Abstract 1215: pVACsplice: Predicting neoantigens from tumor-specific alternative splicing events derived from cis-acting regulatory mutations using whole exome and RNA sequencing data

Abstract

Abstract Neoantigens are tumor-specific peptides on the cell surface that can be recognized by the adaptive immune system. Personalized immunotherapies, such as cancer vaccines, rely on neoantigen prediction to identify sequences that can activate T cells to recognize and destroy the tumor. The majority of cancer vaccine trials have utilized neoantigens derived from missense mutations and small insertions and deletions. However, other mutation types could contribute to the overall neoantigen landscape, such as aberrantly spliced transcripts arising from cis-acting regulatory mutations. In this study, we explore the potential immunogenicity of alternative splicing events by creating pVACsplice, a tool to expand the capability of pVACtools, a suite of tools for neoantigen prediction (http://www.pvactools.org). pVACsplice assembles alternative transcripts from tumor-specific splicing patterns, identifies sequence changes by comparison to a reference, and predicts neoantigens from the novel regions. To verify the accuracy of alternative transcript assembly, we ran pVACsplice with HCC1395 cell line samples and performed long-read sequencing to detect the transcripts in vitro. Matched whole exome sequencing and RNA sequencing datasets from glioblastoma, melanoma, and colorectal cancer cohorts will also be analyzed with pVACsplice to obtain binding affinity estimates. We will compare these results to neoantigen predictions from other mutation sources and across cancer types to discover the prevalence of immunogenic splicing events. Finally, we will perform immunogenicity testing with a set of high quality candidates to validate our predictions. We hope to increase the number of candidates for patients’ vaccines by adding this functionality to our standard neoantigen prediction workflow. This tool could help generate a more accurate portrait of the neoantigen landscape in tumors, and in turn, enhance responses to personalized immunotherapies. Citation Format: Megan M. Richters, Kelsy C. Cotto, Susanna Kiwala, Huiming Xia, Beatriz M. Carreno, Gavin P. Dunn, Antoni Ribas, Obi L. Griffith, Malachi Griffith. pVACsplice: Predicting neoantigens from tumor-specific alternative splicing events derived from cis-acting regulatory mutations using whole exome and RNA sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1215.