU, RASSF4, IL2RA, SLC2A6, CCL5, CD80, FPR3, SLC15A3, CD86, PDCD1LG2 , CXCL10, CXCL11, TNFRSF4, EBI3, ARHGAP22, PLA2G7, CCL18, LAMP3, SIGLEC1, CCL1, CCL13, NR4A3, CLIC2, BIRC3, CCL20, TNFRSF11A, IL12B, CCL19, CCR7, CD1E, CD1B, CCL17, CCL22, SLCO5A1, ETV3, CHST7, and MAP3K13;

[0042] The chemotaxis and invasion markers include CXCL9, CXCL10, CCL5, CX3CL1, LFA1, ICAM1 and SELE;

[0043] The tumor cell recognition markers include genes HLA-A, HLA-B, HLA-C, B2M, TAP1 and TAP2;

[0044] Inhibitory cell markers include genes CCL19, CD34, CD72, CTLA4, FOXP3, GADD45B, GEM, IL1RL1, IL9R, MADCAM1, MYH10, NCF2, RCSD1, RYR1, SELE, SELP, SFRP, SIT1, TIGIT, TLR10, TLR2, TLR7, TLR8, TRAF1, WIPF1, TGFB1, ADORA3, A G2, ARG1, BIN2, C1orf162, CAPS, CD117, CD11B, CD11C, CD124, CD14, CD15, CD163L1, CD1D1, CD21, CD2 3. CD274, CD31, CD35, CD40, CD43, CD44, CD66B, COX2, CR2, CTR9, EBP, FAM48A, FAM70B, FCER2, FCGRT, FERMT3, FLOT1, FLT1, GIMAP7, GLI4, GNA15, GPR34, GPSM3, HLA-DR, IDO, IKZF1, IL12, IL13, IL18BP, I L1R, IL4R, INPP5D, ITGA3, KDR, KRIT1, LGALS3, MGAT4A, NAIP, NEK3, NFSF13, NOG, PARVG, PDRG1, PECA M1, PIK3R5, PPP1R2P4, PSAP, PTGES2, PTPRE, RNASE1, RP11, S100A8, S100A9, SELPLG, SLA, SLC36A1, S LC44A1, ST8SIA4, STAT3, STAT6, TBXAS1, TGFFB1, TGFFB2, TGFFB3, TGFFB5, TFRC, TGFB2, TPP1 and VTCN1;

[0045] The immune checkpoint molecule expression markers include genes PD-1, BTLA, TIM-3, LAG3, CTLA4, PD-L1 and VISTA;

[0046] The inhibitory molecule expression markers included genes IL10, IDO1, TGFB1, ARG1, INOS, and CTNNB1.

[0047] Step S110, extracting or separating tumor tissue DNA and blood cell DNA;

[0048] Step S112, performing whole exome sequencing on tumor tissue DNA and blood cell DNA to obtain whole exome sequencing data;

[0049] Step S114, predicting a tumor neoantigen load marker (IGS2) according to the whole exome sequencing data and the whole transcriptome sequencing data;

[0050] Among them, using whole exome sequencing data matching tumor tissue DNA and blood cell DNA, the prediction of new antigens can be divided into three steps: identification of mutant proteins after tumor-normal tissue pairing, HLA typing, and prediction of new antigen-MHC binding affinity.

[0051] Step S116, unsupervised clustering of tumor immune cycle parameters and tumor neoantigen load markers is performed according to the non-negative matrix factorization (NMF) algorithm, and the solid tumor immune cycle phenotype is divided into: tumor immune cycle type I, tumor immune cycle type II, tumor immune cycle type III and tumor immune cycle type IV according to the clustering results; the radar chart of the eight dimensions of the solid tumor immune cycle is as follows Figure 2 shown.

[0052] Specifically, the immune characteristics of solid tumors of tumor immune cycle type I show the characteristics of "exhausted hot tumors" and have the best clinical prognosis. Tumor immune cycle types II, III, and IV show "cold tumor" characteristics with different characteristics. Tumor immune cycle type II shows the characteristics of "aggressive cold tumors", with a faster tumor evolution rate, a high level of positive selection during tumor evolution, a high expression level of immunosuppressive molecules, and the worst clinical prognosis. Tumor immune cycle type III shows the characteristics of "identifiable cold tumors", with a high expression level of MHC-I class molecules, and low scores in other dimensions of the immune cycle, thus presenting an immunosuppressive phenotype. Tumor immune cycle IV shows the characteristics of "indolent cold tumors", with overall immunosuppression, a slow tumor evolution rate, a low level of positive selection during tumor evolution, weak immune editing ability, and a high accumulation of tumor neoantigen loads.

