Analysis Of Transcriptome And Immune Microenvironment In Patients With Different Responses To Immune Checkpoint Blockade Therapy

Immune checkpoint blockade therapy plays an anti-cancer role by stimulating the body’s natural immune barrier,and is considered as a new and promising tumor treatment approach.However,the current response rate of patients to immune checkpoint blockade therapy is only about 20%.Finding the difference between treatment response and non-response patients,thus predicting the patient response to immune checkpoints treatment,is an important and urgent issue in current research and clinical practice.In recent years,there have been more and more studies on immune checkpoint blockade therapy,and some data have also been accumulated.The aim of this study is to better understand the differences in transcriptomes and tumor microenvironments between patients with different responses and to search for potential therapeutic response markers.We collected,curated and analyzed transcriptome sequencing data from five projects of pre-medication patients for a total of 174 samples.Among them,three projects are for melanoma PD1 blockade therapy samples,one for melanoma CTLA4 blockade therapy samples and one for gastric cancer PD1 blockade therapy samples.The original data of transcriptome sequencing were downloaded from the NCBI SRA and db GAP databases,and the clinical information of patients was obtained by reading the corresponding literature.According to the clinical information of above data,the samples were divided into two groups: response and non-response.We found that nine genes were significantly differentially expressed between these two groups in all five datasets.And eight homologous genes of the nine genes were verified in two sets of mouse data.In addition,we also compared the alternative splicing genes that differed between response and non-response samples in the five datasets,and found that five genes with differential RNA splicing at multiple sites.Then,in the perspective of immune microenvironment,we used the Immu Cell AI platform to predict the abundance of various immune cells in cancer samples and identified significant differences on dendritic cells,exhausted cells,natural killer cells,and Th2 cells between responsive and non-responsive samples.Finally we used the software CATT to predict the type and abundance of CDR3 sequences in TCRs(T cell receptors)in these samples,and revealed that the length distribution of TCR-CDR3 types was different between the response and non-response.This study analyzed the difference between patients with different response to immune checkpoint blockade treatment in the perspective of transcriptome,tumor microenvironment,and T cell receptor,and obtained multiple significantly different genes and immune components.Provide the theoretical basis for the precise treatment of immune checkpoint blockade.