CN113969315A - Marker for assessing responsiveness of colorectal cancer patient to immunotherapeutic drug - Google Patents
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Abstract
The present invention relates to the field of biomedicine, and in particular to a marker for assessing the responsiveness of a colorectal cancer patient to an immunotherapeutic drug, which marker is capable of assessing the responsiveness of a colorectal cancer patient to an immunotherapeutic drug and/or assessing the therapeutic effect of an immunotherapeutic drug on colorectal cancer.
Description
Technical Field
The invention relates to the field of biomedicine, in particular to a marker for evaluating the responsiveness of a colorectal cancer patient to an immunotherapy medicament.
Background
Currently, immune checkpoint inhibitors produce a persistent response in some Microsatellite (MSI) unstable colorectal cancer patients, while Oliveira et al (2019) report in front. The mechanism of resistance is not clear. In colorectal cancer, immunohot MSI tumors/immunocold MSS tumors are widely used as indicators of whether a patient should receive immunotherapy. Thus, much of the research on colorectal cancer has focused on the comparison between MSI tumors and MSS tumors, and although these studies have generated insight into the differences between these two colorectal cancer subtypes, it has not been explained why microsatellite colorectal tumors would be resistant to treatment with immune checkpoint inhibitors. In addition to MSI/MSS status, other biomarkers, such as TMB, PDL1, POLE/POLD1 variants or MSI-like genetic markers are also being used in colorectal cancer studies, as reported by Havelet et al (2019) at Nat.Rev.cancer19, 133-150 and by Tian et al (2012) at JPathol. Essentially, PDL1 may directly indicate whether a tumor sample from a colorectal cancer patient infiltrates CD8+ T cells; whereas MSI/MSS status, TMB, POLE/POLD1 variants or MSI class gene markers characterize the likelihood that a tumor sample will produce high neoantigen levels, therefore, neoantigen levels indirectly indicate whether a tumor sample from a colorectal cancer patient is likely to have potentially infiltrating CD8+ T cells.
However, according to the reports by Liet et al (2019) on Cell176,775-789.e18 and by Sade-Feldman et al (2019) on Cell176,404 and Thommen et al (2015) on Res.3, 1344-1355, studies on anti-PD1 responses in lung and melanoma indicate that the number of tumor-infiltrating CD8+ T cells is not the only indicator requirement for judging the therapeutic effect of anti-PD1, and the failure status characteristics of tumor-infiltrating CD8+ T cells are also required to show the anti-PD1 response status. Thus, regardless of how stable technically biomarkers (e.g., MSI/MSS status, TMB, PDL1, POLE/POLD1 variant, and MSI-like genetic markers) are, these biomarkers still do not fully account for resistance to anti-PD 1.
Sharma et al (2017) reported in cell168,707-723 that at least two characteristics of a tumor need to be met in a responder to anti-PD1 treatment, first, the tumor should have infiltrating CD8+ T cells; second, at least a subset of tumor-infiltrating CD8+ T cells (whether new CD8+ T cells that have been directly regenerated from CD8+ T cells already infiltrating the tumor or indirectly aggregated into the tumor from a surrounding site) exhibit the property of responding to anti-PD 1.
In this regard, there is a need to further study the responsiveness of colorectal cancer patients to immunotherapeutic drugs to make them more targeted for treatment of colorectal cancer patients.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a marker for assessing responsiveness of a colorectal cancer patient to an immunotherapeutic drug, which marker is capable of assessing responsiveness of a colorectal cancer patient to an immunotherapeutic drug and/or assessing the therapeutic effect of an immunotherapeutic drug on colorectal cancer.
In order to solve the above technical problems, embodiments of the present invention provide markers for detecting CD8+ T cells in a colorectal cancer patient, the markers including a first marker for determining whether there are infiltrating CD8+ T cells in the colorectal cancer patient, the first marker including at least two of genes CXCL13, LY6G6D, CXCL10, SRSF6, SAMD9L, TNFSF13B, RASGRP1, CAB39L, SET, EIF5A, ITCH, TRIM69, MCUB, TYMS, ZDHHC9, LYSMD2, zchc 2, BRD3, PSME2, PSME1, NR6a1, ATP5F1A, and nut 2A-AS 1; more preferably, the marker is selected from at least 5 genes of the 23 genes; more preferably, the biomarker is selected from at least 6 genes of the 23 genes; more preferably, the marker is selected from at least 7 genes of the 23 genes; more preferably, the marker is selected from at least 8 genes of the 23 genes; more preferably, the marker is selected from at least 9 genes of the 23 genes; more preferably, the marker is selected from at least 10 genes of the 23 genes; more preferably, the marker is selected from at least 15 genes of the 23 genes; more preferably, the marker is selected from at least 20 genes of the 23 genes; more preferably, the markers are the 23 genes.
AS a preferred embodiment of the marker of the present invention, the marker is a gene and the first marker is at least two of CXCL13, LY6G6D, CXCL10, SRSF6, SAMD9L, TNFSF13B, RASGRP1, CAB39L, SET, EIF5A, ITCH, TRIM69, MCUB, TYMS, ZDHHC9, LYSMD2, zchc 2, BRD3, PSME2, PSME1, NR6a1, ATP5F1A, nut 2A-AS1, CCL5, GZMA, GBP1, STAT1 and CXCL 9; more preferably, the marker is selected from at least 5 genes of the 28 genes; more preferably, the marker is selected from at least 6 genes of the 28 genes; more preferably, the marker is selected from at least 7 genes of the 28 genes; more preferably, the marker is selected from at least 8 genes of the 28 genes; more preferably, the marker is selected from at least 9 genes of the 28 genes; more preferably, the marker is selected from at least 10 genes of the 28 genes; more preferably, the marker is selected from at least 15 genes of the 28 genes; more preferably, the marker is selected from at least 20 genes of the 28 genes; more preferably, the markers are the 28 genes.
As a preferred embodiment of the marker of the present invention, the marker further comprises a marker for determining the failure mode of the infiltrating CD8+ T cells.
As preferred embodiments of the marker of the present invention, the second marker is at least two of genes C1QC, FCGR1B, C1QB, TFEC, CD2, FCER1G, KMO, APBB1IP, CD48, LAPTM5, CYBB, NCF1B, NR1H3, IFI30, WIPF1, SLAMF8, FAM78A, HCST, IL4I1, TNFSF14, LILRB3, and CSF 1. More preferably, the marker is selected from at least 5 genes of the 22 genes; more preferably, the marker is selected from at least 6 genes of the 22 genes; more preferably, the marker is selected from at least 7 genes of the 22 genes; more preferably, the marker is selected from at least 8 genes of the 22 genes; more preferably, the marker is selected from at least 9 genes of the 22 genes; more preferably, the marker is selected from at least 10 genes of the 22 genes; more preferably, the marker is selected from at least 15 genes of the 22 genes; more preferably, the marker is selected from at least 20 genes of the 22 genes; more preferably, the markers are the 22 genes.