[0053] Continue to refer Figure 1A , after or before step S116, the method may further include:

[0054] Step S115, a radar chart is used to visualize the eight dimensions of the immune cycle. The eight dimensional characteristics of the immune cycle include: T cell immunity (IGS1), neoantigen load (IGS2), sensitization and activation (IGS3), chemotaxis and infiltration (IGS4), tumor cell recognition (IGS5), inhibitory cells (IGS6), immune checkpoint molecule expression (IGS7), inhibitory molecule expression (IGS8). It should be noted that Figure 1A In the illustrated embodiment, step S115 is performed before step S116 . In other embodiments, step S115 may also be performed after step S116 .

[0055] The above eight dimensions can be classified from the functional level of tumor immunity as follows: 1. Tumor immune activation markers, including: T cell immunity (IGS1), neoantigen load (IGS2), sensitization and activation (IGS3), chemotaxis and infiltration (IGS4), and tumor cell recognition (IGS5); 2. Tumor immunosuppression markers, including: suppressor cells (IGS6) and inhibitory molecule expression (IGS8); 3. Immune exhaustion markers, including: immune checkpoint molecule expression (IGS7).

[0056] Through the above embodiments, according to the method of solid tumor immune circulation typing and visualization evaluation, it is helpful to phenotype the immune circulation characteristics of the solid tumor immune microenvironment and formulate individualized immunotherapy strategies for patients, which has important theoretical and practical significance for improving the overall efficacy of tumors.

[0057] The embodiments of the present application are described below with reference to examples.

[0058] 1. Collect tumor tissue and matched blood cell samples from patients with solid tumors, separate tumor tissue DNA and RNA, and blood cell DNA, perform whole exome sequencing on tumor tissue and blood cell DNA, and perform transcriptome sequencing on tumor tissue RNA.

[0059] 2. Prediction of new antigens.

[0060] Using whole-exome sequencing data of matched tumor tissue and blood cell DNA, the prediction of neoantigens can be divided into the following three steps: identification of mutant proteins after tumor-normal tissue pairing, HLA typing, and prediction of neoantigen-MHC binding affinity.

[0061] Among them, gene variation analysis and mutant protein identification: the original data of whole exome sequencing of tumor tissue and matched blood cell DNA samples were quality controlled and screened, the sequencing adapter sequences and low-quality fragments were removed, and the BWA-MEM (bwa-0.7.15) analysis software was used for high-quality sequencing data, and the sequence was compared according to the human gene reference database (hg19). The Samtools analysis tool was used to convert the SAM data file into a BAM data file. The GATK analysis tool was used to analyze the gene variation of the BAM data file, and the specific analysis was carried out according to the standard analysis process.

[0062] HLA typing: For the whole exome sequencing data of blood cell DNA samples, the whole exome sequencing data of chromosome 6 HLA molecules were selected, and the HLA type I gene was typed using Opitype software with reference to the HLA reference public database IMGT / HLA. The HLA typing results were further confirmed and analyzed using the HLA-VBseq tool.

[0063] Predicting the affinity of new antigen-MHC binding: NetMHCpan-3.0 software algorithm is used to predict the affinity of wild-type and mutant antigen peptides to HLA molecules. Candidate antigen peptides: MHC-binding peptides are generally 8-11 amino acids. Affinity is expressed by IC50 parameters, that is, the concentration of peptides when HLA binds to half. IC50 < 50nM is screened, and mutant peptides with less than 70% of wild-type affinity are neoantigens.

[0064] 3. Analysis of immune cycle parameters of solid tumors.

[0065] For the whole transcriptome sequencing data of tumor tissues, according to the standard analysis process, HISAT2 software (version number: v2.0.4) was used to align the sequences according to the human genome (hg19), and StringTie software (version number: v1.2.3) was used for transcriptome assembly and TPM (Transcripts Per Million) value of gene expression was calculated. R software (version number: 3.6.1) and R.GSVA software package were used to evaluate the immune cycle parameters by gene set variation analysis (GSVA, Gene Set Variation Analysis), including: T cell immunity (IGS1), sensitization and activation (ICS3), chemotaxis and infiltration (IGS4), recognition of tumor cells (IGS5), inhibitory cells (IGS6), immune checkpoint molecule expression (IGS7), and inhibitory molecule expression (IGS8).

