ASNEO: Identification of personalized alternative splicing based neoantigens with RNA-seq.

Aging
Zhanbing ZhangJing Yu

Abstract

Cancer neoantigens have shown great potential in immunotherapy, while current software focuses on identifying neoantigens which are derived from SNVs, indels or gene fusions. Alternative splicing widely occurs in tumor samples and it has been proven to contribute to the generation of candidate neoantigens. Here we present ASNEO, which is an integrated computational pipeline for the identification of personalized Alternative Splicing based NEOantigens with RNA-seq. Our analyses showed that ASNEO could identify neopeptides which are presented by MHC I complex through mass spectrometry data validation. When ASNEO was applied to two immunotherapy-treated cohorts, we found that alternative splicing based neopeptides generally have a higher immune score than that of somatic neopeptides and alternative splicing based neopeptides could be a marker to predict patient survival pattern. Our identification of alternative splicing derived neopeptides would contribute to a more complete understanding of the tumor immune landscape. Prediction of patient-specific alternative splicing neopeptides has the potential to contribute to the development of personalized cancer vaccines.

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Citations

Nov 7, 2020·Cancer Science·Sachie HashimotoHisato Hara
Dec 6, 2020·Journal of Hematology & Oncology·Mingye HongHua Zhang
May 1, 2021·Genes·Zhongjing Su, Dongyang Huang
May 31, 2021·Current Opinion in Pharmacology·Ernesto GuccioneSlim Mzoughi
Jul 31, 2021·Briefings in Bioinformatics·Amarinder Singh ThindBruce Ashford

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Datasets Mentioned

BETA
PRJNA398141
PXD007635
GSE78220

Methods Mentioned

BETA
RNA-seq
exome sequencing

Software Mentioned

Neopepsee
NetMHCpan
STAR
pTuneos
Comet
INTEGRATE
UCSC Table Brower
Ensembl genome browser
GATK
CloudNeo

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