CN117316291B - Immunoregulation gene classification method based on relationship between therapeutic effect and toxicity of immunotherapy - Google Patents

Abstract

The present invention discloses an immunomodulatory gene classification method based on the relationship between immunotherapy efficacy and toxicity, comprising: obtaining pre-treatment transcriptome second-generation sequencing data and during-treatment group second-generation sequencing data of a number of ICB-treated patients; performing difference analysis on the pre-treatment transcriptome second-generation sequencing data and the during-treatment group second-generation sequencing data to obtain a number of immunomodulatory genes; obtaining the odds ratio of immune-related adverse reactions and the objective response rate of immunotherapy corresponding to each tumor type; obtaining the mRNA expression spectrum data of each tumor type in a cancer genome atlas, and obtaining the median expression value of the immunomodulatory gene based on the mRNA expression spectrum data of each tumor type; performing correlation analysis on the median expression value of the immunomodulatory gene and the odds ratio of immune-related adverse reactions and the objective response rate of immunotherapy to obtain the correlation relationship; and classifying the immunomodulatory genes based on the correlation relationship.

Description

Immunomodulatory gene classification method based on the relationship between immunotherapy efficacy and toxicity

Technical Field

The present invention relates to the field of biomedical technology, and in particular to an immunomodulatory gene classification method based on the relationship between immunotherapy efficacy and toxicity.

Background Art

Immunotherapy, especially immune checkpoint blockade (ICB) therapy targeting programmed death receptor (PD-1) and its ligand (PD-L1), has become a second-line or even first-line treatment for many advanced tumors. However, with the widespread clinical application of ICB, more and more immune-related adverse events (irAEs) have been reported. While activating anti-tumor immunity, ICB therapy can often couple inflammatory side effects in various organ systems throughout the body, inducing toxic reactions such as colitis, pneumonia, and myocarditis. Severe irAEs will force patients to stop taking the drug or even lead to their death.

In clinical practice, PD-L1, microsatellite instability (MSI), and tumor mutation burden (TMB) have been approved as biomarkers for immunotherapy and are widely used in clinical decision-making for cancer patients. In addition, factors such as genomic variation, tumor microenvironment, and organ-specific immunity have also been shown to affect the efficacy of immunotherapy. However, while classic biomarkers predict immunotherapy responses, they are often positively correlated with the occurrence of immune-related adverse reactions. Previous studies have found that indicators such as TMB, MSI, and CD8 T cell infiltration are positively correlated with immune adverse reactions while predicting immunotherapy efficacy. This means that patients with immunotherapy advantages screened using these markers are also high-incidence and high-risk groups for immune adverse reactions. Among the previously reported immunotherapy-related factors, which factors mediate the ICB efficacy-toxicity effect in a coupled manner, which have efficacy specificity, and which are toxicity/adverse reaction specific, need to be identified urgently.

Summary of the invention

To achieve the above-mentioned purpose, the present invention provides a method for classifying immunomodulatory genes based on the relationship between immunotherapy efficacy and toxicity. The construction method includes differential analysis to screen immunotherapy regulatory genes; disproportionality analysis to calculate the odds ratio of immune-related adverse reactions of each tumor type and collect the objective response rate of immunotherapy for each tumor type; calculating the median expression level of immunotherapy regulatory genes in each tumor type; correlation analysis to calculate the correlation coefficient and significance of the median expression level of each immunotherapy gene with the odds ratio of immune-related adverse reactions of the tumor type and the objective response rate of immunotherapy, and the genes are divided into four categories in turn: immunotherapy efficacy/toxicity coupled genes, immunotherapy toxicity specific genes, immunotherapy efficacy specific genes, and immunotherapy efficacy/toxicity unrelated genes.

The present invention provides a method for classifying immunomodulatory genes based on the relationship between immunotherapy efficacy and toxicity, comprising:

Obtain the pre-treatment transcriptome next-generation sequencing data and during-treatment next-generation sequencing data of several ICB-treated patients;

Performing differential analysis on the transcriptome second-generation sequencing data before treatment and the second-generation sequencing data during treatment to obtain several immunotherapy regulatory genes;

Obtain the odds ratio of immune-related adverse reactions and objective response rate of immunotherapy corresponding to each tumor type;

Obtaining mRNA expression profile data of each tumor type in the cancer genome atlas, and obtaining the median expression value of the immunotherapy regulatory gene based on the mRNA expression profile data of each tumor type;

Performing correlation analysis on the median expression value of the immunotherapy regulatory gene and the odds ratio of immune-related adverse reactions and the objective response rate of immunotherapy to obtain a correlation relationship;

The immunotherapy regulatory genes are classified based on the correlation relationship.