[0066] 4. Use the unsupervised clustering algorithm non-negative matrix factorization (NMF) algorithm to classify the immune cycle of solid tumors.

[0067] The tumor immune cycle was unsupervised clustered using the non-negative matrix factorization (NMF) algorithm. NMF clustering was performed using the NMF R package (version 0.22.0) using the NMF R package built-in algorithm brunet and 200 iterations. Figure 3 According to the cluster parameter evaluation diagram, the first value where the cophenetic correlation coefficient starts to decrease continuously is selected, and the optimal number of clusters is determined to be 4. Figure 4 The heat map of solid tumor immune cycle clustering with optimal clustering (4 categories) is shown. Figure 5 A heat map showing immune cycle assessment in eight dimensions for 8,460 tumor tissue samples from 32 solid tumors.

[0068] 5. Visual display of the immune cycle of solid tumors.

[0069] like Figure 6As shown, the eight dimensions of the immune cycle are visualized using a radar chart, including: T cell immunity (IGS1), neoantigen load (IGS2), sensitization and activation (ICS3), chemotaxis and infiltration (IGS4), tumor cell recognition (IGS5), inhibitory cells (IGS6), immune checkpoint molecule expression (IGS7) and inhibitory molecule expression (IGS8).

[0070] 6. Analysis of immune characteristics of solid tumors based on four immune circulation classifications.

[0071] Combined with reference Figure 1B The tumor microenvironment is composed of a variety of innate and adaptive immune cell subsets. This application evaluates the relative abundance of 28 immune cell subsets in tumor tissue. Immune circulating type I tumors show high levels of infiltrating immune cells (reference Fig. 7A , 7B , 7C), a large number of unique T cell receptor (TCR) clonotypes and a high TCR Shannon index (ref. Fig.7D , 7E ). In contrast, immune cycle types II, III, and IV showed lower levels of infiltrating immune cells, fewer unique TCR clonotypes, and higher TCR Shannon indices (reference Fig.7D , 7E At the same time, immune markers including cytotoxic activity, IFN-γ signature, major histocompatibility complex (MHC) class I and MHC class II, and immunosuppressive and immunostimulatory molecules were increased in immune circulating type I tumors (ref. FIG. 8A to FIG. 8D and Fig. 9 In particular, upregulation of MHC class I expression was observed in immune circulating type III tumors (ref. Fig. 8A , Fig.8D In addition, the present invention observed higher levels of immunosuppressive molecules, including TGF-β1, TGF-β2, TGF-β3, and IL-10, in immune circulating type I and II tumors (refer to FIG. 10A to FIG. 10D ). Collectively, these findings suggest that immune cycle type I is characterized by "hot" tumor characteristics, showing activated and exhausted immune patterns, while immune cycle types II, III, and IV are characterized by "cold" tumor characteristics, showing an immunosuppressive phenotype.

[0072] 7. Tumor evolution characteristics and immune editing characteristics of solid tumors classified by four immune circulation types.

