DIPAN: Detecting personalized intronic polyadenylation derived neoantigens from RNA sequencing data

Abstract

Intronic polyadenylation (IPA) refers to a particular type of alternative polyadenylation where a gene makes use of a polyadenylation site located within its introns. Aberrant IPA events have been observed in various types of cancer. IPA can produce noncoding transcripts or truncated protein-coding transcripts with altered coding sequences in the resulting protein product. Therefore, IPA events hold the potential to act as a reservoir of tumor neoantigens. Here, we developed a computational method termed DIPAN, which incorporates IPA detection, protein fragmentation, and MHC binding prediction to predict IPA-derived neoantigens. Utilizing RNA-seq from breast cancer cell lines and ovarian cancer clinical samples, we demonstrated the significant contribution of IPA events to the neoantigen repertoire. Through mass spectrometry immunopeptidome analysis, we further illustrated the processing and presentation of IPA-derived neoantigens on the surface of cancer cells. While most IPA-derived neoantigens are sample-specific, shared neoantigens were identified in both cancer cell lines and clinical samples. Furthermore, we demonstrated an association between IPA-derived neoantigen burden and overall survival in cancer patients.