WO2022016447A1 - 用于评估结直肠癌患者对免疫治疗药物响应性的标志物 - Google Patents

用于评估结直肠癌患者对免疫治疗药物响应性的标志物 Download PDF

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WO2022016447A1
WO2022016447A1 PCT/CN2020/103776 CN2020103776W WO2022016447A1 WO 2022016447 A1 WO2022016447 A1 WO 2022016447A1 CN 2020103776 W CN2020103776 W CN 2020103776W WO 2022016447 A1 WO2022016447 A1 WO 2022016447A1
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marker
markers
genes
colorectal cancer
cells
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French (fr)
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孙恬
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碳逻辑生物科技(香港)有限公司
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer

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  • the invention relates to the field of biomedicine, in particular to a marker for evaluating the response of colorectal cancer patients to immunotherapy drugs.
  • PDL1 can directly indicate whether tumor samples from colorectal cancer patients are infiltrating CD8+ T cells; whereas MSI/MSS status, TMB, POLE/POLD1 variants, or MSI-like gene signatures characterize tumor samples that produce high neoantigen levels The possibility, therefore, that neoantigen levels can indirectly indicate whether tumor samples from colorectal cancer patients may potentially harbor infiltrating CD8+ T cells.
  • tumors need to meet at least two characteristics to be a responder to anti-PD1 therapy.
  • First, tumors should have infiltrating CD8+ T cells; second, at least one tumor A subset of infiltrating CD8+ T cells (whether direct repopulation of CD8+ T cells already infiltrated in the tumor or new CD8+ T cells that accumulate into the tumor indirectly from surrounding sites) exhibit properties of anti-PD1 responses.
  • the technical problem to be solved by the present invention is to provide a marker for evaluating the responsiveness of colorectal cancer patients to immunotherapy drugs, which can evaluate the responsiveness of colorectal cancer patients to immunotherapy drugs and/or evaluate the immunotherapy drugs Therapeutic effect on colorectal cancer.
  • the embodiments of the present invention provide markers for detecting CD8+ T cells in patients with colorectal cancer, and the markers include markers for judging whether there are infiltrating CD8+ T cells in patients with colorectal cancer.
  • the first marker includes genes CXCL13, LY6G6D, CXCL10, SRSF6, SAMD9L, TNFSF13B, RASGRP1, CAB39L, SET, EIF5A, ITCH, TRIM69, MCUB, TYMS, ZDHHC9, LYSMD2, ZCCHC2, BRD3, PSME2 At least two of , PSME1, NR6A1, ATP5F1A, and NUTM2A-AS1; more preferably, the marker is selected from at least 5 genes in the 23 genes; more preferably, the biomarker is selected from the At least 6 genes among the 23 genes; more preferably, the markers are selected from at least 7 genes among the 23 genes; more preferably, the markers are selected from at least 8 among the 23 genes gene; more preferably, the marker is selected from at least 9 genes among the 23 genes; more preferably, the marker is selected from at least 10 genes among the 23 genes; more preferably, the The marker is selected from at least 15 genes among the 23 genes;
  • the marker is a gene
  • the first marker is the gene CXCL13, LY6G6D, CXCL10, SRSF6, SAMD9L, TNFSF13B, RASGRP1, CAB39L, SET, EIF5A, ITCH, TRIM69, At least two of MCUB, TYMS, ZDHHC9, LYSMD2, ZCCHC2, BRD3, PSME2, PSME1, NR6A1, ATP5F1A, NUTM2A-AS1, CCL5, GZMA, GBP1, STAT1 and CXCL9; more preferably, the marker is selected from the group consisting of At least 5 genes among the 28 genes; more preferably, the marker is selected from at least 6 genes among the 28 genes; more preferably, the marker is selected from at least 7 among the 28 genes gene; more preferably, the marker is selected from at least 8 of the 28 genes; more preferably, the marker is selected from at least 9 of the 28 genes; more preferably,
  • the marker further includes a marker for judging the exhaustion mode of the infiltrating CD8+ T cells.
  • the second marker is the genes C1QC, FCGR1B, C1QB, TFEC, CD2, FCER1G, KMO, APBB1IP, CD48, LAPTM5, CYBB, NCF1B, NR1H3, IFI30, WIPF1, At least two of SLAMF8, FAM78A, HCST, IL4I1, TNFSF14, LILRB3, CSF1.
  • the marker is selected from at least 5 genes among the 22 genes; more preferably, the marker is selected from at least 6 genes among the 22 genes; more preferably, the marker selected from at least 7 genes in the 22 genes; more preferably, the marker is selected from at least 8 genes in the 22 genes; more preferably, the marker is selected from the 22 genes at least 9 genes; more preferably, the marker is selected from at least 10 genes among the 22 genes; more preferably, the marker is selected from at least 15 genes among the 22 genes; more preferably , the marker is selected from at least 20 genes in the 22 genes; more preferably, the marker is the 22 genes.
  • the marker is the genes C1QC, FCGR1B, C1QB, TFEC, CD2, FCER1G, KMO, APBB1IP, CD48, LAPTM5, CYBB, NCF1B, NR1H3, IFI30, WIPF1, SLAMF8, At least two of FAM78A, HCST, IL4I1, TNFSF14, LILRB3, CSF1, PDCD1, CD84, IL21R, HAVCR2, FCGR1A, CCL5 and CXCL9; more preferably, the marker is selected from at least 5 of the 29 genes gene; more preferably, the marker is selected from at least 6 genes in the 29 genes; more preferably, the marker is selected from at least 7 genes in the 29 genes; more preferably, the The marker is selected from at least 8 genes among the 29 genes; more preferably, the marker is selected from at least 9 genes among the 29 genes; more preferably, the marker is selected from the 29 genes at least 10 genes in the; more
  • the immunotherapy drug is one of anti-PD1, anti-PDL1, anti-CTLA4, anti-TIM3, anti-BTLA, anti-VISTA and anti-LAG3.
  • the present invention provides a kit for evaluating the responsiveness of colorectal cancer patients to immunotherapy drugs, the kit contains the content of the markers for evaluating the infiltration of CD8+ T cells in colorectal cancer patients according to any one of the above detection reagents.
  • the detection reagent includes a probe for detecting the gene, or/and a reagent for detecting the content of the corresponding mRNA, cDNA or/and protein of the gene.
  • the detection reagent is a monoclonal antibody to the protein encoded by the gene.
  • the present invention provides a method for assessing the responsiveness of a cancer patient to a single immunotherapy drug, comprising the steps of:
  • the samples are selected from at least one of blood samples, serum samples, mononuclear cell samples isolated from peripheral blood, tissue samples and body fluid samples;
  • step 2) if the judgment result of step 2) is no, the colorectal cancer patient has no response to the single immunotherapy drug, and the evaluation ends;
  • step 2) If the determination result of step 2) is yes, then the content of the second marker in the biological sample needs to be detected to determine the exhaustion mode of the infiltrating CD8+ T cells,
  • the failure mode is the precursor failure mode
  • the colorectal cancer patient is the responder of the single immunotherapy drug
  • the failure mode is the terminal failure mode
  • the colorectal cancer patient is determined to be the single immune therapy drug.
  • Non-responders to treatment drugs the non-responsive cancer patients need combination immunotherapy drug treatment, for example, combination therapy of anti-PD1 and other drugs targeting the tumor microenvironment should be considered, or other single immunotherapy drug treatment should be replaced.
  • the first marker is the genes CXCL13, LY6G6D, CXCL10, SRSF6, SAMD9L, TNFSF13B, RASGRP1, CAB39L, SET, EIF5A, ITCH, TRIM69, MCUB, TYMS, ZDHHC9, At least two of LYSMD2, ZCCHC2, BRD3, PSME2, PSME1, NR6A1, ATP5F1A, NUTM2A-AS1; more preferably, the first marker is selected from at least 5 genes among the 23 genes; more preferably, The first marker is selected from at least 6 genes in the 23 genes; more preferably, the first marker is selected from at least 7 genes in the 23 genes; more preferably, the first The marker is selected from at least 8 genes among the 23 genes; more preferably, the first marker is selected from at least 9 genes among the 23 genes; more preferably, the first marker is selected from At least 10 genes among the 23 genes; more preferably, the first marker is
  • the first marker is the genes CXCL13, LY6G6D, CXCL10, SRSF6, SAMD9L, TNFSF13B, RASGRP1, CAB39L, SET, EIF5A, ITCH, TRIM69, MCUB, TYMS, ZDHHC9, At least two of LYSMD2, ZCCHC2, BRD3, PSME2, PSME1, NR6A1, ATP5F1A, NUTM2A-AS1, CCL5, GZMA, GBP1, STAT1 and CXCL9; more preferably, the first marker is selected from the 28 genes at least 5 genes of ; more preferably, the first marker is selected from at least 6 genes in the 28 genes; more preferably, the first marker is selected from at least 7 genes in the 28 genes; More preferably, the first marker is selected from at least 8 genes among the 28 genes; more preferably, the first marker is selected from at least 9 genes among the 28 genes; more preferably, The first marker is selected
  • the second marker is the genes C1QC, FCGR1B, C1QB, TFEC, CD2, FCER1G, KMO, APBB1IP, CD48, LAPTM5, CYBB, NCF1B, NR1H3, IFI30, WIPF1, At least two of SLAMF8, FAM78A, HCST, IL4I1, TNFSF14, LILRB3 and CSF1; more preferably, the second marker is selected from at least 5 genes among the 22 genes; more preferably, the second The marker is selected from at least 6 genes in the 22 genes; more preferably, the second marker is selected from at least 7 genes in the 22 genes; more preferably, the second marker is selected from At least 8 genes among the 22 genes; more preferably, the second marker is selected from at least 9 genes among the 22 genes; more preferably, the second marker is selected from the 22 genes at least 10 genes in; more preferably, the second marker is selected from at least 15 genes in the 22 genes; more preferably,
  • the second marker is the genes C1QC, FCGR1B, C1QB, TFEC, CD2, FCER1G, KMO, APBB1IP, CD48, LAPTM5, CYBB, NCF1B, NR1H3, IFI30, WIPF1, At least two of SLAMF8, FAM78A, HCST, IL4I1, TNFSF14, LILRB3, CSF1, PDCD1, CD84, IL21R, HAVCR2, FCGR1A, CCL5 and CXCL9; more preferably, the second marker is selected from the 29 genes at least 5 genes of gene; more preferably, the second marker is selected from at least 8 genes among the 29 genes; more preferably, the second marker is selected from at least 9 genes among the 29 genes; more preferably Preferably, the second marker is selected from at least 10 genes among the 29 genes; more preferably, the second marker is selected from at least 15 genes among the 29 genes; more preferably, the The second marker is selected from
  • the immunotherapy drug is one of anti-PD1, anti-PDL1, anti-CTLA4, anti-TIM3, anti-BTLA, anti-VISTA and anti-LAG3.