[0073] Combined with reference Figure 1B, during tumor evolution, the immune system exhibits strong selection pressure. In order to evaluate the selection pattern of tumor evolution, this application evaluated the evolutionary selection pattern of 715 known tumor-related genes (tumor clone positive selection level markers) in 8,460 patients with solid tumors, by calculating the ratio of non-synonymous mutations to synonymous mutations (dN / dS ratio), and using the R software package to calculate the dN / dS ratio. The dN / dS ratios of solid tumors of the four immune circulation types were all greater than 1, indicating positive selection of the tumor immune cycle. However, this application found that the dN / dS ratios were different in tumors of the four immune circulation subtypes (reference Fig.11A , 11B , 11C). Immune cycle type IV is characterized by low dN / dS ratios for all nonsynonymous, missense, and nonsense mutations in 715 known tumor genes ( Fig.11A , 11B , 11C). In contrast, immune cycle type II had the highest dN / dS ratio for nonsynonymous and missense tumor mutations among the four subtypes (ref. Fig.11A , 11B , 11C). Notably, immune cycle type I, which has a good overall survival rate, showed an intermediate level of dN / dS ratio compared with other immune cycle subtypes (ref. Fig. 12A , 12B , 12C). For further validation, the present application evaluated the dN / dS ratios in the resampled dataset of 715 genes (500 iterations) and found that the dN / dS ratios of 715 known tumor-related genes in all four immune circulation subtypes were higher than those of 715 randomly selected genes ( FIG. 12A to FIG. 12C ). This suggests that these tumor-related genes are positively selected in tumor evolution. In addition, the present application found that both immune cycle type II and IV tumors have a high degree of tumor heterogeneity, which is reflected by the mutant-allele tumor heterogeneity (MATH) score. Type II and IV tumors have higher MATH scores compared with other immune cycle subtypes (refer to Fig.11D ). However, the clonal selection patterns of immune cycle type II and IV tumors are different. The present application found that although immune cycle type IV tumors have lower levels of positive selection, they exhibit high tumor neoantigen burden (TNB). This is due to the lower immunoediting capacity of immune cycle type IV tumors (refer to Fig.11E In contrast, immune-circulating type II tumors have high levels of positive selection and low levels of neoantigen load (ref. Fig.11A , 11B , 11C, 11F).

[0074] 8. Implications of the immune circulation phenotypic characteristics of solid tumors for clinical treatment strategies and their correlation with clinical prognosis.

[0075] Combined with reference Figure 1B , based on the immune cycle theory, a comprehensive analysis and classification of the immune cycle of solid tumors was carried out using visualization and quantitative methods. The update of the tumor immune cycle theory emphasizes the iterative nature of the anti-tumor immune response to adapt to tumor evolution. Any weak link in the tumor immune cycle may become a speed-limiting bottleneck, causing the tumor to escape the attack of the immune system. This application shows the four main tumor immune cycle patterns in solid tumors, revealing the main shortcomings of solid tumors in the tumor immune cycle. These findings provide a theoretical basis for the implementation of treatment strategies for each immune profile subtype.

[0076] Refer to Figures 13 and Fig.14 Solid tumors with tumor immune cycle type I show the characteristics of "exhausted hot tumors" and have the best clinical prognosis. Solid tumors with tumor immune cycle type I show higher expression levels of tumor immune activation markers (T cell immune markers, sensitization and activation markers, chemotaxis and infiltration markers) and immune exhaustion markers (immune checkpoint molecule expression markers) than the other three types. Immune checkpoint inhibitor treatment can reactivate and possibly enhance existing anti-tumor immune responses.

[0077] Tumor immune cycles II, III, and IV show different characteristics of "cold tumors", as follows:

[0078] Immune cycle type II solid tumors show the characteristics of "aggressive cold tumors", with a faster tumor evolution rate, a high level of positive selection during tumor evolution, a high level of expression of immunosuppressive molecules, and the worst clinical prognosis; immune cycle type II solid tumors are characterized by "cold" and "aggressive", representing high-speed tumor evolution, which may lead to the accumulation of more harmful mutations. At the same time, compared with type I, immune cycle type II solid tumors show low levels of immune activation markers and high levels of tumor immunosuppressive molecules. These factors lead to a higher ability of immune cycle type II tumors to escape immune attacks. Therefore, a combined treatment strategy of activating tumor immune responses and neutralizing immunosuppressants may be effective.

[0079] Immune cycle type III solid tumors show the characteristics of "recognizable cold tumors", with higher expression levels of MHC-I molecules and lower tumor immune activation markers than immune cycle type I solid tumors (T cell immune markers, sensitization and activation markers, chemotaxis and infiltration markers), thus presenting an immunosuppressive phenotype, indicating that immune cycle type III solid tumors have upregulated MHC-I expression, showing that the immune system has the potential to recognize tumor antigens. Therefore, the treatment strategy for immune cycle type III solid tumors should focus on improving other weak links in the immune cycle, including T cell immunity, priming and activation, migration and infiltration.

[0080] Solid tumors in immune cycle IV show lower expression levels of tumor immune activation markers (T cell immune markers, sensitization and activation markers, chemotaxis and infiltration markers, and tumor cell recognition markers) than the other three types of circulating phenotypes, lower tumor evolution rate markers (tumor clone positive selection level markers) than the other three types of circulating phenotypes, and higher expression levels of tumor neoantigen load markers than the other three types of circulating phenotypes, showing the characteristics of "inert cold tumors". Therefore, the treatment strategy for solid tumors in immune cycle IV may require activation of tumor immunity.