Optionally, before performing the differential analysis, the method further includes using the R language DESeq2 package to perform FPKM normalization on the pre-treatment transcriptome second-generation sequencing data and the during-treatment group second-generation sequencing data.

Optionally, the process of obtaining an immunotherapy modulatory gene comprises:

The false discovery rate was obtained based on the difference analysis;

Perform multiple testing correction on P values based on the false discovery rate;

When the false discovery rate is less than 0.2 and the P value is less than 0.05, an immunotherapy regulatory gene is obtained.

Optionally, the odds ratio of immune-related adverse reactions corresponding to each tumor type is calculated and reported based on the disproportionality analysis method.

Optionally, the calculation process of the median expression value of the immunotherapy regulatory gene includes:

Obtain RNA sequencing data of the pan-cancer dataset and clinical data of each tumor type;

Performing gene annotation on the RNA sequencing data to obtain annotated RNA sequencing data;

Merging the annotated RNA sequencing data and the clinical data based on R language to obtain merged data;

The median function was used to calculate the median expression value of each immunotherapy regulatory gene in each cancer type in the merged data.

Optionally, the correlation analysis includes calculating the Spearman correlation coefficient and significance level.

Optionally, the process of classifying the immunotherapy regulatory genes based on the correlation relationship includes:

When the median expression value of the immunotherapy regulatory gene is significantly correlated with the odds ratio of immune-related adverse reactions and the objective response rate of immunotherapy, the immunotherapy regulatory gene is an immunotherapy efficacy/toxicity coupling gene;

When the median expression value of the immunotherapy regulatory gene is significantly correlated with the odds ratio of the immune-related adverse reaction and the median expression value of the immunotherapy regulatory gene is not related to the objective response rate of immunotherapy, the immunotherapy regulatory gene is an immunotherapy toxicity-specific gene;

When the median expression value of the immunotherapy regulatory gene is unrelated to the odds ratio of the immune-related adverse reaction and the median expression value of the immunotherapy regulatory gene is significantly correlated with the objective response rate of immunotherapy, the immunotherapy regulatory gene is an immunotherapy efficacy-specific gene;

When the median expression value of the immunotherapy regulatory gene is respectively unrelated to the immune-related adverse reaction odds ratio and the immunotherapy objective remission rate, the immunotherapy regulatory gene is an immunotherapy efficacy/toxicity-independent gene.

The present invention has the following technical effects:

The prior art does not focus on immunotherapy regulatory genes, and does not comprehensively consider their impact on toxicity when calculating the correlation between genes and efficacy, and cannot achieve the "decoupling" of efficacy and toxicity. The present invention provides an immunotherapy regulatory gene classification method based on the relationship between immunotherapy efficacy and toxicity, which completes the classification of immunotherapy regulatory genes and achieves the "decoupling" of efficacy and toxicity based on the classification results.

The present invention can analyze the comprehensive impact of immunotherapy on immunotherapy by performing correlation analysis on the median expression value of immunotherapy regulatory genes and the odds ratio of immune-related adverse reactions and the objective remission rate of immunotherapy, and uncouple the respective contributions of each immunotherapy regulatory gene to the efficacy and toxicity of immunotherapy, thereby providing a molecular basis and potential therapeutic targets for screening potential beneficiaries of immunotherapy and people at high risk of immune-related adverse reactions.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

FIG1 is a schematic diagram of constructing an efficacy-toxicity model of immunotherapy regulatory genes in an embodiment of the present invention, wherein (A) is a schematic diagram showing that the obtained gene is an immunotherapy regulatory gene, and (B) is a schematic diagram showing the classification of immunotherapy regulatory genes;

Figure 2 is a schematic diagram of the efficacy-toxicity relationship and representative genes of immunotherapy regulatory genes in an embodiment of the present invention, wherein (A) is a schematic diagram of the proportion of each category of immunotherapy regulatory genes, (B) is a schematic diagram of representative genes of efficacy-toxicity coupling genes, (C) is a schematic diagram of representative toxicity-specific genes, and (D) is a schematic diagram of representative efficacy-specific genes;