  • the colorectal cancer patient is in stage I, stage II, stage III or stage IV of colorectal cancer.
  • the present invention provides a method for preparing or screening immunotherapy drugs for colorectal cancer.
  • the cassette or/and the above-mentioned method for evaluating the responsiveness of cancer patients to immunotherapy drugs are used to prepare or screen anti-tumor drugs.
  • the present invention screens out markers that can be used to judge the responsiveness of colorectal cancer patients to immunotherapy drugs and/or evaluate the therapeutic effect of immunotherapy drugs on colorectal cancer, which provides a new approach for the treatment of colorectal cancer .
  • the present invention firstly detects the first marker marker to determine whether the CDT+8 cells are infiltrative, and then detects the second marker to determine the exhaustion pattern of the infiltrating CDT+8 cells, so as to more accurately determine The responsiveness of colorectal cancer patients to cancer treatment drugs is conducive to more accurate drug administration of colorectal cancer patients.
  • Figure 1A is a graph of the overall survival of responders and non-responders in melanoma patients (GSE78220) receiving anti-PD1 therapy versus time, respectively;
  • Figure 1B is a graph of the overall survival of responders and the overall survival of non-responders in melanoma patient set 1 (GSE91061) receiving anti-PD1 therapy versus time, respectively;
  • Figure 1C is a graph of the overall survival of responders and non-responders of melanoma patient pool 2 (GSE91061) receiving anti-PD1 therapy versus time, respectively;
  • Figure 2A is a graph showing the correlation between MCP counts of cytotoxic lymphocytes and labeled CD8+ T cell infiltration scores
  • Figure 2B is a graph showing the correlation between MCPcounter counted CD8+ T cells and labeled CD8+ T cell infiltration scores
  • Figure 2C is a graph showing the correlation between TIDE calculated cytotoxic T lymphocytes (CTL) and labeled CD8+ T cell infiltration scores;
  • Figure 3 is a data graph of lymphocytic choriomeningitis virus (LCMV) response scores and tumor TME2.T cell response scores;
  • LCMV lymphocytic choriomeningitis virus
  • Figure 4 is a data distribution graph of TME1.T cell infiltration scores of tumor cells from 454 samples and TME2.T cell responsiveness scores of tumor cells from 454 samples.
  • Example 1 Design of a method for predicting the tumor microenvironment (TMEPRE)
  • the TMEPRE model has two parts, the first part is the score of whether CD8+ T cells are infiltrating (hereinafter referred to as TME1), and the second part is the score of the exhaustion pattern of infiltrating CD8+ T cells (hereinafter referred to as TME2).
  • the expression level of CD8A was used to initially estimate the abundance of CD8+ T cells.
  • the CD8A gene was excluded from the cross-validation process.
  • the genes with the top 60 p-values in at least 80% of the cross-validation processes were selected as markers of TME1, of which 28 genes were screened as markers of TME1, see Table for details A:
  • TME2 specifically: scoring the tumor microenvironment of infiltrating CD8+ T cells, thereby judging the cell exhaustion pattern, and then judging the responsiveness of colorectal cancer patients to immunotherapy drugs.
  • TIM3 is an early-acquired co-expressed inhibitor receptor among all co-expressed inhibitory receptors
  • the co-expression pattern of multiple inhibitory receptors of PD1 and TIM3 was employed to define the terminal exhaustion pattern.
  • the median PD1 expression level was used as the cutoff for PD1 and the median TIM3 expression level was used as the cutoff for TIM3 value.
  • MSI tumors with infiltrating CD8+ T cells and both PD1 and TIM3 expression levels above cutoff were defined as tumor microenvironments with co-expression of multiple early inhibitory receptors.
  • 200 rounds of 10-fold cross-validation were performed between the two groups. In each cross-validation round, t-tests and p-values were ranked for each gene. In 200 rounds of cross-validation, the genes with the top 60 p-values in at least 80% of the cross-validation processes were selected as TME2 markers, of which 29 genes were screened as TME2 markers, see Table for details B:
  • the infiltrating CD8+ T cells are not in the terminal failure mode, it means that the colorectal cancer patients can still respond to the checkpoint inhibitor, and if the infiltrating CD8+ T cells have the terminal failure mode, it means that the colorectal cancer patients are resistant to checkpoint inhibitors. Checkpoint inhibitor unresponsive.
  • the judgment conditions for evaluating whether patients with colorectal cancer are responsive to immunotherapy drugs are summarized: when the first part of the TME1 score is low (indicating that the tumor has no infiltrating CD8+ T cells), It can be determined that colorectal cancer patients do not respond to a single immunotherapy drug; when the first part of the TME1 score is high (indicating tumor-infiltrating CD8+ T cells), further TME2 scores are required.
  • colorectal cancer patients are responsive to a single immunotherapy drug, and if the TME2 score is low (indicating that the tumor infiltrating CD8+ T cells is the mode of terminal failure), it can determine the colorectal cancer.
  • Cancer patients are not responsive to a single immunotherapy drug.
  • the unresponsive cancer patient needs combination immunotherapy drug treatment, for example, it is necessary to consider the combination therapy of anti-PD1 and other drugs targeting the tumor microenvironment, or to replace other single immunotherapy drug treatment.
  • whether a colorectal cancer patient is responsive to immunotherapy drugs is determined not only based on the tumor infiltrating CD8+ T cells, but also on the basis that a subset of tumor-infiltrating CD8+ T cells exhibits anti-PD1 response characteristics.
  • biomarkers in this example are the biomarkers obtained by the TMEPRE method in Example 1.
  • Melanoma is a model tumor widely used to verify CD8+ T cell and immunotherapy response.
  • the present invention uses melanoma as a model for verification.
  • the TMEPRE model was obtained on three datasets of melanoma patients receiving anti-PD1 therapy. Verify, specifically:
  • the ratio ⁇ (TME1) of the TME1 score of the 284 MSS samples and the TME1 score of the 454 samples was calculated; the MCPcounter count cells of the 284 MSS samples were calculated Ratio of cytotoxic lymphocyte score to MCPcounter count cytotoxic lymphocyte score of 454 samples ⁇ (MCPcounter cytotoxic lymphocyte score); calculated MCPcounter count CD8+T cell score of 284 MSS samples and MCPcounter count CD8+T cell score of 454 samples.
  • the ratio of cell scores ⁇ (MCPcounter CD8+ T cells) ; the ratio of TIDE calculated cytotoxic T lymphocyte score of 284 MSS samples to TIDE calculated cytotoxic T lymphocyte score of 454 samples ⁇ (TIDE cytotoxic T lymphocytes) the calculation formulas are as follows:
  • TAE1 (MSS sample maximum score value (TME1) - MSS sample minimum score value (TME1) )/(All sample maximum score value (TME1) - All sample minimum score value (TME1) );
  • MCPcounter cytotoxic lymphocyte (MSS maximum score sample value (MCPcounter cytotoxic lymphocytes) -MSS sample minimum rating value (MCPcounter cytotoxic lymphocytes)) / (maximum score value of all samples (MCPcounter cytotoxic lymphocyte ) - the minimum score value of all samples (MCPcounter cytotoxic lymphocytes) );
  • ⁇ (MCPcounter CD8+T cells) (maximum score value of MSS sample (MCPcounter CD8+T cells) - minimum score value of MSS sample (MCPcounter CD8+T cells) )/(maximum score value of all samples (MCPcounter CD8+T cells) -Minimum score value for all samples (MCPcounter CD8+ T cells );
  • TIDE cytotoxic T lymphocytes (MSS maximum score sample value (TIDE cytotoxic T lymphocyte) -MSS sample minimum rating value (TIDE cytotoxic T lymphocyte)) / (maximum score value of all samples (TIDE cytotoxicity T lymphocytes) - the minimum score value for all samples (TIDE cytotoxic T lymphocytes) );
  • TME1 0.89
  • ⁇ (MCPcounter cytotoxic lymphocytes) 0.67
  • ⁇ (MCPcounter CD8+ T cells) 0.53
  • ⁇ (TIDE cytotoxic lymphocytes) 0.81
  • the MSS tumor group with low MCPcounter CD8+ T cell scores contained relatively high infiltration of CD8+ T cells with TME1 score.
  • the MSS tumor group with low cytotoxic T lymphocyte scores calculated by TIDE Relatively high CD8+ T cell infiltration with a TME1 score suggest that TME1 is more sensitive in detecting tumor-infiltrating toxic lymphocytes in MSS colorectal tumors with lower tumor-infiltrating immune cells because TME1 targets lymphocytes. designed for the tumor microenvironment of rectal cancer, while the MCPcounter counting and TIDE algorithms are not.
  • TME2 score of the TMEPRE model was designed to detect whether tumor-infiltrating CD8+ T cells can respond to anti-PD1 therapy.
  • TME2 indeed captures this signature of tumor-infiltrating CD8+ T cells.
  • TME2 labeling scores of two groups of dysfunctional CD8+ T cells isolated from tumors and chronic viral infection Terminally exhausted tumor-infiltrating CD8 + T cells were no longer able to respond to anti-PD1 therapy, while precursor exhausted tumor-infiltrating CD8+ T cells remained responsive to anti-PD-1 therapy (GSE122713).
  • TME2 signatures are derived from gene expression data from bulk tumor samples, the origin of gene expression is derived from a mixture of CD8+ T cells, tumor cells, and other tumor-infiltrating immune cells in the tumor microenvironment, whereas precursor/terminally exhausted tumors Infiltrating CD8+ T cell data were derived from isolated CD8+ T cells. Therefore, when reading out the TME2 score, only genes derived from CD8+ T cells were utilized. For the 29 genes of TME2, the median expression values of 16 purified immune cells were compared using the BloodSpot and HemaExporer human hematopoietic databases. When CD8+ T cells are the top two immune cell types expressing a gene, the gene is considered to be predominantly expressed by CD8+ T cells.
  • TME2 Seven genes in TME2 (CCL5, CD2, CD48, CD84, FAM78A, HCST, IL21R) pass these criteria and two genes in TME2 (HAVCR2, PDCD1) are inhibitor receptors on CD8+ T cells for Precursor exhausted CD8+ T cells and terminal exhausted CD8+ T cells were defined.
  • TME2 score of a dataset of isolated precursor-depleted tumor-infiltrating CD8+ T cells, terminally exhausted tumor-infiltrating CD8+ T cells.
  • tumors and chronic viral infections eg, lymphocytic choriomeningitis virus
  • the TME2 score does capture the signature of tumor-infiltrating CD8+ T cells that are still able to respond to anti-PD1 with depleted precursor cells.