[0081] The following describes the embodiments of the present application in conjunction with the tumor immune cycle of metastatic urothelial carcinoma.

[0082] 1. Collect tumor tissue and matched blood cell samples from patients with metastatic urothelial carcinoma, separate tumor tissue DNA and RNA, and blood cell DNA, perform whole exome sequencing on tumor tissue and blood cell DNA, and perform transcriptome sequencing on tumor tissue RNA.

[0083] 2. Prediction of new antigens.

[0084] Using whole exome sequencing data of tumor tissue and blood cell DNA matching, the prediction of neoantigens is divided into three steps: identification of mutant proteins after tumor-normal tissue pairing, HLA typing, and prediction of neoantigen-MHC binding affinity.

[0085] Gene variation analysis and identification of mutant proteins: The original data of whole exome sequencing of tumor tissue and matched blood cell DNA samples were quality controlled and screened, and the sequencing adapter sequences and low-quality fragments were removed. The high-quality sequencing data were analyzed using BWA-MEM (bwa-0.7.15) analysis software, and sequence comparison was performed according to the human gene reference database (hg19). The Samtools analysis tool was used to convert the SAM data file into a BAM data file. The GATK analysis tool was used to analyze the gene variation of the BAM data file, and the specific analysis was carried out according to the standard analysis process.

[0086] HLA typing: For the whole exome sequencing data of blood cell DNA samples, the whole exome sequencing data of chromosome 6 HLA molecules were selected, and the HLA type I gene was typed using Opitype software with reference to the HLA reference public database IMGT / HLA. The HLA typing results were further confirmed and analyzed using the HLA-VBseq tool.

[0087] Predicting the affinity of new antigen-MHC binding: NetMHCpan-3.0 software algorithm is used to predict the affinity of wild-type and mutant antigen peptides to HLA molecules. Candidate antigen peptides: MHC-binding peptides are generally 8-11 amino acids. Affinity is expressed by IC50 parameters, that is, the concentration of peptides when HLA binds to half. IC50 < 50nM is screened, and mutant peptides with less than 70% of wild-type affinity are neoantigens.

[0088] 3. Analysis of tumor circulation parameters in metastatic urothelial carcinoma.

[0089] For the whole transcriptome sequencing data of tumor tissues, according to the standard analysis process, HISAT2 software (version number: v2.0.4) was used to align the sequences according to the human genome (hg19), and StringTie software (version number: v1.2.3) was used for transcriptome assembly and TPM (Transcripts Per Million) value of gene expression was calculated. R software (version number: 3.6.1) and R.GSVA software package were used to evaluate the immune cycle parameters by gene set variation analysis (GSVA, Gene Set Variation Analysis), including: T cell immunity (IGS1), sensitization and activation (ICS3), chemotaxis and infiltration (IGS4), recognition of tumor cells (IGS5), inhibitory cells (IGS6), immune checkpoint molecule expression (IGS7), and inhibitory molecule expression (IGS8).

[0090] 4. Visual display of the immune cycle of solid tumors.

[0091] The eight dimensions of the immune cycle are visualized using a radar chart, including: T cell immunity (IGS1), neoantigen load (IGS2), sensitization and activation (ICS3), chemotaxis and infiltration (IGS4), tumor cell recognition (IGS5), inhibitory cells (IGS6), immune checkpoint molecule expression (IGS7), and inhibitory molecule expression (IGS8).

[0092] 5. Analysis of tumor immune circulation classification and treatment efficacy of immune checkpoint inhibitors in metastatic urothelial carcinoma.

[0093] The tumor immune circulation and clinical efficacy of immune checkpoint inhibitors (ICI) in patients with metastatic urothelial carcinoma (clinical cohort name: IMvigor210) were analyzed. The immune circulation phenotypes of patients with metastatic urothelial carcinoma were divided into four categories using non-negative matrix factorization clustering method, namely tumor immune circulation type I, tumor immune circulation type II, tumor immune circulation type III, and tumor immune circulation type IV.

