Comprehensive analysis of neoantigens derived from structural variation across whole genomes from 2528 tumors

Genome Biology

Research

Biyang Jing¹  Yang Shi²  Ruibin Xi³ 

[1] School of Life Sciences, Peking University, Beijing, China;School of Mathematical Sciences, Peking University, Beijing, China;School of Mathematical Sciences, Peking University, Beijing, China;Center for Statistical Science, Peking University, Beijing, China;

关键词: Neoantigen;    Structural variation;    Immunotherapy;    Bioinformatics;    Cancer vaccine;    Tumor microenvironment;   

DOI  :  10.1186/s13059-023-03005-9

 received in 2022-09-12, accepted in 2023-07-02,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundNeoantigens are critical for anti-tumor immunity and have been long-envisioned as promising therapeutic targets. However, current neoantigen analyses mostly focus on single nucleotide variations (SNVs) and indel mutations and seldom consider structural variations (SVs) that are also prevalent in cancer.ResultsHere, we develop a computational method termed NeoSV, which incorporates SV annotation, protein fragmentation, and MHC binding prediction together, to predict SV-derived neoantigens. Analysis of 2528 whole genomes reveals that SVs significantly contribute to the neoantigen repertoire in both quantity and quality. Whereas most neoantigens are patient-specific, shared neoantigens are identified with high occurrence rates in breast, ovarian, and gastrointestinal cancers. We observe extensive immunoediting on SV-derived neoantigens, especially on clonal events, which suggests their immunogenic potential. We also demonstrate that genomic alteration-related neoantigen burden, which integrates SV-derived neoantigens, depicts the tumor-immune interplay better than tumor neoantigen burden and may improve patient selection for immunotherapy.ConclusionsOur study fills the gap in the current neoantigen repertoire and provides a valuable resource for cancer vaccine development.

【 授权许可】

CC BY   
© The Author(s) 2023

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