Research Paper Volume 12, Issue 14 pp 14633—14648
ASNEO: Identification of personalized alternative splicing based neoantigens with RNA-seq
- 1 Department of Ophthalmology, Shanghai Tenth People’s Hospital, Tongji University, School of Medicine, Tongji University, Shanghai 200009, China
- 2 Department of Endocrinology and Metabolism, Shanghai Tenth People's Hospital; Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200009, China
- 3 Department of Gastroenterology, Shanghai Tenth People's Hospital; Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200009, China
- 4 School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Received: March 19, 2020 Accepted: June 4, 2020 Published: July 22, 2020https://doi.org/10.18632/aging.103516
How to Cite
Copyright © 2020 Zhang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.