[0094] Referring to Figure 15, after receiving ICI treatment, compared with other immune cycles, patients with metastatic urothelial carcinoma of tumor immune cycle type I and IV had a significant benefit in overall survival, as follows: tumor immune cycle type I (n = 147, median OS: 11.93 months); tumor immune cycle IV (n = 6, median OS: 15.64 months). On the contrary, after receiving ICI treatment, patients with metastatic urothelial carcinoma of tumor immune cycle type II and III had a significantly shortened overall survival, as follows: tumor immune cycle type II (n = 50, median OS: 7.46 months), tumor immune cycle III (n = 42, median OS: 5.82 months).

[0095] The following is a description of an embodiment of the present application in conjunction with metastatic gastric cancer. In this embodiment, the immune circulation typing of metastatic gastric cancer and the objective response rate of patients to PD-1 inhibitor (pembrolizumab) treatment are analyzed.

[0096] 1. Collect tumor tissue and matched blood cell samples from patients with metastatic gastric cancer, separate tumor tissue DNA and RNA, and blood cell DNA, perform whole exome sequencing on tumor tissue and blood cell DNA, and perform transcriptome sequencing on tumor tissue RNA.

[0097] 2. Prediction of new antigens.

[0098] Using whole exome sequencing data of tumor tissue and blood cell DNA matching, the prediction of neoantigens is divided into three steps: identification of mutant proteins after tumor-normal tissue pairing, HLA typing, and prediction of neoantigen-MHC binding affinity.

[0099] Gene variation analysis and identification of mutant proteins: Quality control and screening of the original data of whole exome sequencing of tumor tissue and matched blood cell DNA samples were performed to remove the sequencing adapter sequences and low-quality fragments. BWA-MEM (bwa-0.7.15) analysis software was used for high-quality sequencing data, and sequence comparison was performed according to the human gene reference database (hg19). Samtools analysis tool was used to convert SAM data files into BAM data files.

[0100] The GATK analysis tool was used to perform gene variation analysis on the BAM data file. The specific analysis can refer to the standard analysis process.

[0101] HLA typing: For the whole exome sequencing data of blood cell DNA samples, the whole exome sequencing data of chromosome 6 HLA molecules were selected, and the HLA type I gene was typed using Opitype software with reference to the HLA reference public database IMGT / HLA. The HLA typing results were further confirmed and analyzed using the HLA-VBseq tool.

[0102] Predicting the affinity of new antigen-MHC binding: NetMHCpan-3.0 software algorithm is used to predict the affinity of wild-type and mutant antigen peptides to HLA molecules. Candidate antigen peptides: MHC-binding peptides are generally 8-11 amino acids. Affinity is expressed by IC50 parameters, that is, the concentration of peptides when HLA binds to half. IC50 < 50nM is screened, and mutant peptides with less than 70% of wild-type affinity are neoantigens.

[0103] 3. Analysis of immune cycle parameters of solid tumors.

[0104] For the whole transcriptome sequencing data of tumor tissues, according to the standard analysis process, HISAT2 software (version number: v2.0.4) was used to align the sequences according to the human genome (hg19), and StringTie software (version number: v1.2.3) was used for transcriptome assembly and TPM (Transcripts Per Million) value of gene expression was calculated. R software (version number: 3.6.1) and R.GSVA software package were used to evaluate the immune cycle parameters by gene set variation analysis (GSVA, Gene Set Variation Analysis), including: T cell immunity (IGS1), sensitization and activation (ICS3), chemotaxis and infiltration (IGS4), recognition of tumor cells (IGS5), inhibitory cells (IGS6), immune checkpoint molecule expression (IGS7), and inhibitory molecule expression (IGS8).

[0105] 4. Visual display of the immune cycle of solid tumors.

[0106] The eight dimensions of the immune cycle are visualized using a radar chart, including: T cell immunity (IGS1), neoantigen load (IGS2), sensitization and activation (ICS3), chemotaxis and infiltration (IGS4), tumor cell recognition (IGS5), inhibitory cells (IGS6), immune checkpoint molecule expression (IGS7), and inhibitory molecule expression (IGS8).

[0107] 5. Analysis of immune circulation classification of metastatic gastric cancer and clinical response to treatment with PD-1 inhibitor (pembrolizumab).

[0108] Using the non-negative matrix factorization clustering method, the immune circulation phenotypes of patients with metastatic gastric cancer were divided into four categories, namely tumor immune circulation type I, type II, type III, and type IV. Fig.16 , patients with metastatic gastric cancer with tumor immune cycle type I phenotype had a high clinical response rate to PD-1 inhibitors (patient proportion: complete remission, 50%; partial remission, 50%). In contrast, patients with metastatic gastric cancer with immune cycle types II, III, and IV showed a lower clinical response rate to PD-1 inhibitors.