As preferred embodiments of the marker of the present invention, the marker is at least two of genes C1QC, FCGR1B, C1QB, TFEC, CD2, FCER1G, KMO, APBB1IP, CD48, lamm 5, CYBB, NCF1B, NR1H3, IFI30, WIPF1, SLAMF8, FAM78A, HCST, IL4I1, TNFSF14, LILRB3, CSF1, PDCD1, CD84, IL 63 21R, HAVCR2, fc1A, CCL5, and CXCL 9; more preferably, the marker is selected from at least 5 genes of the 29 genes; more preferably, the marker is selected from at least 6 genes of the 29 genes; more preferably, the marker is selected from at least 7 genes of the 29 genes; more preferably, the marker is selected from at least 8 genes out of the 29 genes; more preferably, the marker is selected from at least 9 genes of the 29 genes; more preferably, the marker is selected from at least 10 genes of the 29 genes; more preferably, the marker is selected from at least 15 genes of the 29 genes; more preferably, the marker is selected from at least 20 genes of the 29 genes; more preferably, the markers are the 29 genes.
As a preferred embodiment of the marker of the present invention, the immunotherapeutic drug is one of anti-PD1, anti-PDL1, anti-CTLA4, anti-TIM3, anti-BTLA, anti-VISTA and anti-LAG 3.
The invention provides a kit for evaluating the responsiveness of a colorectal cancer patient to an immunotherapy drug, which comprises a detection reagent of any one of the contents of the markers for evaluating the CD8+ T cell infiltration of the colorectal cancer patient.
As a preferred embodiment of the marker of the present invention, the detection reagent comprises a probe for detecting the gene, or/and a reagent for detecting the content of mRNA, cDNA, or/and protein corresponding to the gene.
In a preferred embodiment of the marker of the present invention, the detection reagent is a monoclonal antibody to a protein encoded by the gene.
The present invention provides a method for assessing responsiveness of a cancer patient to a single immunotherapeutic drug, comprising the steps of:
1) providing a biological sample of a colorectal cancer patient, the sample selected from at least one of a blood sample, a serum sample, a mononuclear cell sample isolated from peripheral blood, a tissue sample, and a body fluid sample;
2) detecting the content of the first marker in the biological sample to judge whether the colorectal cancer patient has invasive CD8+ T cells; and
3) if the judgment result in the step 2) is negative, the colorectal cancer patient has no responsiveness to the single immunotherapy medicament, and the evaluation is finished;
if the determination result in the step 2) is yes, detecting the content of the second marker in the biological sample to determine the exhaustion pattern of the infiltrating CD8+ T cells,
when the failure mode is a precursor failure mode, judging that the colorectal cancer patient is a responder of the single immunotherapy drug; when the failure mode is terminal failure mode, the colorectal cancer patient is judged to be a non-responder to the single immunotherapeutic drug, and the non-responder cancer patient needs to be treated with a combination immunotherapeutic drug, for example, a combination therapy that considers anti-PD1 and other drugs directed against the tumor microenvironment, or to be replaced with other single immunotherapeutic drug.
AS preferred embodiments of the marker of the present invention, the first marker is at least two of genes CXCL13, LY6G6D, CXCL10, SRSF6, SAMD9L, TNFSF13B, RASGRP1, CAB39L, SET, EIF5A, ITCH, TRIM69, MCUB, TYMS, ZDHHC9, lysdm 2, zchc 2, BRD3, PSME2, PSME1, NR6a1, ATP5F1A, nut 2A-AS 1; more preferably, the first marker is selected from at least 5 genes of the 23 genes; more preferably, the first marker is selected from at least 6 genes of the 23 genes; more preferably, the first marker is selected from at least 7 genes of the 23 genes; more preferably, the first marker is selected from at least 8 genes of the 23 genes; more preferably, the first marker is selected from at least 9 genes of the 23 genes; more preferably, the first marker is selected from at least 10 genes of the 23 genes; more preferably, the first marker is selected from at least 15 genes of the 23 genes; more preferably, the first marker is selected from at least 20 genes of the 23 genes; more preferably, the first marker is the 23 genes.
AS preferred embodiments of the marker of the present invention, the first marker is at least two of genes CXCL13, LY6G6D, CXCL10, SRSF6, SAMD9L, TNFSF13B, RASGRP1, CAB39L, SET, EIF5A, ITCH, TRIM69, MCUB, TYMS, ZDHHC9, lysdm 2, zchc 2, BRD3, PSME2, PSME1, NR6a1, ATP5F1A, nut 2A-AS1, CCL5, GZMA, GBP1, STAT1, and CXCL 9; more preferably, said first marker is selected from at least 5 genes of the 28 genes; more preferably, the first marker is selected from at least 6 genes of the 28 genes; more preferably, said first marker is selected from at least 7 genes of the 28 genes; more preferably, said first marker is selected from at least 8 genes of the 28 genes; more preferably, said first marker is selected from at least 9 genes of the 28 genes; more preferably, said first marker is selected from at least 10 genes of the 28 genes; more preferably, said first marker is selected from at least 15 genes of the 28 genes; more preferably, said first marker is selected from at least 20 genes of the 28 genes; more preferably, the first marker is the 28 genes.
As preferred embodiments of the marker of the present invention, the second marker is at least two of genes C1QC, FCGR1B, C1QB, TFEC, CD2, FCER1G, KMO, APBB1IP, CD48, lamtm 5, CYBB, NCF1B, NR1H3, IFI30, WIPF1, SLAMF8, FAM78A, HCST, IL4I1, TNFSF14, LILRB3 and CSF 1; more preferably, said second marker is selected from at least 5 genes of the 22 genes; more preferably, said second marker is selected from at least 6 genes of the 22 genes; more preferably, said second marker is selected from at least 7 genes of the 22 genes; more preferably, said second marker is selected from at least 8 genes out of the 22 genes; more preferably, said second marker is selected from at least 9 genes of the 22 genes; more preferably, said second marker is selected from at least 10 genes of the 22 genes; more preferably, said second marker is selected from at least 15 genes of the 22 genes; more preferably, said second marker is selected from at least 20 genes of the 22 genes; more preferably, the second marker is the 22 genes.