  • the biomarkers in this example are the biomarkers obtained by the method in Example 1.
  • MSI non-responders are caused by insufficient numbers of tumor-infiltrating CD8+ T cells, and the remaining 50% of MSI non-responders are caused by terminal exhaustion of CD8+ T cells in the tumor microenvironment, These non-responders need to consider combination therapy with anti-PD1 and other drugs targeting the tumor microenvironment.
  • the TMEPRE model yielded that 10.6% of MSS colorectal cancer patients' tumors versus 67.2% MSI colorectal cancer patients' tumors exhibited biological characteristics that could benefit from anti-PD1 therapy, these predicted percentages of MSS tumor responders and MSI The percentage of tumor responders was consistent with the reported benefit of immune-related disease control in colorectal cancer patients treated with pembrolizumab for 20 weeks.
  • tumor tissue preferably, using tumor-related blood as the sample, more preferably, using the mononuclear cells of peripheral blood as the sample;
  • b) Use diagnostic products to detect markers selected from Table A in the sample. When the markers in Table A are low, it indicates that colorectal cancer patients are not responsive to immunotherapy drugs, and the colorectal cancer patients are not suitable for immunotherapy drugs ; When the Table A marker is high, continue to detect the Table B marker. If the Table B marker has a high score, it indicates that the colorectal cancer patient is responsive to immunotherapy drugs, and the colorectal cancer patient is suitable for immunotherapy drugs.
  • the colorectal cancer patient is not responsive to immunotherapy drugs, the colorectal cancer patient is not suitable for a single immunotherapy drug, and the colorectal cancer patient needs combined immunotherapy drug treatment, such as Combination therapy with anti-PD1 and other drugs targeting the tumor microenvironment, or treatment with additional single immunotherapy drugs, needs to be considered.
  • the high scores of the above markers represent up-regulated or down-regulated gene expression, the concentration C1 of the responsive marker is higher than the standard value C0, the gene expression is up-regulated, and the concentration C1 of the responsive marker is lower than the standard value C0, the gene expression is down-regulated.
  • C0 is the expression level of immunotherapy drug responsive markers in the population of immunotherapy drug non-responders.
  • the aforementioned markers may be genes, mRNAs, cDNAs and/or proteins.
  • Table A is the marker described in Table A of Example 1
  • Table B is the marker described in Table B of Example 1.
  • this embodiment detects a marker selected from at least one of Table A markers A1-A23.
  • this embodiment detects at least two markers selected from Table A markers A1-A23, more preferably, at least 5 markers selected from Table A markers A1-A23; more preferably, Markers selected from at least 6 markers in Table A markers A1-A23; more preferably, markers selected from at least 7 markers in Table A markers A1-A23; more preferably, markers selected from Table A At least 8 markers in A1-A23; more preferably, at least 9 or more markers selected from Table A markers A1-A23; more preferably, selected from Table A markers A1-A23 at least 10 markers; more preferably, at least 15 markers selected from Table A markers A1-A23; more preferably, at least 20 markers selected from Table A markers A1-A23; More preferably, all 23 markers are selected from Table A markers A1-A23.
  • this embodiment detects at least two markers selected from Table A markers A1-A28, more preferably, at least 5 markers selected from Table A markers A1-A28; more preferably , selected from at least 6 markers in Table A markers A1-A28; more preferably, selected from at least 7 markers in Table A markers A1-A28; more preferably, selected from Table A markers Markers of at least 8 markers in A1-A28; more preferably, at least 9 markers selected from markers A1-A28 in Table A; more preferably, markers selected from markers A1-A28 in Table A at least 10 markers; more preferably, at least 15 markers selected from Table A markers A1-A28; more preferably, at least 20 markers selected from Table A markers A1-A28 ; more preferably, all 28 markers selected from Table A markers A1-A28.
  • this embodiment detects a marker selected from at least one of Table B markers B1-B22.
  • this embodiment detects at least two markers selected from Table B markers B1-B22, more preferably, at least 6 markers selected from Table B markers B1-B22; more preferably Preferably, at least 7 markers selected from Table B markers B1-B22; more preferably, at least 8 markers selected from Table B markers B1-B22; more preferably, selected from Table B markers At least 9 markers in B1-B22; more preferably, at least 10 markers selected from Table B markers B1-B22; more preferably, at least 15 markers selected from Table B markers B1-B22 Markers; more preferably, at least 20 markers selected from Table B markers B1-B22; more preferably, selected from the 22 markers in Table B markers B1-B22;
  • this embodiment detects at least two markers selected from Table B markers B1-B29. More preferably, at least 6 markers selected from Table B markers B1-B29; more preferably, at least 7 markers selected from Table B markers B1-B29; more preferably, selected from Table B markers At least 8 markers in markers B1-B29; more preferably, at least 9 markers selected from Table B markers B1-B29; more preferably, at least 10 markers selected from Table B markers B1-B29 more preferably, at least 15 markers selected from Table B markers B1-B29; more preferably, at least 20 markers selected from Table B markers B1-B29; more preferably, selected from the 29 markers in Table B markers B1-B29;
  • simultaneous measurement of at least two markers enables a more appropriate and reliable assessment of colorectal cancer patient responsiveness to a single immunotherapy drug, and the present invention uses such panels of markers rather than just a single marker.
  • Example 6 About the detection of at least two markers of colorectal cancer patient typing method
  • the mRNA gene expression level of at least two markers in A1-A28 or A1-A23 in Table A of a certain test object in the sample preferably, the mRNA gene expression level is obtained by the technology of the following group : microarray, RNAseq, RT-PCR.
  • normalization is performed by a method selected from the following group: fRMA, RMA, RNAseq CPM, RNAseq FPKM.
  • the gene expression values of multiple known immunotherapy drug responders can be obtained from clinical medical databases; similarly, the average gene expression values of the same markers of tumor non-responders without infiltrating CD8+ T cells are calculated, that is, obtained separately Gene expression values for at least two markers A1-A28 or A1-A23 in Table A above in multiple tumor non-responders without infiltrating CD8+ T cells, and then count all tumors without infiltrating CD8+ T cells Mean gene expression values for the same markers for non-responders, where gene expression values for tumor non-responders for multiple known immunotherapy drugs without infiltrating CD8+ T cells are available from clinical medical databases.
  • the mRNA gene expression level of at least two markers in the sample B1-B29 or B1-B22 in Table B of the above-mentioned detection object preferably, the mRNA gene expression level is obtained by the technology of the following group : microarray, RNAseq, RT-PCR.
  • Normalize the gene expression values of at least two markers in B1-B29 or B1-B22 in Table B preferably, normalization is performed by a method selected from the following group: fRMA, RMA, RNAseq CPM, RNAseq FPKM.
  • the mean gene expression values for the same markers of CD8+ T cell non-responders were obtained from a plurality of terminally exhausted tumor-infiltrating CD8+ T cell non-responders, respectively Gene expression values for at least two markers, then calculate the mean gene expression values for the same markers for all non-responders with terminally exhausted tumor-infiltrating CD8+ T cells, where multiple known immunotherapy drugs have terminally exhausted tumors
  • Gene expression values for infiltrating CD8+ T cell non-responders can be obtained from clinical medical databases.
  • This embodiment is applicable to the detection of more than two markers, but does not limit the present invention to detect one marker.
  • this embodiment detects at least two markers selected from Table A markers A1-A23, more preferably, at least 5 markers selected from Table A markers A1-A23; more preferably, Markers selected from at least 6 markers in Table A markers A1-A23; more preferably, markers selected from at least 7 markers in Table A markers A1-A23; more preferably, markers selected from Table A At least 8 markers in A1-A23; more preferably, at least 9 or more markers selected from Table A markers A1-A23; more preferably, selected from Table A markers A1-A23 at least 10 markers; more preferably, at least 15 markers selected from Table A markers A1-A23; more preferably, at least 20 markers selected from Table A markers A1-A23; More preferably, all 23 markers are selected from Table A markers A1-A23.
  • this embodiment detects at least two markers selected from Table A markers A1-A28, more preferably, at least 5 markers selected from Table A markers A1-A28; more preferably , selected from at least 6 markers in Table A markers A1-A28; more preferably, selected from at least 7 markers in Table A markers A1-A28; more preferably, selected from Table A markers Markers of at least 8 markers in A1-A28; more preferably, at least 9 markers selected from markers A1-A28 in Table A; more preferably, markers selected from markers A1-A28 in Table A at least 10 markers; more preferably, at least 15 markers selected from Table A markers A1-A28; more preferably, at least 20 markers selected from Table A markers A1-A28 ; more preferably, all 28 markers selected from Table A markers A1-A28.
  • this embodiment detects at least two markers selected from Table B markers B1-B22, more preferably, at least 6 markers selected from Table B markers B1-B22; more Preferably, at least 7 markers selected from Table B markers B1-B22; more preferably, at least 8 markers selected from Table B markers B1-B22; more preferably, selected from Table B markers At least 9 markers in markers B1-B22; more preferably, at least 10 markers selected from Table B markers B1-B22; more preferably, at least 15 markers selected from Table B markers B1-B22 more preferably, at least 20 markers selected from Table B markers B1-B22; more preferably, selected from the 22 markers in Table B markers B1-B22;
  • this embodiment detects at least two markers selected from Table B markers B1-B29. More preferably, at least 6 markers selected from Table B markers B1-B29; more preferably, at least 7 markers selected from Table B markers B1-B29; more preferably, selected from Table B markers At least 8 markers in markers B1-B29; more preferably, at least 9 markers selected from Table B markers B1-B29; more preferably, at least 10 markers selected from Table B markers B1-B29 more preferably, at least 15 markers selected from Table B markers B1-B29; more preferably, at least 20 markers selected from Table B markers B1-B29; more preferably, selected from the 29 markers in Table B markers B1-B29;
  • Cancer immunotherapy includes: any one or more of anti-PD1 drugs, anti-PDL1 drugs, anti-CTLA4 drugs, anti-TIM3 drugs, anti-BTLA drugs, anti-VISTA drugs or anti-LAG3 drugs combination therapy.
