Xi Ren,Bin Zhang,Jia Li,Thamizhanban Manoharan,Beijia Liu,Yangyang Song,Shuye Tian,Kar-Tong Tan,Ling‐Wen Ding,Ying Li,Ömer An,Ming Li,Chan-Shuo Wu,Yang Liu,Boon Heng Dennis Teo,Sze Jing Tang,Jinhua Lu,Yuhui Hu,Wei Chen,Leilei Chen,Gloryn Chia,Henry Yang
出处
期刊:Research Square - Research Square日期:2022-04-26被引量:3
标识
DOI:10.21203/rs.3.rs-1537870/v1
摘要
Abstract Tumor-specific neoantigens have emerged as a promising source for cancer immunotherapy. These tumor-specific neoantigens could arise from somatic mutations, aberrant splicing and RNA editing. Since intronic polyadenylation has similar potential as mutations to generate tumor-specific transcripts and peptides, it may serve as another neoantigen source, which has not been explored. We developed a novel computational pipeline for identification of tumor-specific transcripts and their translated neoantigens derived from intronic polyadenylation. Applying it to RNA-seq data from 5,654 tumor samples of various cancers and 11,000 + normal samples of different tissues, we observed widespread tumor-specific intronic polyadenylated transcripts and their corresponding neoantigens. In addition, we also discovered complementary effects of neoantigens derived from different sources, identified neoantigens arising from recurrent intronic polyadenylated transcripts, and validated their immunogenicity. Here, we have demonstrated that we were able to identify and predict neoantigens from intronic polyadenylated transcripts using RNA sequencing data, hence, allowing us to explore such neoantigens as potential candidates for cancer immunotherapy.