As preferred embodiments of the marker of the present invention, the second marker is at least two of genes C1QC, FCGR1B, C1QB, TFEC, CD2, FCER1G, KMO, APBB1IP, CD48, lamtm 5, CYBB, NCF1B, NR1H3, IFI30, WIPF1, SLAMF8, FAM78A, HCST, IL4I1, TNFSF14, LILRB3, CSF1, PDCD1, CD84, IL21R, HAVCR2, FCGR1A, CCL5 and CXCL 9; more preferably, said second marker is selected from at least 5 genes of the 29 genes; more preferably, said second marker is selected from at least 6 genes of the 29 genes; more preferably, said second marker is selected from at least 7 genes of the 29 genes; more preferably, said second marker is selected from at least 8 genes out of the 29 genes; more preferably, said second marker is selected from at least 9 genes out of the 29 genes; more preferably, said second marker is selected from at least 10 genes out of the 29 genes; more preferably, said second marker is selected from at least 15 genes of the 29 genes; more preferably, said second marker is selected from at least 20 genes out of the 29 genes; more preferably, the second marker is the 29 genes.
As a preferred embodiment of the marker of the present invention, the immunotherapeutic agent is one of anti-PD1, anti-PDL1, anti-CTLA4, anti-TIM3, anti-BTLA, anti-VISTA and anti-LAG 3.
As a preferred embodiment of the marker of the present invention, said colorectal cancer patient is at stage I, II, III or IV of colorectal cancer.
The invention provides a method for preparing or screening an immunotherapy colorectal cancer drug, which adopts any one of the marker for evaluating the CD8+ T cell infiltration of a colorectal cancer patient, the kit and/or the method for evaluating the responsiveness of the cancer patient to the immunotherapy drug to prepare or screen an anti-tumor drug.
The embodiment of the invention has the following beneficial effects:
(1) the invention screens out the marker which can be used for judging the responsiveness of the colorectal cancer patient to the immunotherapy medicament and/or evaluating the treatment effect of the immunotherapy medicament on the colorectal cancer, and provides a new way for the treatment of the colorectal cancer.
(2) According to the invention, the first marker is detected to judge whether the CDT +8 cells have infiltrative property, and then the second marker is detected to judge the failure mode of the infiltrative CDT +8 cells, so that the responsiveness of the colorectal cancer patient to the cancer treatment drug can be judged more accurately, and the colorectal cancer patient can be treated by administration more accurately.
Drawings
Figure 1A is a graph of overall survival of responders and non-responders, respectively, versus time in melanoma patients (GSE78220) receiving anti-PD1 treatment;
fig. 1B is a graph of the overall survival of responders and non-responders, respectively, versus time in melanoma patient pool 1(GSE91061) treated with anti-PD 1;
fig. 1C is a graph of overall survival of a melanoma patient set 2(GSE91061) responder and an overall survival of a non-responder, respectively, treated with anti-PD1 as a function of time;
FIG. 2A is a graph relating MCP count cytotoxic lymphocytes to the labeled CD8+ T cell infiltration score;
FIG. 2B is a graph relating MCPcounter counts CD8+ T cells to the marker CD8+ T cell infiltration score;
FIG. 2C is a graph of the correlation of TIDE calculated Cytotoxic T Lymphocytes (CTL) with a labeled CD8+ T cell infiltration score;
FIG. 3 is a graph of data for lymphocyte choriomeningitis virus (LCMV) response scores and TME2.T cell response scores for tumors;
figure 4 is a data distribution graph of the tme1.t cell infiltrates score of the tumor cells of 454 samples and the tme2.t cell responsiveness score of the tumor cells of 454 samples.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
Example 1 design of a method to predict Tumor Microenvironment (TMEPRE)
Data source
Publicly available gene table data and MSI/MSS status of four colorectal cancer datasets, namely GSE13294, GSE26682, GSE18088 and GSE39084, are downloaded from the GEO database, and are all from the Affymetrix Human Genome U133 plus2.0 Array platform and normalized using the freezing rma (frma) method in the frma software package (McCall et al 2010), and then sample batch processing effects of the four datasets are removed using ComBat (Johnson et al 2007), and gene expression data of 454 samples are collected, wherein the MSI samples are 131, the MSI-L (microsatellite low instability) samples are 23, the MSS samples are 284 and the samples of unknown species are 16.
Design of TMEPRE model
The TMEPRE model has two scores, the first is the score of whether CD8+ T cells have infiltrates (hereinafter referred to as TME1) and the second is the score of the failure pattern of infiltrative CD8+ T cells (hereinafter referred to as TME 2).
Regarding the score of TME1, specifically: the expression level of CD8A was used to initially estimate the abundance of CD8+ T cells. Cutoff values for CD8A expression levels were defined as 40% of CD8A expression levels of 131 MSI tumors; among them, MSI tumors with CD8A expression levels above the cutoff value were defined as tumors with infiltrating CD8+ T cells (n is 78, n is the number of samples, the same below); MSS tumors with CD8A expression levels below the critical value were defined as tumors without infiltrating CD8+ T cells (n 211). 200 rounds of 10 fold cross validation were performed between these two sets. In each cross-validation round, the t-test and p-value of each gene were ranked. The CD8A gene was excluded from the cross-validation process. In 200 rounds of cross-validation, the genes with p-values top 60 were selected as markers for TME1 in at least 80% of the cross-validation runs, of which 28 genes were selected as markers for TME1, as detailed in table a:
regarding the score of TME2, specifically: scoring the tumor microenvironment of infiltrating CD8+ T cells to determine the pattern of cell failure and thus the responsiveness of colorectal cancer patients to immunotherapeutic drugs.
Since TIM3 is an early-obtained coexpression inhibitor receptor among all coexpression inhibitory receptors, the coexpression pattern of multiple inhibitory receptors of PD1 and TIM3 was employed for defining the pattern of end-depletion. (Thommen et al 2015) in the above MSI tumors with high CD8+ T cell infiltration (n ═ 78), the median PD1 expression level was used as the cutoff value for PD1 and the median TIM3 expression level was used as the cutoff value for TIM 3. MSI tumors that infiltrated CD8+ T cells and PD1 and TIM3 expressed levels above the cutoff value were defined as tumor microenvironments with co-expression of multiple early inhibitory receptors. CD8+ T cells presenting this tumor microenvironment begin to gradually end-fail and resist anti-PD1 treatment (n-21). MSI tumors that infiltrate CD8+ T cells but have PD1 and TIM3 expressed levels below the critical value are defined as tumor microenvironments in which CD8+ T cells can still respond to anti-PD1 therapy (n ═ 21). 200 rounds of 10-fold cross validation were performed between the two groups. In each cross-validation round, the t-test and p-value of each gene were ranked. In 200 rounds of cross-validation, the genes with p-values top 60 were selected as markers for TME2 in at least 80% of the cross-validation runs, of which 29 genes were selected as markers for TME2, as detailed in table B:
it can be seen that invasive CD8+ T cells, if not in terminal failure mode, indicate that the colorectal cancer patient is still able to respond to the checkpoint inhibitor, and invasive CD8+ T cells, if in terminal failure mode, indicate that the colorectal cancer patient is not responding to the checkpoint inhibitor.