  • Example 7 Colorectal cancer typing device for detection of at least two markers
  • the equipment includes:
  • (P1) Input unit the input unit is used to input the mRNA of at least two markers in the sample in Table A or Table B of a certain subject, or A1-A23 in Table A or B1-B22 in Table B data on gene expression levels;
  • the data processing unit processes the data of the input mRNA gene expression level, and the data processing unit includes a normalization processing subunit, a similarity calculation subunit and a similarity difference calculation subunit;
  • the normalization processing subunit is used to normalize the gene expression values of at least two markers in Table A or Table B, or A1-A23 in Table A or B1-B22 in Table B ;
  • the similarity calculation subunit is used to calculate the normalized value of at least two markers in Table A or Table B, or A1-A23 in Table A or B1-B22 in Table B, and the at least two markers First similarity of mean gene expression values in immunotherapy drug responders; and calculating normalized values of at least two markers in Table A or Table B with the at least two markers in immunotherapy drug non-responders The second similarity of the mean gene expression values in ;
  • the similarity difference calculation subunit is used to calculate the difference between the first similarity and the second similarity of each marker gene
  • (P3) a typing unit, the typing unit types the test object based on the difference of each marker gene, and obtains that the test object is a responder or non-responder of a single immunotherapy drug, thereby obtaining a typing result;

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Abstract

涉及生物医药领域，涉及用于评估结直肠癌患者对免疫治疗药物响应性的标志物，该标志物能评估结直肠癌患者对免疫治疗药物的响应性和/或评价免疫治疗药物对结直肠癌症的治疗效果。

Description

用于评估结直肠癌患者对免疫治疗药物响应性的标志物 技术领域
本发明涉及生物医药领域，尤其涉及一种评估结直肠癌患者对免疫治疗药物响应性的标志物。
背景技术
目前，免疫检查点抑制剂在一些微卫星(MSI)不稳定结直肠癌患者中能产生持久响应，而Oliveira等人(2019)在Front.Oncol.9中报道仍有约60％的MSI结直肠癌患者对单一免疫检查点抑制剂治疗(例如抗-PD1)无响应，并且约40％的MSI结直肠癌患者对免疫检查点抑制剂联合治疗也无响应。抗药性的机制尚不清楚。在结直肠癌中，免疫热的MSI肿瘤/免疫冷的MSS肿瘤被广泛用作患者是否应接受免疫治疗的指标。因此，关于结直肠癌的研究很多都集中在MSI肿瘤与MSS肿瘤之间的对比上，虽然这些研究对这两种结直肠癌亚型之间的差异产生了见解，但并未解释微卫星结直肠肿瘤为何会对免疫检查点抑制剂的治疗产生抗药性。Havelet等人(2019)在Nat.Rev.Cancer19,133–150上以及Tian等人(2012)在JPathol上报道除了MSI/MSS状态外，其他的生物标志物，例如TMB、PDL1、POLE/POLD1变体或MSI类基因标志物也正在结直肠癌研究中使用。本质上来讲，PDL1可直接指示结直肠癌患者的肿瘤样本是否浸润性CD8+T细胞；而MSI/MSS状态、TMB、POLE/POLD1变体或MSI类基因标记则表征了肿瘤样本产生高新抗原水平的可能性，因此，新抗原水平能间接指示结直肠癌患者的肿瘤样本是否可能潜在地具有浸润性CD8+T细胞。
然而，根据Liet等人(2019)在Cell176,775-789.e18以及Sade-Feldman等人(2019)在Cell176,404和Thommen等人(2015)在Res.3,1344–1355上的报道，关于肺癌和黑色素瘤的抗-PD1响应的研究表明肿瘤浸润性CD8+T细胞的数目并不是判断抗-PD1治疗效果的唯一指标要求，还需要肿瘤浸润性CD8+T细胞的衰竭状态特征来显示抗-PD1响应状态。因此，无论生物标记(例如MSI/MSS状态、TMB、PDL1、POLE/POLD1变体和MSI类基因标记)在技术上如何稳定，这些生物标记仍无法完全解释抗-PD1的耐药性。
Sharma等人(2017)在Cell168,707-723中报道肿瘤至少需要满足两个特征才是抗-PD1治疗的响应者，第一，肿瘤应具有浸润性CD8+T细胞；第二，至少一个肿瘤浸润性CD8+T细胞的子集(不管是已经浸润在肿瘤中的CD8+T细胞直接重生或间接从周围部位聚集到肿瘤中的新CD8+T细胞)表现出对抗-PD1反应的特性。
对此，有必要进一步研究结直肠癌患者对免疫治疗药物的响应性，使免疫治疗药物更针对性地治疗结直肠癌患者。
发明内容
本发明所要解决的技术问题在于，提供用于评估结直肠癌患者对免疫治疗药物响应性的标志物，该标志物能评估结直肠癌患者对免疫治疗药物的响应性和/或评价免疫治疗药物对结直肠癌的治疗效果。
为了解决上述技术问题，本发明实施例提供了用于检测结直肠癌患者体内CD8+T细胞的标志物，所述标志物包括用于判断结直肠癌患者体内是否有浸润性CD8+T细胞的第一标志物，所述第一标志物包括基因CXCL13、LY6G6D、CXCL10、SRSF6、SAMD9L、TNFSF13B、RASGRP1、CAB39L、SET、EIF5A、ITCH、TRIM69、MCUB、TYMS、ZDHHC9、LYSMD2、ZCCHC2、BRD3、PSME2、PSME1、NR6A1、ATP5F1A、和NUTM2A-AS1中的至少两种；更优选地，所述标志物选自该23个基因中的至少5个基因；更优选地，所述生标志物选自该23个基因中的至少6个基因；更优选地，所述标志物选自该23个基因中的至少7个基因； 更优选地，所述标志物选自该23个基因中的至少8个基因；更优选地，所述标志物选自该23个基因中的至少9个基因；更优选地，所述标志物选自该23个基因中的至少10个基因；更优选地，所述标志物选自该23个基因中的至少15个基因；更优选地，所述标志物选自该23个基因中的至少20个基因；更优选地，所述标志物为该23个基因。
作为本发明所述标志物的优选实施方式，所述标志物为基因所述第一标志物为基因CXCL13、LY6G6D、CXCL10、SRSF6、SAMD9L、TNFSF13B、RASGRP1、CAB39L、SET、EIF5A、ITCH、TRIM69、MCUB、TYMS、ZDHHC9、LYSMD2、ZCCHC2、BRD3、PSME2、PSME1、NR6A1、ATP5F1A、NUTM2A-AS1、CCL5、GZMA、GBP1、STAT1和CXCL9中的至少两种；更优选地，所述标志物选自该28个基因中的至少5个基因；更优选地，所述标志物选自该28个基因中的至少6个基因；更优选地，所述标志物选自该28个基因中的至少7个基因；更优选地，所述标志物选自该28个基因中的至少8个基因；更优选地，所述标志物选自该28个基因中的至少9个基因；更优选地，所述标志物选自该28个基因中的至少10个基因；更优选地，所述标志物选自该28个基因中的至少15个基因；更优选地，所述标志物选自该28个基因中的至少20个基因；更优选地，所述标志物为该28个基因。
作为本发明所述标志物的优选实施方式，所述标志物还包括用于判断所述浸润性CD8+T细胞的衰竭模式的标志物。
作为本发明所述标志物的优选实施方式，所述第二标志物为基因C1QC、FCGR1B、C1QB、TFEC、CD2、FCER1G、KMO、APBB1IP、CD48、LAPTM5、CYBB、NCF1B、NR1H3、IFI30、WIPF1、SLAMF8、FAM78A、HCST、IL4I1、TNFSF14、LILRB3、CSF1中的至少两种。更优选地，所述标志物选自该22个基因中的至少5个基因；更优选地，所述标志物选自该22个基因中的至少6个基因；更优选地，所述标志物选自该22个基因中的至少7个基因；更优选地，所述标志物选自该22个基因中的至少8个基因；更优选地，所述标志物选自该22个基因中的至少9个基因；更优选地，所述标志物选自该22个基因中的至少10个基因；更优选地，所述标志物选自该22个基因中的至少15个基因；更优选地，所述标志物选自该22个基因中的至少20个基因；更优选地，所述标志物为该22个基因。
