Tumor neoantigen screening method fused with single cell TCR sequencing data

Abstract

The invention discloses a tumor neoantigen screening method fused with single cell TCR sequencing data. The tumor neoantigen screening method comprises the following steps: based on whole exon sequencing data and transcriptome sequencing data, carrying out quality control, comparison and other steps through software to obtain a neomutant peptide library; predicting an HLA-I type typing by using HLA typing prediction software; in combination with single-cell TCR sequencing and single-cell transcriptome sequencing, finding cancer-specific CD8 + T cell receptors through cell type annotation and clone frequency analysis; meanwhile, based on integrated deep learning, the short peptide immunogenicity is identified through a peptide-TCR interaction prediction model, a tumor neoantigen screening method fused with single cell TCR sequencing data is provided, and the problems that a traditional tumor antigen screening method is high in neoantigen misselection and missed selection rate, insufficient in immunogenicity and the like are solved.

Description

technical field [0001] The invention relates to the field of tumor therapeutic vaccines, in particular to a tumor neoantigen screening method that combines single-cell TCR sequencing data. Background technique [0002] The occurrence of tumors is often accompanied by mutations of multiple genes. Neoantigens refer to epitope-specific antigens produced by tumor cell mutations, which are only expressed on tumor cells and will not lead to immune tolerance of the body. During tumor immunotherapy, T cells play an indispensable role in monitoring the killing of pathological cells. T cell receptors (TCRs) on the surface of T cells recognize short peptides presented by HLA proteins to respond to threats in an antigen-specific manner. Classical CD8+ T cells, also known as cytotoxic T cells, recognize short peptides presented by HLA class I (HLA I) molecules, whereas CD4+ T cells only recognize peptides presented by HLA class II (HLA II) molecules. This process is known as antigen re...

AI Technical Summary

Problems solved by technology

However, most neoantigens predicted by current neoantigen recognition algorithms fail to elicit an immune response in vivo, and identification of immunogenic peptides remains an unsolved problem

The reason is that the two characteristics associated with immunogenicity are the stability of the pHLA complex and the functional affinity of the T cell receptor (TCR) that interacts with it, and the current algorithm can only measure the stability of the pHLA complex, but not Predicting interactions of T cell receptors with antigenic peptides

Antigenic peptides interact with TCRs with lower affinity than antibodies and their ligands, requiring particularly sensitive biochemical techniques for detection

Moreover, experimental testing of all potentially immunogenic peptides is time-consuming, labor-intensive, and expensive

Images