The judgment conditions for evaluating whether the colorectal cancer patients have the responsiveness to the immunotherapy drugs are summarized by the first part TME1 score and the second part TME2 score: when the first fraction TME1 is low (indicating tumor non-infiltrating CD8+ T cells), it can be determined that the colorectal cancer patient is not responsive to a single immunotherapeutic drug; TME2 was further scored when the first fraction TME1 was high (indicating tumor-infiltrating CD8+ T cells), if TME2 was high (indicating tumor-infiltrating CD8+ T cells are not in end-failure mode) it was determined that the colorectal cancer patient was responsive to a single immunotherapeutic drug, and if TME2 was low (indicating tumor-infiltrating CD8+ T cells are in end-failure mode) it was determined that the colorectal cancer patient was not responsive to a single immunotherapeutic drug. Such non-responsive cancer patients need combination immunotherapy drug therapy, e.g., considering anti-PD1 and other combination therapies directed against tumor microenvironment drugs, or replacing other single immunotherapy drug therapies.
This example determines whether a colorectal cancer patient is responsive to an immunotherapeutic drug based on the characteristics of a subset of tumor-infiltrating CD8+ T cells that exhibit an anti-PD1 response, as well as the tumor-infiltrating CD8+ T cells.
Example 2 testing marker Performance in cancer immunotherapy response
The predicted value of the anti-PD1 response was examined, using anti-PD1 as an example. The biomarkers of this example were the biomarkers obtained by the TMEPRE method of example 1.
Data source
Downloading three RNAseq data sets from the GEO database, wherein the three data sets specifically comprise:
the first data set included normalized RNAseq data and clinical data from pre-treatment samples of melanoma patients receiving pembrolizumab or nivolumab treatment, excluding patients receiving MAPK inhibitors (n-16, GSE 78220);
the second data set included normalized RNAseq data and clinical data of melanoma patient specimens receiving nivolumab, samples from early treatment time points prior to day 29 of cycle 1 and pretreatment time points prior to day 0 of cycle 1 were analyzed to reject patients who received prior ipilimumab (ipilimumab) treatment or whose overall survival data was incomplete (n-21, GSE91061), respectively;
the third data set included standardized RNAseq data for precursor depleted CD8+ T cells and terminal depleted CD8+ T cells isolated from tumors and chronic viral infections (n ═ 20, GSE 122713).
Prediction of anti-PD1 therapeutic response using the TMEPRE model
Melanoma is a model tumor widely used for verifying CD8+ T cells and immunotherapy response, the melanoma is used as a model for verification, and a TMEPRE model is verified on 3 data sets of a melanoma patient receiving anti-PD1 treatment, and specifically comprises the following steps:
in the first data set, shown in fig. 1A, melanoma patient survival analysis of the TMEPRE model yielded significant risk ratios (n-16, pretreatment samples, GSE78220, HR-4.59, p-0.056). In the second data set, fig. 1B, despite the large p-value of the melanoma patient survival analysis of the TMEPRE predictive model (n 21, sampled before day 0 of cycle 1, GSE91061, HR 2.12, p 0.115), survival separation between the TMEPRE predicted response group and the TMEPRE predicted response group was observed.
In the third data set, shown in figure 1C, melanoma patient survival analysis of the TMEPRE model yielded significant risk ratios (sampled before day 29 of cycle 1 at the early treatment time point, n-21, GSE91061, HR-5.04, p-0.003, figure 1C).
In addition, the data used to validate the anti-PD1 response (GSE78220 and GSE91061) were from melanoma patients on the IlluminaHiSeq platform; data used to train the models (GSE13294, GSE26682, GSE18088, GSE39084) were from colorectal cancer patients on the Affymetrix platform. anti-PD1 response data or survival data were not used in model training. Despite different cancer types and different data platforms, the TMEPRE model showed predictive ability for responsiveness to anti-PD1 therapy.
Example 3 testing the biological principles or associations of markers and CD8+ T cells responsive to cancer immunotherapy
In a data set of 454 samples, tumor-infiltrating immune cell counts were read using MCPcounter (MCP counter) (Becht et al 2016) counts and TIDE (Jiang et al 2018) counts of Cytotoxic T Lymphocytes (CTL). As shown in fig. 2A, 2B, and 2C, the TME1 score of the first part of the TMEPRE model was positively correlated with the MCPcounter count CD8+ T cells, the MCPcounter count cytotoxic lymphocytes, and the TIDE count cytotoxic T lymphocytes, respectively.
In addition, based on the 284 MSS samples and 454 samples obtained in example 1, the ratio ω of the TME1 score of the 284 MSS samples to the TME1 score of the 454 samples was calculated(TME1)(ii) a Calculate the ratio ω of the MCPcounter cytotoxic lymphocyte score for 284 MSS samples to the MCPcounter cytotoxic lymphocyte score for 454 samples(MCPcounter cytotoxic lymphocytes)(ii) a The ratio of the MCPcounter CD8+ T cell score of 284 MSS samples to the MCPcounter CD8+ T cell score of 454 samples was calculated, ω(MCPcounter CD8+ T cells)(ii) a Calculate TIDE for 284 MSS samples the ratio ω of the cytotoxic T lymphocyte score to the TIDE for 454 samples the cytotoxic T lymphocyte score(TIDE cytotoxic T lymphocytes)The calculation formulas are respectively as follows:
ω(TME1)(MSS sample maximum value of credit)(TME1)-MSS sample minimum score value(TME1)) V (Total sample maximum score value)(TME1)-total sample minimum score value(TME1))；
ω(MCPcounter cytotoxic lymphocytes)(MSS sample maximum value of credit)(MCPcounter cytotoxic lymphocytes)-MSS sample minimum score value(MCPcounter cytotoxic lymphocytes)) V (Total sample maximum score value)(MCPcounter cytotoxic lymphocytes)-total sample minimum score value(MCPcounter cytotoxic lymphocytes))；
ω(MCPcounter CD8+ T cells)(MSS sample maximum value of credit)(MCPcounter CD8+ T cells)-MSS sample minimum score value(MCPcounter CD8+ T cells)) V (Total sample maximum score value)(MCPcounter CD8+ T cells)-total sample minimum score value(MCPcounter CD8+ T cells))；
ω(TIDE cytotoxic T lymphocytes)(MSS sample maximum value of credit)(TIDE cytotoxic T lymphocytes)-MSS sample minimum score value(TIDE cytotoxic T lymphocytes)) V (Total sample maximum score value)(TIDE cytotoxic T lymphocytes)-total sample minimum score value(TIDE cytotoxic T lymphocytes))；
Through calculation, omega is obtained(TME1)＝0.89，ω(MCPcounter cytotoxic lymphocytes)＝0.67，ω(MCPcounter CD8+ T cells)＝0.53，ω(TIDE cytotoxic lymphocytes)As can be seen, the ratio ω of TME1 scores is maximal at 0.81.