作为本发明所述标志物的优选实施方式，所述标志物为基因C1QC、FCGR1B、C1QB、TFEC、CD2、FCER1G、KMO、APBB1IP、CD48、LAPTM5、CYBB、NCF1B、NR1H3、IFI30、WIPF1、SLAMF8、FAM78A、HCST、IL4I1、TNFSF14、LILRB3、CSF1、PDCD1、CD84、IL21R、HAVCR2、FCGR1A、CCL5和CXCL9中的至少两种；更优选地，所述标志物选自该29个基因中的至少5个基因；更优选地，所述标志物选自该29个基因中的至少6个基因；更优选地，所述标志物选自该29个基因中的至少7个基因；更优选地，所述标志物选自该29个基因中的至少8个基因；更优选地，所述标志物选自该29个基因中的至少9个基因；更优选地，所述标志物选自该29个基因中的至少10个基因；更优选地，所述标志物选自该29个基因中的至少15个基因；更优选地，所述标志物选自该29个基因中的至少20个基因；更优选地，所述标志物为该29个基因。
作为本发明所述标志物的优选实施方式，所述免疫治疗药物为抗-PD1、抗-PDL1、抗-CTLA4、抗-TIM3、抗-BTLA、抗-VISTA和抗-LAG3中的一种。
本发明提供用于评估结直肠癌患者对免疫治疗药物响应性的试剂盒，所述试剂盒含有上述任一项所述的用 于评估结直肠癌患者CD8+T细胞浸润性的标志物的含量的检测试剂。
作为本发明所述标志物的优选实施方式，所述检测试剂包括用于检测所述基因的探针，或/和用于检测所述基因相应的mRNA、cDNA或/和蛋白质的含量的试剂。
作为本发明所述标志物的优选实施方式，所述检测试剂为所述基因编码的蛋白质的单克隆抗体。
本发明提供用于评估癌症患者对单一免疫治疗药物响应性的方法，包括以下步骤：
1)提供结直肠癌患者的生物学样本，所述样本选自血液样本、血清样本、分离自外周血的单个核细胞样本、组织样本和体液样本中的至少一种；
2)检测生物学样本中第一标志物含量，以判断结直肠癌患者体内是否有浸润性CD8+T细胞；以及
3)若步骤2)的判断结果为否，则结直肠癌患者对所述的单一免疫治疗药物无响应性，评估结束；
若步骤2)的判断结果为是，则需检测生物学样本中第二标志物含量，以判断所述浸润性CD8+T细胞的衰竭模式，
当衰竭模式为前体衰竭模式时，则判断结直肠癌患者是所述的单一免疫治疗药物的响应者；当衰竭模式是终末衰竭模式时，则判断结直肠癌患者是所述的单一免疫治疗药物的无响应者，该无响应的癌症患者需要组合免疫治疗药物治疗，例如需要考虑抗-PD1和其它针对肿瘤微环境药物的组合疗法，或更换其他单一免疫治疗药物治疗。
作为本发明所述标志物的优选实施方式，所述第一标志物为基因CXCL13、LY6G6D、CXCL10、SRSF6、SAMD9L、TNFSF13B、RASGRP1、CAB39L、SET、EIF5A、ITCH、TRIM69、MCUB、TYMS、ZDHHC9、LYSMD2、ZCCHC2、BRD3、PSME2、PSME1、NR6A1、ATP5F1A、NUTM2A-AS1中的至少两种；更优选地，所述第一标志物选自该23个基因中的至少5个基因；更优选地，所述第一标志物选自该23个基因中的至少6个基因；更优选地，所述第一标志物选自该23个基因中的至少7个基因；更优选地，所述第一标志物选自该23个基因中的至少8个基因；更优选地，所述第一标志物选自该23个基因中的至少9个基因；更优选地，所述第一标志物选自该23个基因中的至少10个基因；更优选地，所述第一标志物选自该23个基因中的至少15个基因；更优选地，所述第一标志物选自该23个基因中的至少20个基因；更优选地，所述第一标志物为该23个基因。
作为本发明所述标志物的优选实施方式，所述第一标志物为基因CXCL13、LY6G6D、CXCL10、SRSF6、SAMD9L、TNFSF13B、RASGRP1、CAB39L、SET、EIF5A、ITCH、TRIM69、MCUB、TYMS、ZDHHC9、LYSMD2、ZCCHC2、BRD3、PSME2、PSME1、NR6A1、ATP5F1A、NUTM2A-AS1、CCL5、GZMA、GBP1、STAT1和CXCL9中的至少两种；更优选地，所述第一标志物选自该28个基因中的至少5个基因；更优选地，第一标志物选自该28个基因中的至少6个基因；更优选地，所述第一标志物选自该28个基因中的至少7个基因；更优选地，所述第一标志物选自该28个基因中的至少8个基因；更优选地，所述第一标志物选自该28个基因中的至少9个基因；更优选地，所述第一标志物选自该28个基因中的至少10个基因；更优选地，所述第一标志物选自该28个基因中的至少15个基因；更优选地，所述第一标志物选自该28个基因中的至少20个基因；更优选地，所述第一标志物为该28个基因。
作为本发明所述标志物的优选实施方式，所述第二标志物为基因 C1QC、FCGR1B、C1QB、TFEC、CD2、FCER1G、KMO、APBB1IP、CD48、LAPTM5、CYBB、NCF1B、NR1H3、IFI30、WIPF1、SLAMF8、FAM78A、HCST、IL4I1、TNFSF14、LILRB3和CSF1中的至少两种；更优选地，所述第二标志物选自该22个基因中的至少5个基因；更优选地，所述第二标志物选自该22个基因中的至少6个基因；更优选地，所述第二标志物选自该22个基因中的至少7个基因；更优选地，所述第二标志物选自该22个基因中的至少8个基因；更优选地，所述第二标志物选自该22个基因中的至少9个基因；更优选地，所述第二标志物选自该22个基因中的至少10个基因；更优选地，所述第二标志物选自该22个基因中的至少15个基因；更优选地，所述第二标志物选自该22个基因中的至少20个基因；更优选地，所述第二标志物为该22个基因。
作为本发明所述标志物的优选实施方式，所述第二标志物为基因C1QC、FCGR1B、C1QB、TFEC、CD2、FCER1G、KMO、APBB1IP、CD48、LAPTM5、CYBB、NCF1B、NR1H3、IFI30、WIPF1、SLAMF8、FAM78A、HCST、IL4I1、TNFSF14、LILRB3、CSF1、PDCD1、CD84、IL21R、HAVCR2、FCGR1A、CCL5和CXCL9中的至少两种；更优选地，所述第二标志物选自该29个基因中的至少5个基因；更优选地，所述第二标志物选自该29个基因中的至少6个基因；更优选地，所述第二标志物选自该29个基因中的至少7个基因；更优选地，所述第二标志物选自该29个基因中的至少8个基因；更优选地，所述第二标志物选自该29个基因中的至少9个基因；更优选地，所述第二标志物选自该29个基因中的至少10个基因；更优选地，所述第二标志物选自该29个基因中的至少15个基因；更优选地，所述第二标志物选自该29个基因中的至少20个基因；更优选地，所述第二标志物为该29个基因。
作为本发明所述标志物的优选实施方式，免疫治疗药物为抗-PD1、抗-PDL1、抗-CTLA4、抗-TIM3、抗-BTLA、抗-VISTA和抗-LAG3中的一种。
作为本发明所述标志物的优选实施方式，所述结直肠癌患者处于结直肠癌I期、II期、III期或IV期。
本发明提供一种制备或筛选免疫治疗结直肠癌药物的方法，所述方法采用上述任一项所述的用于评估结直肠癌患者CD8+T细胞浸润性的标志物、上述所述的试剂盒或/和上述所述的用于评估癌患者对免疫治疗药物响应性的方法制备或筛选抗肿瘤药物。
实施本发明实施例，具有如下有益效果：
(1)本发明筛选出能用于判断结直肠癌患者对免疫治疗药物的响应性和/或评价免疫治疗药物对结直肠癌的治疗效果的标志物，为结直肠癌的治疗提供了新途径。
(2)本发明先检测第一标志物标志物来判断CDT+8细胞的是否有浸润性，继而检测第二标志来判断有浸润性的CDT+8细胞的衰竭模式，从而能更精确地判断结直肠癌患者对癌症治疗药物的响应性，利于更准确地对结直肠癌患者给药治疗。
附图说明
图1A是接受抗-PD1治疗的黑色素瘤患者(GSE78220)中的响应者的总存活数和无响应者的总存活数分别与时间的关系图；
图1B是接受抗-PD1治疗的黑色素瘤患者集合1(GSE91061)中响应者的总存活数和无响应者的总存活数分别与时间的关系图；
图1C是接受抗-PD1治疗的黑色素瘤患者集合2(GSE91061)响应者的总存活数和无响应者的总存活数分别 与时间的关系图；
图2A是MCP计数细胞毒性淋巴细胞与标记的CD8+T细胞浸润性评分的相关性图；
图2B是MCPcounter计数CD8+T细胞与标记的CD8+T细胞浸润性评分的相关性图；
图2C是TIDE计算细胞毒性T淋巴细胞(CTL)与标记的CD8+T细胞浸润性评分的相关性图；
图3是淋巴细胞脉络丛脑膜炎病毒(LCMV)响应评分和肿瘤的TME2.T细胞响应评分的数据图；
图4是454个样本的肿瘤细胞的TME1.T细胞浸润性评分和454个样本的肿瘤细胞的TME2.T细胞响应性评分的数据分布图。
具体实施方式
为使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明作进一步地详细描述。
实施例1预测肿瘤微环境(TMEPRE)的方法的设计
数据来源
从GEO数据库下载四个结直肠癌数据集的公开可用基因表数据和MSI/MSS状态，该四个结直肠癌数据集分别是GSE13294、GSE26682、GSE18088、GSE39084，且该四个结直肠癌数据集均来自Affymetrix Human Genome U133 Plus2.0 Array平台，并使用frma软件包(McCall等人2010)中的冷冻RMA(fRMA)方法进行归一化处理，然后使用ComBat(Johnson等人2007)去除四个数据集的样本批处理影响，共收集了454个样本的基因表达数据，其中，MSI样本为131个，MSI-L(微卫星低度不稳定)样本为23个，MSS样本为284，未知种类的样本为16。
TMEPRE模型的设计
TMEPRE模型有两个部分的评分，第一部分是CD8+T细胞是否有浸润性(以下简称TME1)的评分，第二部分是浸润性CD8+T细胞的衰竭模式(以下简称TME2)的评分。
关于TME1的评分，具体地：采用CD8A的表达水平来初步估计CD8+T细胞的丰度。CD8A表达水平的临界值定义为131个MSI肿瘤的CD8A表达水平的40％；其中，CD8A表达水平高于临界值的MSI肿瘤定义为具有浸润性CD8+T细胞的肿瘤(n＝78，n为样本数量,下同)；CD8A表达水平低于临界值的MSS肿瘤定义为无浸润性CD8+T细胞的肿瘤(n＝211)。在这些两组之间进行了200轮10折交叉验证。在每个交叉验证回合中，对每个基因t检验和p值排列。CD8A基因排除在交叉验证过程之外。在200轮交叉验证中，在至少有80％的交叉验证过程中p值排在前60的基因被选为TME1的标志物，其中，筛选出了28个基因作为TME1的标志物，详见表A:
Figure PCTCN2020103776-appb-000001
Figure PCTCN2020103776-appb-000002
关于TME2的评分，具体地：对有浸润性的CD8+T细胞的肿瘤微环境评分,从而判断细胞衰竭模式，进而判断结直肠癌患者对免疫治疗药物的响应性。
由于TIM3是所有共表达抑制受体中的一种早期获得的共表达的抑制剂受体，采用PD1和TIM3的多种抑制受体的共表达模式，以用于定义终末衰竭模式。(Thommen等人2015)在上述CD8+T细胞浸润高的MSI肿瘤中(n＝78)，PD1表达水平的中位数用作PD1的临界值，TIM3表达水平的中位数用作TIM3的临界值。浸润性CD8+T细胞且PD1和TIM3表达水平均高于临界值的MSI肿瘤定义为多种早期抑制受体的共表达的肿瘤微环境。存在这种肿瘤微环境的CD8+T细胞开始逐渐变为终末衰竭，并抵抗抗-PD1治疗(n＝21)。浸润性CD8+T细胞但PD1和TIM3表达水平均低于临界值的MSI肿瘤定义为CD8+T细胞仍可对抗-PD1治疗(n＝21)产生响应的肿瘤微环境。两组之间进行了200轮10折交叉验证。在每个交叉验证回合中，对每个基因t检验和p值排列。在200轮交叉验证中，在至少有80％的交叉验证过程中p值排在前60的基因被选择作为TME2的标志物，其中，筛选出了29个基因作为TME2的标志物，详见表B:
Figure PCTCN2020103776-appb-000003
Figure PCTCN2020103776-appb-000004
可见，若浸润性CD8+T细胞不是终末衰竭模式，则表示结直肠癌患者仍能对检查点抑制剂响应，若浸润性CD8+T细胞具有终末衰竭模式，则表示结直肠癌患者对检查点抑制剂无响应。
经过第一部分TME1评分和第二部分TME2评分，总结出评价结直肠癌患者对免疫治疗药物是否有响应性的判断条件：当第一部分TME1低分时(表示肿瘤无浸润性CD8+T细胞)，则可决定结直肠癌患者对单一免疫治疗药物无响应；当第一部分TME1高分时(表示肿瘤浸润性CD8+T细胞)，需进一步对TME2评分，若TME2高分(表示肿瘤高浸润CD8+T细胞不是终末衰竭模式)，则可决定结直肠癌患者对单一免疫治疗药物有响应性，若TME2低分(表示肿瘤高浸润CD8+T细胞是终末衰竭模式)，则可决定结直肠癌患者对单一免疫治疗药物无响应性。该无响应的癌症患者需要组合免疫治疗药物治疗，例如需要考虑抗-PD1和其它针对肿瘤微环境药物 的组合疗法，或更换其他单一免疫治疗药物治疗。
本实施例不但依据肿瘤具有CD8+T细胞浸润性，还依据肿瘤浸润CD8+T细胞的子集表现出对抗-PD1反应的特性来决定结直肠癌患者对免疫治疗药物是否有响应性。
实施例2检验标志物在癌症免疫治疗响应中的表现
以抗-PD1为例，从而检验抗-PD1响应的预测价值。本实施例的生物标志物是实施例1TMEPRE方法获得的生物标志物。
数据来源
从GEO数据库下载三个RNAseq数据集，三个数据集具体为：
第一个数据集包括标准化的RNAseq数据和接受派姆单抗(pembrolizumab)或纳武单抗(nivolumab)治疗的黑色素瘤患者预处理样本的临床数据，剔除接受了MAPK抑制剂的患者(n＝16，GSE78220)；
第二个数据集包括标准化RNAseq数据和接受纳武单抗的黑色素瘤患者样本的临床数据，分别分析第1周期第29天之前的早期治疗时间点的样本和第1周期第0天之前的预处理时间点的样本，剔除接受了先验的伊匹单抗(ipilimumab)治疗或总存活数据不完整的患者(n＝21，GSE91061)；
第三个数据集包括分离自肿瘤和慢性病毒感染的前体衰竭CD8+T细胞和终末衰竭CD8+T细胞的标准化RNAseq数据(n＝20，GSE122713)。
采用TMEPRE模型来预测抗-PD1的治疗响应
黑色素瘤是广泛用于验证CD8+T细胞和免疫治疗反应的模型肿瘤，本发明采用黑色素瘤作为模型以进行验证，TMEPRE模型在接受抗-PD1治疗的黑色素瘤患者的3个数据集上得到了验证，具体为：
第一个数据集中，图1A所示，TMEPRE模型的黑色素瘤患者存活分析得出了显著的风险比(n＝16，预处理样本，GSE78220，HR＝4.59，p值＝0.056)。第二个数据集中，图1B所示，尽管TMEPRE预测模型的黑色素瘤患者存活分析的p值很大(n＝21，在第1周期第0天之前采样，GSE91061，HR＝2.12，p值＝0.115)，但仍然能观察到TMEPRE预测的响应组与TMEPRE预测响应组之间的存活分离。
第三个数据集中，图1C所示，TMEPRE模型的黑色素瘤患者存活分析得出了显著的风险比(在早期治疗时间点的第1周期第29天之前采样，n＝21，GSE91061，HR＝5.04，p值＝0.003，图1C)。
此外，用于验证抗-PD1响应的数据(GSE78220和GSE91061)来自在IlluminaHiSeq平台上的黑素瘤患者；用于训练模型的数据(GSE13294、GSE26682、GSE18088、GSE39084)来自Affymetrix平台的结直肠癌患者。在模型训练中未使用抗-PD1响应数据或存活数据。尽管不同的癌症类型和不同的数据平台，TMEPRE模型显示了对抗-PD1治疗响应性的预测能力。
实施例3检验标志物与癌症免疫治疗响应性的CD8+T细胞的生物学原理或联系
在454个样本的数据集中，采用MCPcounter(MCP计数器)(Becht等人2016)计数和TIDE(Jiang等人2018)计算细胞毒性T淋巴细胞(CTL)来读取肿瘤浸润免疫细胞的计数。如图2A、图2B、图2C所示，TMEPRE模型的第一部分的TME1评分分别与MCPcounter计数CD8+T细胞、MCPcounter计数细胞毒性淋巴细胞和TIDE计算细胞毒性T淋巴细胞呈正相关。
另外，以实施例1获得的284个MSS样本和454个样本为基础，计算284个MSS样本的TME1评分与454个样本的TME1评分的比值ω (TME1)；计算284个MSS样本的MCPcounter计数细胞毒性淋巴细胞评分与454个样本的MCPcounter计数细胞毒性淋巴细胞评分的比值ω (MCPcounter细胞毒性淋巴细胞)；计算284个MSS样本的 MCPcounter计数CD8+T细胞评分与454个样本的MCPcounter计数CD8+T细胞评分比值ω (MCPcounter CD8+T细胞)；计算284个MSS样本的TIDE计算细胞毒性T淋巴细胞评分与454个样本的TIDE计算细胞毒性T淋巴细胞评分的比值ω (TIDE细胞毒性T淋巴细胞)，计算公式分别如下：
ω (TME1)＝(MSS样本最大评分值 (TME1)-MSS样本最小评分值 (TME1))/(全部样本最大评分值 (TME1)-全部样本最小评分值 (TME1))；
ω (MCPcounter细胞毒性淋巴细胞)＝(MSS样本最大评分值 (MCPcounter细胞毒性淋巴细胞)-MSS样本最小评分值 (MCPcounter细胞毒性淋巴细 胞))/(全部样本最大评分值 (MCPcounter细胞毒性淋巴细胞)-全部样本最小评分值 (MCPcounter细胞毒性淋巴细胞))；
ω (MCPcounter CD8+T细胞)＝(MSS样本最大评分值 (MCPcounter CD8+T细胞)-MSS样本最小评分值 (MCPcounter CD8+T细胞))/(全部样本最大评分值 (MCPcounter CD8+T细胞)-全部样本最小评分值 (MCPcounter CD8+T细胞))；
ω (TIDE细胞毒性T淋巴细胞)＝(MSS样本最大评分值 (TIDE细胞毒性T淋巴细胞)-MSS样本最小评分值 (TIDE细胞毒性T淋巴细胞))/(全部样本最大评分值 (TIDE细胞毒性T淋巴细胞)-全部样本最小评分值 (TIDE细胞毒性T淋巴细胞))；
经计算，得出ω (TME1)＝0.89，ω (MCPcounter细胞毒性淋巴细胞)＝0.67，ω (MCPcounter CD8+T细胞)＝0.53，ω (TIDE细胞毒性淋巴细胞)＝0.81，可见，TME1评分的比值ω最大。
另外，观察图2B可知，MCPcounter计数CD8+T细胞低分的MSS肿瘤组含有TME1评分的相对高的CD8+T细胞浸润，观察图2C可知，TIDE计算细胞毒性T淋巴细胞低分的MSS肿瘤组含有TME1评分的相对高的CD8+T细胞浸润，这些结果表明在具有较低肿瘤浸润免疫细胞的MSS结直肠肿瘤中，TME1能更灵敏地检测肿瘤浸润毒性淋巴细胞，这是因为TME1是针对结直肠癌的肿瘤微环境而设计的，而MCPcounter计数和TIDE算法却不是。
TMEPRE模型的TME2评分旨在检测肿瘤浸润性CD8+T细胞是否能对抗-PD1治疗产生响应。为测试TME2是否确实捕捉到了肿瘤浸润性CD8+T细胞的这一特征，读出了分离自肿瘤和慢性病毒感染的两组功能障碍的CD8+T细胞的TME2标记评分：终末衰竭肿瘤浸润CD8+T细胞不能再对抗-PD1治疗产生响应，而前体衰竭肿瘤浸润CD8+T细胞仍然能对抗PD-1治疗产生响应(GSE122713)。由于TME2标记来源于大块肿瘤样本的基因表达数据，基因表达的来源源自肿瘤微环境中CD8+T细胞、肿瘤细胞和其他肿瘤浸润性免疫细胞的混合，而前体/终末衰竭的肿瘤浸润CD8+T细胞数据来源于分离的CD8+T细胞。因此，当读出TME2评分时,只利用源自CD8+T细胞的基因。对于TME2的29个基因，利用BloodSpot和HemaExporer人血细胞生成数据库比较16种纯化的免疫细胞的中位表达值。当CD8+T细胞是表达一种基因的前两大免疫细胞类型时，则认为该基因主要由CD8+T细胞表达。TME2中有7个基因(CCL5、CD2、CD48、CD84、FAM78A、HCST、IL21R)通过这些标准，TME2中有2个基因(HAVCR2、PDCD1)是CD8+T细胞上的抑制剂受体，用于定义前体衰竭CD8+T细胞和终末衰竭CD8+T细胞。
这9个基因被用来读取分离的前体衰竭肿瘤浸润CD8+T细胞、终末衰竭肿瘤浸润CD8+T细胞的数据集的TME2评分。如图3所示，在肿瘤和慢性病毒感染中(例如，淋巴细胞脉络丛脑膜炎病毒)，前体衰竭肿瘤浸润CD8+T细胞亚群TME2评分明显更高(评分值 (肿瘤)<0.001，评分值 (病毒感染)＝0.048)。因此，TME2评分的确捕获了仍然能对抗-PD1作出响应的前体细胞衰竭的肿瘤浸润性CD8+T细胞的特征。
实施例4检验标志物在结直肠癌MSI/MSS样本中的表现
本实施例的生物标志物是实施例1方法获得的生物标志物。
在454个结直肠样本中读取了TMEPRE模型(MSI＝131，MSI-L＝23，MSS＝284，未知＝16)。图4所示，显示出足够的CD8+T细胞浸润模式而不具CD8+T细胞终末衰竭模式的肿瘤认为是抗-PD1治疗的潜在响应者。
