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WO2022016447A1 - Marqueur pour évaluer la réactivité de patients atteints d'un cancer colorectal à un médicament immunothérapeutique - Google Patents

Marqueur pour évaluer la réactivité de patients atteints d'un cancer colorectal à un médicament immunothérapeutique 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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孙恬
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Carbon Logic Biotech Hk Ltd
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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

Definitions

  • 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

La présente invention concerne le domaine de la biomédecine et concerne un marqueur pour évaluer la réactivité de patients atteints d'un cancer colorectal à un médicament immunothérapeutique. Le marqueur peut évaluer la réactivité de patients atteints d'un cancer colorectal à un médicament immunothérapeutique et/ou évaluer les effets thérapeutiques d'un médicament immunothérapeutique sur le cancer colorectal.
PCT/CN2020/103776 2020-07-23 2020-07-23 Marqueur pour évaluer la réactivité de patients atteints d'un cancer colorectal à un médicament immunothérapeutique Ceased WO2022016447A1 (fr)

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