[0109] According to an embodiment of the present application, a solid tumor immune circulation typing system is also provided, which includes:

[0110] The acquisition module is used to acquire tumor tissue DNA and blood cell DNA for whole exome sequencing to obtain whole exome sequencing data; acquire tumor tissue RNA for whole transcriptome sequencing to obtain whole transcriptome sequencing data;

[0111] An immune cycle parameter evaluation module, used to evaluate immune cycle parameters according to the whole transcriptome sequencing data, wherein the immune cycle parameters include: T cell immune markers, sensitization and activation markers, chemotaxis and infiltration markers, tumor cell recognition markers, inhibitory cell markers, immune checkpoint molecule expression markers and inhibitory molecule expression markers;

[0112] A tumor neoantigen load marker prediction module, used to predict tumor neoantigen load markers based on the whole exome sequencing data and the whole transcriptome sequencing data;

[0113] The classification module is used to perform unsupervised clustering of tumor immune circulation parameters and tumor neoantigen load markers using a non-negative matrix factorization algorithm. According to the clustering results, the solid tumor immune circulation phenotypes are divided into: tumor immune circulation type I, tumor immune circulation type II, tumor immune circulation type III and tumor immune circulation type IV;

[0114] Among them, the tumor immune cycle type I shows hot tumor characteristics, and the tumor immune cycle type II, tumor immune cycle type III and tumor immune cycle type IV show cold tumor characteristics;

[0115] Among them, the clinical prognosis of patients with solid tumors of the four immune circulation types after receiving anti-tumor treatment was that the tumor immune circulation type I had the longest overall survival, and the tumor immune circulation type II had the shortest overall survival.

[0116] The operation steps of the method of the present invention correspond to the structural features of the system, and can be referenced to each other, and will not be described in detail again.

[0117] Although the present disclosure has been described in detail with reference to the specific embodiments of the present application, it will be understood by those skilled in the art that various changes and modifications may be made therein without departing from the spirit and scope of the embodiments. Therefore, the present application is intended to cover the modifications and variations of the present application, and any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included within the scope of the claims of the present application and their equivalents.

[0118] In addition, the features disclosed in the above description or claims or drawings, in their specific form or according to the mode for performing the disclosed functions or the method or process for obtaining the disclosed results, can be used to implement the present application in their different forms alone or in any combination of these features, as appropriate. Specifically, one or more features of any embodiment described in the present application can be combined with one or more features of any other embodiment described in the present application.

[0119] Protection may also be sought for any features disclosed in any one or more of the publications cited in conjunction with the present application and / or incorporated by reference.

Claims

1. A method for solid tumor immune circulation typing, characterized in that: include: Obtain tumor tissue DNA and blood cell DNA for whole exome sequencing to obtain whole exome sequencing data; Obtaining tumor tissue RNA for whole transcriptome sequencing to obtain whole transcriptome sequencing data; Evaluating immune cycle parameters according to the whole transcriptome sequencing data, wherein the immune cycle parameters include: T cell immune markers, sensitization and activation markers, chemotaxis and infiltration markers, tumor cell recognition markers, inhibitory cell markers, immune checkpoint molecule expression markers, and inhibitory molecule expression markers; Predicting tumor neoantigen load markers according to the whole exome sequencing data and the whole transcriptome sequencing data; The non-negative matrix factorization algorithm was used to perform unsupervised clustering of tumor immune cycle parameters and tumor neoantigen load markers. According to the clustering results, the solid tumor immune cycle phenotypes were divided into: tumor immune cycle type I, tumor immune cycle type II, tumor immune cycle type III and tumor immune cycle type IV. Among them, the tumor immune cycle type I shows hot tumor characteristics, and the tumor immune cycle type II, tumor immune cycle type III and tumor immune cycle type IV show cold tumor characteristics; Among them, the clinical prognosis of patients with solid tumors of the four immune circulation types after receiving anti-tumor treatment was that the tumor immune circulation type I had the longest overall survival, and the tumor immune circulation type II had the shortest overall survival.