In addition, observing figure 2B reveals that the MSS tumor group with a low MCPcounter count of CD8+ T cells contained a relatively high CD8+ T cell infiltration on the TME1 score and observing figure 2C reveals that the MSS tumor group with a low TIDE count of cytotoxic T lymphocytes contained a relatively high CD8+ T cell infiltration on the TME1 score, which indicates that TME1 is more sensitive in detecting tumor infiltrating toxic lymphocytes in MSS colorectal tumors with lower tumor infiltration immune cells because TME1 is designed for the tumor microenvironment of colorectal cancer, whereas the mcpounter count and TIDE algorithms are not.
The TME2 score of the TMEPRE model was aimed at testing whether tumor-infiltrating CD8+ T cells could respond to anti-PD1 therapy. To test whether TME2 actually captured this feature of tumor-infiltrating CD8+ T cells, TME2 marker scores were read for CD8+ T cells isolated from both groups of tumors and chronic viral infections that were dysfunctional: end-depleted tumor-infiltrating CD8+ T cells no longer respond to anti-PD1 therapy, while precursor-depleted tumor-infiltrating CD8+ T cells still respond to anti-PD-1 therapy (GSE 122713). Since the TME2 markers gene expression data from bulk tumor samples, the source of gene expression was derived from a mixture of CD8+ T cells, tumor cells and other tumor-infiltrating immune cells in the tumor microenvironment, while precursor/terminal failure tumor-infiltrating CD8+ T cell data was derived from isolated CD8+ T cells. Thus, when reading the TME2 score, only genes derived from CD8+ T cells were utilized. Median expression values of 16 purified immune cells were compared using the BloodSpot and hemaexpressor human hematopoiesis databases for the 29 genes of TME2. When CD8+ T cells are the first two major immune cell types expressing a gene, the gene is considered to be predominantly expressed by CD8+ T cells. There are 7 genes in TME2 (CCL5, CD2, CD48, CD84, FAM78A, HCST, IL21R) passing these criteria, and 2 genes in TME2 (HAVCR2, PDCD1) are inhibitor receptors on CD8+ T cells, defining precursor depleted CD8+ T cells and terminal depleted CD8+ T cells.
These 9 genes were used to read TME2 scores of a data set of isolated precursor-depleted tumor-infiltrating CD8+ T cells, terminal-depleted tumor-infiltrating CD8+ T cells. As shown in FIG. 3, the precursor failure tumor infiltration CD8+ T cell subset TME2 scored significantly higher (score value) in tumors and chronic viral infections (e.g., lymphocytic choriomeningitis virus)(tumor)<0.001, score value(viral infection)0.048). Thus, the TME2 score did capture the characteristics of tumor-infiltrating CD8+ T cells that were still able to respond to precursor cell failure in anti-PD 1.
Example 4 testing of marker expression in colorectal cancer MSI/MSS samples
The biomarker of this example is the biomarker obtained by the method of example 1.
The TMEPRE model was read in 454 colorectal samples (MSI 131, MSI-L23, MSS 284, unknown 16). As shown in fig. 4, tumors that showed sufficient CD8+ T cell infiltration patterns without CD8+ T cell end-failure patterns were considered potential responders to anti-PD1 treatment.
Of the 284 MSS tumor samples, 10.6% (n-30) were responders and 89.4% (n-254) were non-responders. Of the MSS non-responders, 86.6% of MSS tumor samples (n ═ 246) showed no sufficient tumor-infiltrating CD8+ T cells, and 2.8% of MSS tumor samples (n ═ 8) showed sufficient tumor-infiltrating CD8+ T cells to show terminal-failure CD8+ T cell pattern. It can be seen that the anti-PD1 resistance mechanism of most MSS tumors is an insufficient number of tumor-infiltrating CD8+ T cells.
Of the 131 MSI tumor samples, 67.2% of the MSI tumor samples (n ═ 88) were responders and 32.8% of the MSI tumor samples (n ═ 43) were non-responders. Among the MSI non-responders, 16.0% of MSI tumor samples (n ═ 21) showed no sufficient tumor-infiltrating CD8+ T cells, and 16.8% of MSI tumor samples (n ═ 22) showed sufficient tumor-infiltrating CD8+ T cells to show a pattern of end-depleted CD8+ T cells. Thus, approximately 50% of MSI non-responders are due to insufficient numbers of tumor-infiltrating CD8+ T cells, and the remaining 50% are caused by terminal depletion of CD8+ T cells in the tumor microenvironment, which requires consideration of anti-PD1 and other combination therapies against tumor microenvironment drugs.
The TMEPRE model concluded that tumors in 10.6% MSS colorectal cancer patients and 67.2% MSI colorectal cancer patients exhibited biological characteristics that could benefit from anti-PD1 treatment, and these predicted percentages of MSS tumor responders and MSI tumor responders were consistent with the reported benefit of pembrolizumab treatment in immune-related disease control rates in colorectal cancer patients at 20 weeks.
In summary, it is not suggested to administer anti-PD1 therapy to MSS colorectal cancer patients by evaluating MSI/MSS status alone. Clinical data indicate that disease control rate for metastatic MSS colorectal cancer treated with pembrolizumab is 11%, and further, in the latest clinical trial of neoadjuvant therapy, the pathological response rate for early stage MSS colorectal cancer treatment with early stage ipilimumab + rifammab treatment is 27%. These results indicate the presence of responders to anti-PD1 treatment in the MSS colorectal cancer population. In the above analysis, approximately 10.6% of MSS tumor samples showed high TME1 and high TME2 scores, indicating that the biological properties of the tumor microenvironment of 10.6% of MSS patients may still benefit from anti-PD1 treatment.