FIG3 is a schematic diagram of the correlation between IFNG and immunotherapy efficacy and toxicity based on pan-cancer analysis in an embodiment of the present invention;

FIG4 is a schematic diagram of the correlation between ABCB1 and immunotherapy efficacy and toxicity based on pan-cancer analysis in an embodiment of the present invention;

FIG5 is a schematic diagram of the correlation between HLA-F and immunotherapy efficacy and toxicity based on pan-cancer analysis in an embodiment of the present invention.

DETAILED DESCRIPTION

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Embodiment 1

This embodiment provides a method for classifying immunomodulatory genes based on the relationship between immunotherapy efficacy and toxicity, comprising the following steps:

S1. Based on several ICB-treated patients, the pre-treatment transcriptome next-generation sequencing data and the during-treatment next-generation sequencing data were obtained.

The transcriptome next-generation sequencing data (RNA-seq) of 68 patients treated with ICB were collected before treatment (pre-ICB) and during treatment (on-ICB).

Based on the second-generation sequencing data of the transcriptome before treatment and the second-generation sequencing data of the during-treatment group, differential analysis was performed to obtain immunotherapy regulatory genes.

The sequencing data were normalized by FPKM using the R language DESeq2 package, and differential analysis was performed to calculate the false discovery rate (FDR) to correct for multiple testing P values. Genes with FDR q values < 0.2 and P < 0.05 were considered immunotherapy regulatory genes.

S2. Based on existing reports, obtain the odds ratio of immune-related adverse reactions and the objective response rate of immunotherapy for each tumor type.

Using the US FDA adverse events reporting system (FARES), the odds of irAEs with anti-PD-(L)1 drugs were compared with the odds of irAEs with all other drugs in the FARES database, and the immune-related adverse reaction odds ratio (irROR) for each cancer type was calculated by disproportionality analysis. The specific calculation process is as follows:

(1) Download the adverse event records from the U.S. FDA Adverse Event Reporting System (FAERS);

(2) Statistics: the number of patients who developed immune-related adverse events while using PD-1 inhibitors or PD-L1 inhibitors (a), the number of cancer patients who developed all non-immune-related adverse events while using PD-1 inhibitors or PD-L1 inhibitors (b), the number of all patients who developed immune-related adverse events while not using PD-1 inhibitors or PD-L1 inhibitors (c), and the number of all patients who developed all non-immune-related adverse events while not using PD-1 inhibitors or PD-L1 inhibitors (d);

(3) Calculate the odds ratio (ROR) of immune-related adverse reactions (a/b)/(c/d).

The immunotherapy-related reporting odds ratio (irROR) data of previously published clinical studies were collected.

S3. Collect the mRNA expression profile data of each tumor type from The Cancer Genome Atlas (TCGA) and calculate the median expression value of immunotherapy regulatory genes in various tumor types. The specific process includes:

(1) Download RNA sequencing data and clinical data of the TCGA Pan-Cancer dataset (recording the type of cancer to which each sample belongs) from Xena (https://xenabrowser.net/datapages/);

(2) Download the gene annotation data file (https://toil-xena-hub.s3.us-east-1.amazonaws.com/download/tcga_RSE M_gene_tpm.gz) and perform gene annotation on the RNA sequencing data;

(3) The annotated RNA sequencing data and clinical data were merged using R language, and the TCGA pan-cancer immune regulatory gene subset was selected. The median expression value of each immune regulatory gene in each cancer type was calculated using the median function.

S4. Perform correlation analysis on the median expression value of the immunotherapy regulatory gene and the odds ratio of immune-related adverse reactions and the objective response rate of immunotherapy to obtain the correlation relationship.

The pan-cancer analysis calculated the Spearman correlation coefficient and significance level of the median expression level of each immunotherapy gene, the odds ratio of immune-related adverse reactions of the tumor type, and the objective response rate of immunotherapy.

S5. Classify the immunotherapy regulatory genes based on the correlation relationship.