2. The method according to claim 1, characterized in that: The solid tumor of tumor immune cycle type I is characterized by higher expression levels of the first tumor immune activation marker and immune exhaustion marker than the other three types of circulating phenotypes. The tumor immune cycle type I presents exhaustion-type hot tumor characteristics, wherein: The first tumor immune activation marker includes: T cell immune markers, sensitization and activation markers, chemotaxis and infiltration markers; The immune exhaustion markers include: immune checkpoint molecule expression markers.

3. The method according to claim 1, characterized in that The solid tumors of tumor immune circulation type II are characterized by higher expression levels of tumor immune suppression markers than the other three types of circulation phenotypes, lower expression levels of tumor immune activation markers than the solid tumors of tumor immune circulation type I, and higher expression levels of tumor evolution rate markers than the other three types of circulation phenotypes. The tumor immune circulation type II presents aggressive cold tumor characteristics, wherein, The first tumor immunosuppression marker includes: an inhibitory molecule expression marker; The second tumor immune activation marker includes: T cell immune markers, sensitization and activation markers, chemotaxis and infiltration markers, and tumor cell recognition markers; The tumor evolution rate markers include: tumor clone positive selection level markers.

4. The method according to claim 1, characterized in that: The solid tumor of tumor immune circulation type III is characterized by a higher expression level of tumor cell recognition markers than the other three types of circulation phenotypes, and a lower expression level of the first tumor immune activation marker than that of immune circulation type I solid tumors. The tumor immune circulation type III presents identifiable cold tumor characteristics, wherein: The first tumor immune activation marker includes: T cell immune markers, sensitization and activation markers, and chemotaxis and infiltration markers.

5. The method according to claim 1, characterized in that The solid tumor of tumor immune circulation type IV is characterized by lower expression level of the second tumor immune activation marker than the other three types of circulation phenotypes, lower expression level of tumor evolution rate marker than the other three types of circulation phenotypes, and higher expression level of tumor neoantigen load marker than the other three types of circulation phenotypes. The tumor immune circulation type IV presents the characteristics of an inert cold tumor, wherein, The second tumor immune activation marker includes: T cell immune markers, sensitization and activation markers, chemotaxis and infiltration markers, and tumor cell recognition markers; The tumor evolution rate markers include: tumor clone positive selection level markers.

6. The method according to claim 1, characterized in that Also includes: The first value at which the common classification correlation coefficient of the NMF algorithm begins to decrease continuously is determined as the number of classifications of the phenotype of the tumor immune cycle.

7. The method according to claim 1, characterized in that Also includes: The anti-tumor immune cycle is represented by eight-dimensional gene parameters including tumor immune cycle parameters and tumor neoantigen load markers, and the eight-dimensional gene parameters are visualized.

8. The method according to claim 7, characterized in that Also includes: The gene parameters of the eight dimensions are displayed in a radar chart visualization manner.

9. A solid tumor immune circulation typing system, characterized in that: include: An acquisition module is used to acquire tumor tissue DNA and blood cell DNA for whole exome sequencing to obtain whole exome sequencing data; Obtaining tumor tissue RNA for whole transcriptome sequencing to obtain whole transcriptome sequencing data; An immune cycle parameter evaluation module, used to evaluate immune cycle parameters according to the whole transcriptome sequencing data, wherein the immune cycle parameters include: T cell immune markers, sensitization and activation markers, chemotaxis and infiltration markers, tumor cell recognition markers, inhibitory cell markers, immune checkpoint molecule expression markers and inhibitory molecule expression markers; A tumor neoantigen load marker prediction module, used to predict tumor neoantigen load markers based on the whole exome sequencing data and the whole transcriptome sequencing data; The classification module is used to perform unsupervised clustering of tumor immune circulation parameters and tumor neoantigen load markers using a non-negative matrix factorization algorithm. According to the clustering results, the solid tumor immune circulation phenotypes are divided into: tumor immune circulation type I, tumor immune circulation type II, tumor immune circulation type III and tumor immune circulation type IV; Among them, the tumor immune cycle type I shows hot tumor characteristics, and the tumor immune cycle type II, tumor immune cycle type III and tumor immune cycle type IV show cold tumor characteristics; Among them, the clinical prognosis of patients with solid tumors of the four immune circulation types after receiving anti-tumor treatment was that the tumor immune circulation type I had the longest overall survival, and the tumor immune circulation type II had the shortest overall survival.

10. Use of the solid tumor immune circulation typing method according to claims 1 to 8 in tumor personalized immune assessment.

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