284个MSS肿瘤样本中，有10.6％MSS肿瘤样本(n＝30)为有响应者，有89.4％MSS肿瘤样本(n＝254)为无响应者。MSS无响应者中，有86.6％MSS肿瘤样本(n＝246)显示没有足够肿瘤浸润的CD8+T细胞，2.8％MSS肿瘤样本(n＝8)显示有足够肿瘤浸润的CD8+T细胞而显示出终末衰竭的CD8+T细胞模式。可见，大多数MSS肿瘤的抗-PD1耐药机制是肿瘤浸润的CD8+T细胞数量不足。
131个MSI肿瘤样本中，有67.2％MSI肿瘤样本中(n＝88)为有响应者，32.8％MSI肿瘤样本中(n＝43)为无响应者。在MSI无响应者中，有16.0％MSI肿瘤样本中(n＝21)显示没有足够肿瘤浸润的CD8+T细胞，有16.8％MSI肿瘤样本中(n＝22)显示有足够肿瘤浸润的CD8+T细胞而显示出终末衰竭CD8+T细胞的模式。因此，约50％的MSI无响应者是由于肿瘤浸润的CD8+T细胞数量不足而引起的，其余50％的MSI无响应者是由肿瘤微环境中CD8+T细胞的终末衰竭引起的，这些无响应者需要考虑抗-PD1和其它针对肿瘤微环境药物的组合疗法。
TMEPRE模型得出了10.6％MSS结直肠癌患者的肿瘤与67.2％MSI结直肠癌患者的肿瘤表现出可以从抗-PD1治疗中受益的生物学特征，这些预测的MSS肿瘤响应者的百分比和MSI肿瘤响应者的百分比与报道的派姆单抗治疗治疗20周结直肠癌患者的免疫相关疾病控制率的益处一致。
综上，不建议仅通过评估MSI/MSS状态，就不给MSS结直肠癌患者施予抗-PD1治疗。临床数据表明，用派姆单抗治疗的转移性MSS结直肠癌的疾病控制率为11％，更进一步地，在新辅助治疗的最新临床试验中，依匹莫单抗+利福单抗治疗早期的治疗早期MSS结直肠癌的病理反应率为27％。这些结果表明，MSS结直肠癌人群中存在抗-PD1治疗的响应者。在上述分析中，约10.6％的MSS肿瘤样本显示高TME1分和高TME2分，这表明10.6％的MSS患者的肿瘤微环境的生物学特性仍然可能受益于抗PD1治疗。
实施例5诊断结直肠癌患者对免疫治疗药物响应性的方法
本实施例的方法主要包括以下步骤：
a)以肿瘤组织为检测样本，优选地，以与肿瘤相关的血液为样本，更优选地，以外周血的单个核细胞作为样本；
b)使用诊断产品检测样本中选自表A的标志物，当表A标志物低分时，则提示结直肠癌患者对免疫治疗药物无响应性，该结直肠癌患者不适合采用免疫治疗药物；当表A标志物高分时，继续检测表B标志物，若表B标志物高分时，则提示结直肠癌患者对免疫治疗药物有响应性，该结直肠癌患者适合采用免疫治疗药物，若表B标志物低分时，则提示结直肠癌患者对免疫治疗药物无响应性，该结直肠癌患者不适合采用单一免疫治疗药物，该结直肠癌患者需要组合免疫治疗药物治疗，例如需要考虑抗-PD1和其它针对肿瘤微环境药物的组合疗法，或采用另外的单一免疫治疗药物来治疗。
上述标志物的高分表现为基因表达上调或基因表达下调，响应性标志物的浓度C1比标准值C0高为基因表达上调，响应性标志物的浓度C1比标准值C0低为基因表达下调，C0为免疫治疗药物不能响应者人群中的免疫治疗药物响应性标志物的表达量。
实施例1中的表A和表B的标志物对癌症免疫药物治疗作出响应时的表现分别如下：
Figure PCTCN2020103776-appb-000005
Figure PCTCN2020103776-appb-000006
上述的标志物可以是基因、mRNA、cDNA和/或蛋白质。
其中，表A为实施例1表A所述的标志物，表B为实施例1表B所述的标志物。
当检测表A标志物时，本实施例检测选自表A标志物A1-A23中至少一个的标志物。
优选地，本实施例检测选自表A标志物A1-A23中的至少两个标志物，更优选地，选自表A标志物A1-A23中的至少5个的标志物；更优选地，选自表A标志物A1-A23中的至少6个的标志物；更优选地，选自表A标志物A1-A23中的至少7个的标志物；更优选地，选自表A标志物A1-A23中的至少8个的标志物；更优选地，选自表A标志物A1-A23中的至少9个以上的标志物；更优选地，选自表A标志物A1-A23中的至少10个的标志物；更优选地，选自表A标志物A1-A23中的至少15个的标志物；更优选地，选自表A标志物A1-A23中的至少20的标志物；更优选地，选自表A标志物A1-A23中的所有23个标志物。
更优选地，本实施例检测选自表A标志物A1-A28中的至少两个标志物，更优选地，选自表A标志物A1-A28中的至少5个的标志物；更优选地，选自表A标志物A1-A28中的至少6个的标志物；更优选地，选自表A标志物A1-A28中的至少7个的标志物；更优选地，选自表A标志物A1-A28中的至少8个的标志物；更优选地，选自表A标志物A1-A28中的至少9个以上的标志物；更优选地，选自表A标志物A1-A28中的至少10个的标志物；更优选地，选自表A标志物A1-A28中的至少15个的标志物；更优选地，选自表A标志物A1-A28中的至少20的标志物；更优选地，选自表A标志物A1-A28中的所有28个标志物。
当检测表B标志物时，本实施例检测选自表B标志物B1-B22中至少一个的标志物。
更优选地，优选地，本实施例检测选自表B标志物B1-B22中的至少两个标志物，更优选地，选自表B标志物B1-B22中的至少6标志物；更优选地，选自表B标志物B1-B22中的至少7个标志物；更优选地，选自表B标志物B1-B22中的至少8个标志物；更优选地，选自表B标志物B1-B22中的至少9个标志物；更优选地，选自表B标志物B1-B22中的至少10个标志物；更优选地，选自表B标志物B1-B22中的至少15个标志物；更优选地，选自表B标志物B1-B22中的至少20个标志物；更优选地，选自表B标志物B1-B22中该22标志物；
更优选地，当检测表B标志物时，本实施例检测选自表B标志物B1-B29中的至少两个标志物。更优选地，选自表B标志物B1-B29中的至少6标志物；更优选地，选自表B标志物B1-B29中的至少7个标志物；更优选地，选自表B标志物B1-B29中的至少8个标志物；更优选地，选自表B标志物B1-B29中的至少9个标志物；更优选地，选自表B标志物B1-B29中的至少10个标志物；更优选地，选自表B标志物B1-B29中的至少15个标志物；更优选地，选自表B标志物B1-B29中的至少20个标志物；更优选地，选自表B标志物B1-B29中该29标志物；
事实上，同时测量至少两种标志物能够更合适且可靠地评估结直肠癌患者对单一免疫治疗药物的响应性，本发明正是使用这样的成组的标记物而不仅是单一标志物。
实施例6关于检测至少两个标志物的结直肠癌患者分型方法
本实施例的结直肠癌分型的方法主要包括以下步骤：
(1)获得某一检测对象的表A中的A1-A28或A1-A23中至少两个标志物在所述样品中的mRNA基因表达水平，优选地，mRNA基因表达水平通过下组的技术获得：microarray，RNAseq，RT-PCR。
(2)归一化表A中的A1-A28或A1-A23中至少两个标志物的基因表达值，优选地，选自下组的方法进行归一化处理：fRMA，RMA，RNAseq CPM，RNAseq FPKM。
(3)分别获取多个响应者中的上述表A中的A1-A28或A1-A23至少两个标志物的基因表达值，然后计算所有响应者的相同标志物的平均基因表达值，其中，多个已知免疫治疗药物响应者的基因表达值可从临床医学数据库中获取；同理计算无浸润性CD8+T细胞的肿瘤无响应者的相同标志物的平均基因表达值，即，分别获取多个无浸润性CD8+T细胞的肿瘤无响应者中的上述表A中的A1-A28或A1-A23至少两个标志物的基因表达值，然后计算所有无浸润性CD8+T细胞的肿瘤无响应者的相同标志物的平均基因表达值，其中，多个已知免疫治疗药物无浸润性CD8+T细胞的肿瘤无响应者的基因表达值可从临床医学数据库中获取。
(3)计算待检测对象的上述表A中的A1-A28或A1-A23中的至少两个标志物归一化值与该至少两个个标志物在响应者中的平均基因表达值的第一相似度；以及计算待检测对象的上述表A中的A1-A28或表A1-A23中至少两个标志物归一化值与至少两个标志物在物无浸润性CD8+T细胞的肿瘤无响应者中的平均基因表达值的第二相似度，优选地，用选自下组的方法计算相似性：欧氏距离、曼哈顿距离、明可夫斯基距离、切比雪夫距离、雅卡德距离、皮尔逊相关性、余弦相关性或回归值。
(4)计算第一相似度与第二相似度的差值，当差值为负值，表明对象为免疫治疗药物的无响应者；当差值为正值，继续以下步骤：
(5)获得上述检测对象的表B中的B1-B29或B1-B22中至少两个标志物在所述样品中的mRNA基因表达水平，优选地，mRNA基因表达水平用通过下组的技术获得：microarray，RNAseq，RT-PCR。
(6)归一化表B中的B1-B29或B1-B22中至少两个标志物的基因表达值，优选地，选自下组的方法进行归一化处理：fRMA、RMA、RNAseq CPM、RNAseq FPKM。
(7)分别获取多个响应者中的上述表B中的B1-B29或B1-B22至少两个标志物的基因表达值，然后计算所有有前体衰竭肿瘤浸润CD8+T细胞响应者的相同标志物的平均基因表达值，其中，多个已知免疫治疗药物有前体衰竭肿瘤浸润CD8+T细胞响应者的基因表达值可从临床医学数据库中获取；同理计算有终末衰竭肿瘤浸润CD8+T细胞无响应者的相同标志物的平均基因表达值，即，分别获取多个有终末衰竭肿瘤浸润CD8+T细胞无响应者中的上述表B中的B1-B29或B1-B22至少两个标志物的基因表达值，然后计算所有有终末衰竭肿瘤浸润CD8+T细胞无响应者的相同标志物的平均基因表达值，其中，多个已知免疫治疗药物有终末衰竭肿瘤浸润CD8+T细胞无响应者的基因表达值可从临床医学数据库中获取。
(8)计算待检测对象表B中的B1-B29或B1-B22中所述至少两个标志物的归一化值与该至少两个标志物在有前体衰竭肿瘤浸润CD8+T细胞响应者中的平均基因表达值的第一相似度；以及计算待检测对象表B中的B1-B29或B1-B22中所述至少两个标志物的归一化与该至少两个标志物在有终末衰竭肿瘤浸润CD8+T细胞无响应者中的平均基因表达值的第二相似度；优选地，用选自下组的方法计算相似性：欧氏距离、曼哈顿距离、明可夫斯基距离、切比雪夫距离、雅卡德距离、皮尔逊相关性、余弦相关性或回归值；
(9)计算第一相似度与第二相似度的差值，当差值为负值，表明对象为单一免疫治疗药物的无响应者，需考虑采用组合免疫药物对无响应者进行结直肠癌治疗；当差值为正值，表明对象为免疫治疗药物的响应者，可采用单一免疫治疗药物对分型为免疫治疗药物响应者进行结直肠癌治疗。