Example 5 method for diagnosing responsiveness of colorectal cancer patient to immunotherapeutic drugs
The method of the embodiment mainly comprises the following steps:
a) taking tumor tissue as a detection sample, preferably taking blood related to tumor as a sample, and more preferably taking mononuclear cells of peripheral blood as a sample;
b) detecting a marker selected from table a in the sample using the diagnostic product, wherein when the table a marker is low, it is indicative of a colorectal cancer patient not being responsive to the immunotherapeutic agent, the colorectal cancer patient being unsuited for using the immunotherapeutic agent; when the markers in table a are high, the markers in table B are continuously detected, if the markers in table B are high, it is indicated that the patient with colorectal cancer is responsive to the immunotherapeutic drug, the patient with colorectal cancer is suitable for using the immunotherapeutic drug, if the markers in table B are low, it is indicated that the patient with colorectal cancer is not responsive to the immunotherapeutic drug, the patient with colorectal cancer is not suitable for using the single immunotherapeutic drug, the patient with colorectal cancer needs to be treated with the combination immunotherapeutic drug, for example, the combination therapy of anti-PD1 and other drugs against tumor microenvironment is needed to be considered, or the treatment is carried out with the other single immunotherapeutic drug.
The high score of the marker is expressed by up-regulation or down-regulation of gene expression, the concentration C1 of the responsive marker is higher than the standard value C0 and is up-regulation of gene expression, the concentration C1 of the responsive marker is lower than the standard value C0 and is down-regulation of gene expression, and C0 is the expression quantity of the immunotherapeutic drug responsive marker in the population of the non-responders of the immunotherapeutic drug.
The markers of table a and table B in example 1, when responding to cancer immunotherapy, behave as follows:
the marker may be a gene, mRNA, cDNA and/or protein.
Wherein Table A is the markers described in Table A of example 1 and Table B is the markers described in Table B of example 1.
When detecting a marker of Table A, this example detects a marker selected from at least one of markers A1-A23 of Table A.
Preferably, the present example detects at least two markers selected from the group consisting of table a markers a1-a23, more preferably, at least 5 markers selected from the group consisting of table a markers a1-a 23; more preferably, a marker selected from at least 6 of Table A markers A1-A23; more preferably, a marker selected from at least 7 of Table A markers A1-A23; more preferably, a marker selected from at least 8 of Table A markers A1-A23; more preferably, at least 9 or more markers selected from table a markers a1-a 23; more preferably, a marker selected from at least 10 of Table A markers A1-A23; more preferably, at least 15 markers selected from table a markers a1-a 23; more preferably, a marker selected from at least 20 of table a markers a1-a 23; more preferably, all 23 markers selected from table a markers a1-a 23.
More preferably, this example detects at least two markers selected from the group consisting of table a markers a1-a28, more preferably, at least 5 markers selected from the group consisting of table a markers a1-a 28; more preferably, a marker selected from at least 6 of Table A markers A1-A28; more preferably, a marker selected from at least 7 of Table A markers A1-A28; more preferably, a marker selected from at least 8 of Table A markers A1-A28; more preferably, at least 9 or more markers selected from table a markers a1-a 28; more preferably, a marker selected from at least 10 of Table A markers A1-A28; more preferably, at least 15 markers selected from table a markers a1-a 28; more preferably, a marker selected from at least 20 of table a markers a1-a 28; more preferably, all 28 markers selected from table a markers a1-a 28.
When detecting a marker of table B, the present example detects a marker selected from at least one of the markers B1-B22 of table B.
More preferably, this example 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, at least 9 markers selected from table B 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, the 22 marker is selected from the 22 markers in Table B markers B1-B22;
more preferably, when detecting a marker of table B, the present example detects at least two markers selected from the group consisting of markers B1-B29 of table B. 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, at least 8 markers selected from table B 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, the 29 marker is selected from the group consisting of markers B1-B29 of Table B;
in fact, the simultaneous measurement of at least two markers enables a more suitable and reliable assessment of the responsiveness of colorectal cancer patients to a single immunotherapeutic drug, it is the use of such a set of markers that the present invention is directed to, and not just the single marker.
Example 6 typing method for colorectal cancer patients for detecting at least two markers
The colorectal cancer typing method of the embodiment mainly comprises the following steps:
(1) obtaining the mRNA gene expression level of at least two markers a1-a28 or a1-a23 in table a of a certain test subject in said sample, preferably the mRNA gene expression level is obtained by a technique of the group: microarray, RNAseq, RT-PCR.
(2) Normalizing the gene expression values of at least two markers in a1-a28 or a1-a23 in table a, preferably, the normalization is performed by a method selected from the group consisting of: fRMA, RMA, RNAseq CPM, RNAseq FPKM.
(3) Obtaining gene expression values of at least two markers of A1-A28 or A1-A23 in the above Table A in a plurality of responders, respectively, and then calculating an average gene expression value of the same marker of all responders, wherein the gene expression values of a plurality of known immunotherapy drug responders can be obtained from clinical medicine database; similarly, the average gene expression values of the same marker of tumor non-responders of non-invasive CD8+ T cells were calculated, i.e., the gene expression values of at least two markers a1-a28 or a1-a23 in table a above were obtained respectively from a plurality of tumor non-responders of non-invasive CD8+ T cells, and then the average gene expression values of the same marker of all tumor non-responders of non-invasive CD8+ T cells were calculated, wherein the gene expression values of tumor non-responders of a plurality of known immunotherapeutic drugs non-invasive CD8+ T cells were obtained from clinical medical databases.
(3) Calculating a first similarity of at least two marker normalization values in table a above or a1-a28 or a1-a23 of the object to be detected to the average gene expression value of the at least two markers in the responder; and calculating a second similarity of the normalized values of at least two markers of table a1-a28 or table a1-a23 to the subject to be tested to the mean gene expression values of the at least two markers in tumor non-responders of non-infiltrating CD8+ T cells, preferably, calculating the similarity by a method selected from the group consisting of: euclidean distance, manhattan distance, minkowski distance, chebyshev distance, jacado distance, pearson correlation, cosine correlation, or regression value.
(4) Calculating the difference between the first similarity and the second similarity, wherein when the difference is a negative value, the subject is a non-responder to the immunotherapy drug; when the difference value is a positive value, the following steps are continued:
(5) obtaining the mRNA gene expression level of at least two markers of B1-B29 or B1-B22 in table B of the above-mentioned test object in the sample, preferably, the mRNA gene expression level is obtained with a technique by the following group: microarray, RNAseq, RT-PCR.
(6) Normalizing the gene expression values of at least two markers in B1-B29 or B1-B22 in table B, preferably, the normalization is performed by a method selected from the group consisting of: fRMA, RMA, RNAseq CPM, RNAseq FPKM.