According to the correlation between each gene and the odds ratio of immune-related adverse reactions and the objective response rate of immunotherapy, immunotherapy regulatory genes were divided into four categories: efficacy-toxicity coupling genes (gene expression levels and the odds ratio of immune-related adverse reactions and the objective response rate of immunotherapy were significantly correlated), efficacy-specific genes (gene expression levels were significantly correlated with the objective response rate of immunotherapy, but not with the odds ratio of immune-related adverse reactions), toxicity-specific genes (gene expression levels were significantly correlated with the odds ratio of immune-related adverse reactions, but not with the objective response rate of immunotherapy) and irrelevant genes (gene expression levels were not significantly correlated with the objective response rate of immunotherapy and the odds ratio of immune-related adverse reactions). P < 0.05 was considered significant.

Embodiment 2

This embodiment provides a method for classifying immunomodulatory genes based on the relationship between immunotherapy efficacy and toxicity. The technical solution specifically includes the following contents:

Construction of an immunomodulatory gene classification method based on the relationship between immunotherapy efficacy and toxicity: RNA-seq data of baseline and early-stage tumor samples of patients receiving anti-PD-1 combined with anti-CTLA-4 immunotherapy were collected, and differential analysis was performed using the R language DESeq2 package. Genes with FDR q values <0.2 and P <0.05 were considered immunotherapy regulatory genes, as shown in (A) in Figure 1. Download TCGA pan-cancer mRNA expression profile data and calculate the average expression value of each gene in each tumor type; download the FARES real-world adverse reaction database and calculate the proportion of anti-PD-1 and anti-PD-L1 reports for each tumor type; summarize the objective response rates of immunotherapy for each tumor type reported previously. The pan-cancer analysis calculated the correlation between each gene and irORR and irROR. Based on the correlation between each gene and irROR and irORR, immunotherapy regulatory genes were divided into four categories: efficacy-toxicity coupling genes (gene expression levels were significantly correlated with both irORR and irROR), efficacy-specific genes (gene expression levels were significantly correlated with irORR and unrelated to irROR), toxicity-specific genes (gene expression levels were significantly correlated with irROR and unrelated to irORR) and unrelated genes (gene expression levels were not significantly correlated with either irORR or irROR), as shown in (B) in Figure 1.

As shown in Figure 2, the proportions of immunotherapy regulatory genes are: 26.20% efficacy-toxicity coupling genes, 9.17% efficacy-specific genes, 7.86% toxicity-specific genes, and 56.77% irrelevant genes. Among them, representative efficacy-toxicity coupling genes include IFNG, PRF1, CD8B, CXCL9 and other genes; representative toxicity-specific genes include CRTAM, LCP1, ABCB1 and other genes; and efficacy-specific genes include TIMM8A, HLA-C, HLA-F and other genes.

Example 1: Representative genes of efficacy-toxicity coupling genes include IFNG. The correlation between the gene expression and the odds ratio of immune-related adverse reactions and the objective response rate of immunotherapy was calculated. It can be seen that IFNG is positively correlated with both the efficacy (rho=0.6690; P=0.0012) and toxicity (rho=0.6287, P=0.0029) of immunotherapy, as shown in Figure 3.

Example 2: Representative toxicity-specific genes include ABCB1 and other genes. The correlation between the gene expression and the odds ratio of immune-related adverse reactions and the objective response rate of immunotherapy was calculated. It can be seen that ABCB1 is only negatively correlated with immunotherapy toxicity (rho = -0.4947, P = 0.0281), but has nothing to do with the efficacy (rho = -0.1679, P = 0.4793), as shown in Figure 4.

Example 3: A representative gene for efficacy-specific treatment is HLA-F. The correlation between the gene expression level and the odds ratio of immune-related adverse reactions and the objective response rate of immunotherapy was calculated. It can be seen that HLA-F is only positively correlated with the efficacy of immunotherapy (rho=0.5848, P=0.0067), but not with toxicity (rho=0.3007, P=0.1971), as shown in Figure 5.

The present invention uncouples the respective contributions of various immunotherapy regulatory genes to the efficacy and toxicity of immunotherapy, providing a molecular basis and potential therapeutic targets for screening potential beneficiaries of immunotherapy and high-risk groups for immune-related adverse reactions.

Among them, the screened immunotherapy toxicity-specific genes are used to predict immune-related adverse reactions.