本实施例适用于检测两个以上的标志物，但是并不限制本发明检测一个标志物。
优选地，本实施例检测选自表A标志物A1-A23中的至少两个标志物，更优选地，选自表A标志物A1-A23中的至少5个的标志物；更优选地，选自表A标志物A1-A23中的至少6个的标志物；更优选地，选自表A标志物A1-A23中的至少7个的标志物；更优选地，选自表A标志物A1-A23中的至少8个的标志物；更优选地，选自表A标志物A1-A23中的至少9个以上的标志物；更优选地，选自表A标志物A1-A23中的至少10个的标志物；更优选地，选自表A标志物A1-A23中的至少15个的标志物；更优选地，选自表A标志物A1-A23中的至少20的标志物；更优选地，选自表A标志物A1-A23中的所有23个标志物。
更优选地，本实施例检测选自表A标志物A1-A28中的至少两个标志物，更优选地，选自表A标志物A1-A28中的至少5个的标志物；更优选地，选自表A标志物A1-A28中的至少6个的标志物；更优选地，选自表A标志物A1-A28中的至少7个的标志物；更优选地，选自表A标志物A1-A28中的至少8个的标志物；更优选地，选自表A标志物A1-A28中的至少9个以上的标志物；更优选地，选自表A标志物A1-A28中的至少10个的标志物；更优选地，选自表A标志物A1-A28中的至少15个的标志物；更优选地，选自表A标志物A1-A28中的至少20的标志物；更优选地，选自表A标志物A1-A28中的所有28个标志物。
当检测表B标志物时，本实施例检测选自表B标志物B1-B22中的至少两个标志物，更优选地，选自表B标志物B1-B22中的至少6标志物；更优选地，选自表B标志物B1-B22中的至少7个标志物；更优选地，选自表B标志物B1-B22中的至少8个标志物；更优选地，选自表B标志物B1-B22中的至少9个标志物；更优选地，选自表B标志物B1-B22中的至少10个标志物；更优选地，选自表B标志物B1-B22中的至少15个标志物；更优选地，选自表B标志物B1-B22中的至少20个标志物；更优选地，选自表B标志物B1-B22中该22标志物；
更优选地，当检测表B标志物时，本实施例检测选自表B标志物B1-B29中的至少两个标志物。更优选地，选自表B标志物B1-B29中的至少6标志物；更优选地，选自表B标志物B1-B29中的至少7个标志物；更优选地，选自表B标志物B1-B29中的至少8个标志物；更优选地，选自表B标志物B1-B29中的至少9个标志物；更优选地，选自表B标志物B1-B29中的至少10个标志物；更优选地，选自表B标志物B1-B29中的至少15个标志物；更优选地，选自表B标志物B1-B29中的至少20个标志物；更优选地，选自表B标志物B1-B29中该29标志物；
癌症免疫药物治疗包括：anti-PD1药物、anti-PDL1药物、anti-CTLA4药物、anti-TIM3药物、anti-BTLA药物、anti-VISTA药物或anti-LAG3药物中的任一种或两种以上药物组合的治疗。
实施例7用于检测至少两个标志物的结直肠癌分型设备
该设备包括：
(P1)输入单元，输入单元用于输入某一对象的表A或表B中，或者表A中的A1-A23或表B中的B1-B22中，至少两个标志物在样品中的mRNA基因表达水平的数据；
(P2)数据处理单元，数据处理单元对输入的mRNA基因表达水平的数据进行处理，并且数据处理单元包括归一化处理子单元、相似度计算子单元和相似度差值计算子单元；
其中，归一化处理子单元用于对表A或表B中，或者表A中的A1-A23或表B中的B1-B22中，至少两个标志物的基因表达值进行归一化处理；
相似度计算子单元用于计算表A或表B中，或者表A中的A1-A23或表B中的B1-B22中，至少两个标 志物的归一化值与该至少两个标志物在免疫治疗药物响应者中的平均基因表达值的第一相似度；以及计算表A或表B中至少两个标志物的归一化值与该至少两个标志物在免疫治疗药物无响应者中的平均基因表达值的第二相似度；
相似度差值计算子单元用于计算各标志物基因的第一相似度与第二相似度的差值；
(P3)分型单元，分型单元基于各标志物基因的差值，对检测对象进行分型，得到检测对象为单一免疫治疗药物的响应者或无响应者，从而获得分型结果；和
(P4)输出设备，输出设备用于输出分型结果。
以上所揭露的仅为本发明一种较佳实施例而已，当然不能以此来限定本发明之权利范围，因此依本发明权利要求所作的等同变化，仍属本发明所涵盖的范围。

Claims (16)

  1. 用于检测结直肠癌患者体内CD8+T细胞的标志物，其特征在于，所述标志物包括用于判断结直肠癌患者体内是否有浸润性CD8+T细胞的第一标志物，所述第一标志物包括基因CXCL13、LY6G6D、CXCL10、SRSF6、SAMD9L、TNFSF13B、RASGRP1、CAB39L、SET、EIF5A、ITCH、TRIM69、MCUB、TYMS、ZDHHC9、LYSMD2、ZCCHC2、BRD3、PSME2、PSME1、NR6A1、ATP5F1A、和NUTM2A-AS1中的至少两种。
  2. 根据权利要求1所述的标志物，其特征在于，所述第一标志物为基因CXCL13、LY6G6D、CXCL10、SRSF6、SAMD9L、TNFSF13B、RASGRP1、CAB39L、SET、EIF5A、ITCH、TRIM69、MCUB、TYMS、ZDHHC9、LYSMD2、ZCCHC2、BRD3、PSME2、PSME1、NR6A1、ATP5F1A、NUTM2A-AS1、CCL5、GZMA、GBP1、STAT1和CXCL9中的至少两种。
  3. 根据权利要求1所述的标志物，其特征在于，所述标志物还包括用于判断所述浸润性CD8+T细胞的衰竭模式的第二标志物。
  4. 根据权利要求3所述的标志物，其特征在于，所述第二标志物为基因C1QC、FCGR1B、C1QB、TFEC、CD2、FCER1G、KMO、APBB1IP、CD48、LAPTM5、CYBB、NCF1B、NR1H3、IFI30、WIPF1、SLAMF8、FAM78A、HCST、IL4I1、TNFSF14、LILRB3、CSF1中的至少两种。
  5. 根据权利要求3所述的标志物，其特征在于，所述第二标志物为基因C1QC、FCGR1B、C1QB、TFEC、CD2、FCER1G、KMO、APBB1IP、CD48、LAPTM5、CYBB、NCF1B、NR1H3、IFI30、WIPF1、SLAMF8、FAM78A、HCST、IL4I1、TNFSF14、LILRB3、CSF1、PDCD1、CD84、IL21R、HAVCR2、FCGR1A、CCL5和CXCL9中的至少两种。
  6. 根据权利要求1～5任一项所述的标志物，其特征在于，所述免疫治疗药物为抗-PD1、抗-PDL1、抗-CTLA4、抗-TIM3、抗-BTLA、抗-VISTA和抗-LAG3中的一种。
  7. 用于评估结直肠癌患者对免疫治疗药物响应性的试剂盒，其特征在于，所述试剂盒含有如权利要求1～6任一项所述的标志物的含量的检测试剂。
  8. 根据权利要求7所述的试剂盒，其特征在于：所述检测试剂包括用于检测所述基因的探针，或/和用于检测所述基因相应的mRNA、cDNA或/和蛋白质的含量的试剂。
  9. 用于评估结直肠癌患者对单一免疫治疗药物响应性的方法，其特征在于，包括以下步骤：
    1)提供结直肠癌患者的生物学样本，所述样本选自血液样本、血清样本、分离自外周血的单个核细胞样本、组织样本和体液样本中的至少一种；
    2)检测生物学样本中第一标志物含量，以判断结直肠癌患者体内是否有浸润性CD8+T细胞；以及
    3)若步骤2)的判断结果为否，则结直肠癌患者对所述的单一免疫治疗药物无响应性，评估结束；
    若步骤2)的判断结果为是，则需检测生物学样本中第二标志物含量，以判断所述浸润性CD8+T细胞的衰竭模式，
    当衰竭模式为前体衰竭模式时，则判断结直肠癌患者是所述的单一免疫治疗药物的响应者；当衰竭模式是终末衰竭模式时，则判断结直肠癌患者是所述的单一免疫治疗药物的无响应者。
  10. 根据权利要求8所述的方法，其特征在于，所述第一标志物为基因CXCL13、LY6G6D、CXCL10、SRSF6、SAMD9L、TNFSF13B、RASGRP1、CAB39L、SET、EIF5A、ITCH、TRIM69、MCUB、TYMS、ZDHHC9、LYSMD2、ZCCHC2、BRD3、PSME2、PSME1、NR6A1、ATP5F1A、NUTM2A-AS1中的至少两种。
  11. 根据权利要求8所述的方法，其特征在于，所述第一标志物为基因CXCL13、LY6G6D、CXCL10、SRSF6、SAMD9L、TNFSF13B、RASGRP1、CAB39L、SET、EIF5A、ITCH、TRIM69、MCUB、TYMS、ZDHHC9、LYSMD2、ZCCHC2、BRD3、PSME2、PSME1、NR6A1、ATP5F1A、NUTM2A-AS1、CCL5、GZMA、GBP1、STAT1和CXCL9中的至少两种。
  12. 根据权利要求8所述的方法，其特征在于，所述第二标志物为基因C1QC、FCGR1B、C1QB、TFEC、CD2、FCER1G、KMO、APBB1IP、CD48、LAPTM5、CYBB、NCF1B、NR1H3、IFI30、WIPF1、SLAMF8、FAM78A、HCST、IL4I1、TNFSF14、LILRB3、CSF1中的至少两种。
  13. 根据权利要求8所述的方法，其特征在于，所述第二标志物为基因C1QC、FCGR1B、C1QB、TFEC、CD2、FCER1G、KMO、APBB1IP、CD48、LAPTM5、CYBB、NCF1B、NR1H3、IFI30、WIPF1、SLAMF8、FAM78A、HCST、IL4I1、TNFSF14、LILRB3、CSF1、PDCD1、CD84、IL21R、HAVCR2、FCGR1A、CCL5和CXCL9中的至少两种。
  14. 根据权利要求8所述的方法，其特征在于，所述免疫治疗药物为抗-PD1、抗-PDL1、抗-CTLA4、抗-TIM3、抗-BTLA、抗-VISTA和抗-LAG3中的一种。
  15. 根据权利要求8所述的方法，其特征在于，所述结直肠癌患者处于结直肠癌I期、II期、III期或IV期。
  16. 一种制备或筛选免疫治疗结直肠癌药物的方法，其特征在于，所述方法采用权利要求1～6的标志物、权利要求7～8的试剂盒或/和权利要求9～14的方法制备或筛选抗肿瘤药物。
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