(7) Obtaining gene expression values of at least two markers of B1-B29 or B1-B22 in the above-mentioned Table B in a plurality of responders respectively, and then calculating the average gene expression value of the same marker of all responders with precursor failure tumor infiltration CD8+ T cells, wherein the gene expression values of a plurality of responders with known immunotherapy drugs, precursor failure tumor infiltration CD8+ T cells can be obtained from a clinical medicine database; similarly, the average gene expression values of the same markers of the patients with terminal failure tumor infiltration CD8+ T cell non-responders are calculated, i.e., the gene expression values of at least two markers of B1-B29 or B1-B22 in table B above are obtained respectively from a plurality of patients with terminal failure tumor infiltration CD8+ T cell non-responders, and then the average gene expression values of the same markers of all the patients with terminal failure tumor infiltration CD8+ T cell non-responders are calculated, wherein the gene expression values of a plurality of known immunotherapy drugs with terminal failure tumor infiltration CD8+ T cell non-responders can be obtained from clinical medicine databases.
(8) Calculating a first similarity between the normalized values of the at least two markers in B1-B29 or B1-B22 in Table B of the subject to be detected and the mean gene expression values of the at least two markers in the responder to CD8+ T cells with precursor failure tumor infiltration; and calculating a second similarity of the normalization of the at least two markers in B1-B29 or B1-B22 in table B of the subjects to be detected to the mean gene expression value of the at least two markers in non-responders with terminal failure tumor infiltration CD8+ T cells; preferably, the similarity is calculated by a method selected from the group consisting of: euclidean distance, manhattan distance, minkowski distance, chebyshev distance, jacado distance, pearson correlation, cosine correlation, or regression value;
(9) calculating a difference value between the first similarity and the second similarity, wherein when the difference value is a negative value, the object is a non-responder of a single immunotherapy medicament, and colorectal cancer treatment needs to be performed on the non-responder by adopting a combined immunotherapy medicament; when the difference value is a positive value, the object is a responder of the immunotherapy medicament, and the colorectal cancer treatment can be carried out on the responder classified as the immunotherapy medicament by adopting a single immunotherapy medicament.
This embodiment is suitable for detecting more than two markers, but does not limit the present invention to detecting one marker.
Preferably, the present example detects at least two markers selected from the group consisting of table a markers a1-a23, more preferably, at least 5 markers selected from the group consisting of table a markers a1-a 23; more preferably, a marker selected from at least 6 of Table A markers A1-A23; more preferably, a marker selected from at least 7 of Table A markers A1-A23; more preferably, a marker selected from at least 8 of Table A markers A1-A23; more preferably, at least 9 or more markers selected from table a markers a1-a 23; more preferably, a marker selected from at least 10 of Table A markers A1-A23; more preferably, at least 15 markers selected from table a markers a1-a 23; more preferably, a marker selected from at least 20 of table a markers a1-a 23; more preferably, all 23 markers selected from table a markers a1-a 23.
More preferably, this example detects at least two markers selected from the group consisting of table a markers a1-a28, more preferably, at least 5 markers selected from the group consisting of table a markers a1-a 28; more preferably, a marker selected from at least 6 of Table A markers A1-A28; more preferably, a marker selected from at least 7 of Table A markers A1-A28; more preferably, a marker selected from at least 8 of Table A markers A1-A28; more preferably, at least 9 or more markers selected from table a markers a1-a 28; more preferably, a marker selected from at least 10 of Table A markers A1-A28; more preferably, at least 15 markers selected from table a markers a1-a 28; more preferably, a marker selected from at least 20 of table a markers a1-a 28; more preferably, all 28 markers selected from table a markers a1-a 28.
When detecting a marker of table B, the present example detects at least two markers selected from the group consisting of markers B1-B22 of table B, more preferably, at least 6 markers selected from the group consisting of markers B1-B22 of table B; 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, at least 9 markers selected from table B 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, the 22 marker is selected from the 22 markers in Table B markers B1-B22;
more preferably, when detecting a marker of table B, the present example detects at least two markers selected from the group consisting of markers B1-B29 of table B. 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, at least 8 markers selected from table B 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, the 29 marker is selected from the group consisting of markers B1-B29 of Table B;
cancer immunotherapy includes: any one or more than two of anti-PD1 drug, anti-PDL1 drug, anti-CTLA4 drug, anti-TIM3 drug, anti-BTLA drug, anti-VISTA drug or anti-LAG3 drug.
EXAMPLE 7 colorectal cancer typing apparatus for detecting at least two markers
The apparatus comprises:
(P1) an input unit for inputting data on the mRNA gene expression levels of at least two markers in a sample in table a or table B, or a1-a23 in table a or B1-B22 in table B, of a subject;
(P2) a data processing unit which processes the input data of mRNA gene expression level, and which includes a normalization processing subunit, a similarity operator unit, and a similarity difference value calculation subunit;
wherein the normalization processing subunit is used for normalizing the gene expression values of at least two markers in the table A or the table B, or the A1-A23 in the table A or the B1-B22 in the table B;
the similarity operator unit is used for calculating a first similarity of the normalized values of the at least two markers and the average gene expression value of the at least two markers in the table A or the table B, or the A1-A23 in the table A or the B1-B22 in the table B in the immunotherapy drug responder; and calculating a second similarity of the normalized values of the at least two markers in table a or table B to the mean gene expression value of the at least two markers in the non-responder to the immunotherapeutic drug;
the similarity difference calculation subunit is used for calculating the difference between the first similarity and the second similarity of each marker gene;
(P3) a typing unit that types the test object based on the difference between the marker genes to obtain a test object that is a responder or non-responder to the single immunotherapeutic drug, thereby obtaining a typing result; and
(P4) an output device for outputting the typing result.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (20)
1. A marker for detecting CD8+ T cells in a colorectal cancer patient, wherein the marker comprises a first marker for determining whether infiltrating CD8+ T cells are present in the colorectal cancer patient, the first marker comprising at least two of the genes CXCL13, LY6G6D, CXCL10, SRSF6, SAMD9L, TNFSF13B, RASGRP1, CAB39L, SET, EIF5A, ITCH, TRIM69, MCUB, TYMS, ZDHHC9, LYSMD2, zchc 2, BRD3, PSME2, PSME1, NR6a1, ATP5F1A, and nut 2A-AS 1.
2. The marker of claim 1, wherein the first marker is at least two of the genes CXCL13, LY6G6D, CXCL10, SRSF6, SAMD9L, TNFSF13B, RASGRP1, CAB39L, SET, EIF5A, ITCH, TRIM69, MCUB, TYMS, ZDHHC9, lysdm 2, zchc 2, BRD3, PSME2, PSME1, NR6a1, ATP5F1A, nut 2A-AS1, CCL5, GZMA, GBP1, STAT1, and CXCL 9.
3. The marker of claim 1, further comprising a second marker for determining the failure mode of the infiltrating CD8+ T cells.