The screened immunotherapy efficacy-specific genes are used to predict the objective response rate or overall survival and progression-free survival of immunotherapy.

The screened immunotherapy toxicity-specific genes are used as therapeutic targets for blocking adverse immune reactions.

The screened immunotherapy efficacy-specific genes are used as therapeutic targets for enhancing immunotherapy response or reversing immunotherapy resistance.

The basic principles, main features and advantages of the present invention are shown and described above. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims (6)

1. A method for classifying immunomodulatory genes based on the relationship between immunotherapy efficacy and toxicity, comprising: Obtain the pre-treatment transcriptome next-generation sequencing data and during-treatment next-generation sequencing data of several ICB-treated patients; Performing differential analysis on the transcriptome second-generation sequencing data before treatment and the second-generation sequencing data during treatment to obtain several immunotherapy regulatory genes; Obtain the odds ratio of immune-related adverse reactions and objective response rate of immunotherapy corresponding to each tumor type; Obtaining mRNA expression profile data of each tumor type in the cancer genome atlas, and obtaining the median expression value of the immunotherapy regulatory gene based on the mRNA expression profile data of each tumor type; Performing correlation analysis on the median expression value of the immunotherapy regulatory gene and the odds ratio of immune-related adverse reactions and the objective response rate of immunotherapy to obtain a correlation relationship; Classifying the immunotherapy regulatory genes based on the correlation relationship; The process of classifying the immunotherapy regulatory genes based on the correlation relationship comprises: When the median expression value of the immunotherapy regulatory gene is significantly correlated with the odds ratio of immune-related adverse reactions and the objective response rate of immunotherapy, the immunotherapy regulatory gene is an immunotherapy efficacy/toxicity coupling gene; When the median expression value of the immunotherapy regulatory gene is significantly correlated with the odds ratio of the immune-related adverse reaction and the median expression value of the immunotherapy regulatory gene is not related to the objective response rate of immunotherapy, the immunotherapy regulatory gene is an immunotherapy toxicity-specific gene; When the median expression value of the immunotherapy regulatory gene is unrelated to the odds ratio of the immune-related adverse reaction and the median expression value of the immunotherapy regulatory gene is significantly correlated with the objective response rate of immunotherapy, the immunotherapy regulatory gene is an immunotherapy efficacy-specific gene; When the median expression value of the immunotherapy regulatory gene is respectively unrelated to the immune-related adverse reaction odds ratio and the immunotherapy objective remission rate, the immunotherapy regulatory gene is an immunotherapy efficacy/toxicity-independent gene. 2. According to the immunomodulatory gene classification method based on the relationship between immunotherapy efficacy and toxicity according to claim 1, it is characterized in that before performing the difference analysis, it also includes using the R language DESeq2 package to perform FPKM standardization on the pre-treatment transcriptome second-generation sequencing data and the during-treatment group second-generation sequencing data. 3. The immunomodulatory gene classification method based on the relationship between immunotherapy efficacy and toxicity according to claim 1, characterized in that the process of obtaining immunotherapy regulatory genes comprises: The false discovery rate was obtained based on the difference analysis; Perform multiple testing correction on P values based on the false discovery rate; When the false discovery rate is less than 0.2 and the P value is less than 0.05, an immunotherapy regulatory gene is obtained. 4. The immunomodulatory gene classification method based on the relationship between immunotherapy efficacy and toxicity according to claim 1 is characterized in that the odds ratio of immune-related adverse reactions corresponding to each tumor type is obtained based on the calculation report of the disproportionality analysis method. 5. The immunomodulatory gene classification method based on the relationship between immunotherapy efficacy and toxicity according to claim 1, characterized in that the calculation process of the median expression value of the immunotherapy regulatory gene includes: Obtain RNA sequencing data of the pan-cancer dataset and clinical data of each tumor type; Performing gene annotation on the RNA sequencing data to obtain annotated RNA sequencing data; Merging the annotated RNA sequencing data and the clinical data based on R language to obtain merged data; The median function was used to calculate the median expression value of each immunotherapy regulatory gene in each cancer type in the merged data. 6. The immunomodulatory gene classification method based on the relationship between immunotherapy efficacy and toxicity according to claim 1 is characterized in that the correlation analysis includes calculating the Spearman correlation coefficient and significance level.