4. The marker according to claim 3, wherein said second marker is at least two of genes C1QC, FCGR1B, C1QB, TFEC, CD2, FCER1G, KMO, APBB1IP, CD48, LAPTM5, CYBB, NCF1B, NR1H3, IFI30, WIPF1, SLAMF8, FAM78A, HCST, IL4I1, TNFSF14, LRLIB 3, CSF 1.
5. The marker of claim 3, wherein the second marker is at least two of genes C1QC, FCGR1B, C1QB, TFEC, CD2, FCER1G, KMO, APBB1IP, CD48, LAPTM5, CYBB, NCF1B, NR1H3, IFI30, WIPF1, SLAMF8, FAM78A, HCST, IL4I1, TNFSF14, LRLIB 3, CSF1, PDCD1, CD84, IL21R, HAVCCR 2, FCGR1 GR1A, CCL5 and CXCL 9.
6. The marker of any one of claims 1 to 5, wherein the immunotherapeutic drug is one of anti-PD1, anti-PDL1, anti-CTLA4, anti-TIM3, anti-BTLA, anti-VISTA and anti-LAG 3.
7. A kit for assessing responsiveness of a colorectal cancer patient to an immunotherapeutic drug, comprising a first detection reagent for detecting the amount of a first marker capable of determining whether infiltrating CD8+ T cells are present in the colorectal cancer patient, the first marker comprising at least two of the genes CXCL13, LY6G6D, CXCL10, SRSF6, SAMD9L, TNFSF13B, RASGRP1, CAB39L, SET, EIF5A, ITCH, TRIM69, MCUB, TYMS, ZDHHC9, LYSMD2, zchc 2, BRD3, PSME2, PSME1, NR6a1, ATP5F1A, and nut 2A-AS 1.
8. The kit of claim 7, wherein the first marker is at least two of the genes CXCL13, LY6G6D, CXCL10, SRSF6, SAMD9L, TNFSF13B, RASGRP1, CAB39L, SET, EIF5A, ITCH, TRIM69, MCUB, TYMS, ZDHHC9, LYSMD2, zchc 2, BRD3, PSME2, PSME1, NR6a1, ATP5F1A, nut 2A-AS1, CCL5, GZMA, GBP1, STAT1, and CXCL 9.
9. The kit according to claim 7 or 8, wherein the first detection reagent comprises a probe for detecting the gene of the first marker, or/and a reagent for detecting the amount of mRNA, cDNA or/and protein corresponding to the gene of the first marker.
10. The kit of claim 7, further comprising a second detection reagent for detecting the amount of a second marker capable of determining the failure mode of said infiltrating CD8+ T cells.
11. The kit of claim 10, wherein the second marker is at least two of genes C1QC, FCGR1B, C1QB, TFEC, CD2, FCER1G, KMO, APBB1IP, CD48, lamtm 5, CYBB, NCF1B, NR1H3, IFI30, WIPF1, SLAMF8, FAM78A, HCST, IL4I1, TNFSF14, LILRB3, CSF 1.
12. The kit of claim 10, wherein the second marker is at least two of the genes C1QC, FCGR1B, C1QB, TFEC, CD2, FCER1G, KMO, APBB1IP, CD48, lamtm 5, CYBB, NCF1B, NR1H3, IFI30, WIPF1, SLAMF8, FAM78A, HCST, IL4I1, TNFSF14, LILRB3, CSF1, PDCD1, CD84, IL21R, HAVCR2, FCGR1A, CCL5, and CXCL 9.
13. The kit according to any one of claims 10 to 12, wherein the second detection reagent comprises a probe for detecting a gene of the second marker, or/and a reagent for detecting the content of mRNA, cDNA or/and protein corresponding to the gene of the second marker.
14. A method for assessing the responsiveness of a colorectal cancer patient to a single immunotherapeutic drug, comprising the steps of:
1) providing a biological sample of a colorectal cancer patient, the sample selected from at least one of a blood sample, a serum sample, a mononuclear cell sample isolated from peripheral blood, a tissue sample, and a body fluid sample;
2) detecting the content of the first marker in the biological sample to judge whether the colorectal cancer patient has invasive CD8+ T cells; and
3) if the judgment result in the step 2) is negative, the colorectal cancer patient has no responsiveness to the single immunotherapy medicament, and the evaluation is finished;
if the determination result in the step 2) is yes, detecting the content of the second marker in the biological sample to determine the exhaustion pattern of the infiltrating CD8+ T cells,
when the failure mode is a precursor failure mode, judging that the colorectal cancer patient is a responder of the single immunotherapy drug; when the failure mode is terminal failure mode, the colorectal cancer patient is judged to be a non-responder to the single immunotherapy drug.
15. The method of claim 14, wherein the first marker is at least two of the genes CXCL13, LY6G6D, CXCL10, SRSF6, SAMD9L, TNFSF13B, RASGRP1, CAB39L, SET, EIF5A, ITCH, TRIM69, MCUB, TYMS, ZDHHC9, lysdm 2, zchc 2, BRD3, PSME2, PSME1, NR6a1, ATP5F1A, nut 2A-AS 1.
16. The method of claim 14, wherein the first marker is at least two of the genes CXCL13, LY6G6D, CXCL10, SRSF6, SAMD9L, TNFSF13B, RASGRP1, CAB39L, SET, EIF5A, ITCH, TRIM69, MCUB, TYMS, ZDHHC9, lysdm 2, zchc 2, BRD3, PSME2, PSME1, NR6a1, ATP5F1A, nut 2A-AS1, CCL5, GZMA, GBP1, STAT1, and CXCL 9.
17. The method of claim 14, wherein the second marker is at least two of genes C1QC, FCGR1B, C1QB, TFEC, CD2, FCER1G, KMO, APBB1IP, CD48, lamtm 5, CYBB, NCF1B, NR1H3, IFI30, WIPF1, SLAMF8, FAM78A, HCST, IL4I1, TNFSF14, LILRB3, CSF 1.
18. The method of claim 14, wherein the second marker is at least two of genes C1QC, FCGR1B, C1QB, TFEC, CD2, FCER1G, KMO, APBB1IP, CD48, lamtm 5, CYBB, NCF1B, NR1H3, IFI30, WIPF1, SLAMF8, FAM78A, HCST, IL4I1, TNFSF14, LILRB3, CSF1, PDCD1, CD84, IL21R, HAVCR2, FCGR1A, CCL5, and CXCL 9.
19. The method of claim 14, wherein the immunotherapeutic drug is one of anti-PD1, anti-PDL1, anti-CTLA4, anti-TIM3, anti-BTLA, anti-VISTA and anti-LAG 3.
20. A method for preparing or screening a drug for immunotherapy of colorectal cancer, which comprises preparing or screening an anti-tumor drug by using the marker of claims 1 to 6, the kit of claims 7 to 13, or/and the method of claims 14 to 19.
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