111. pVACsplice: Predicting neoantigens from tumor-specific alternative splicing events derived from regulatory mutations

Aly Abdelkareem

Megan Richters

Megan Richters is a Ph.D. Candidate in the Molecular Genetics and Genomics program at Washington University in St. Louis. She graduated from the University of Louisiana at Monroe in 2014 with a B.S. in Biology. She is interested in assessing the impact of intratumoral heterogeneity on neoantigen prediction accuracy as well as exploring novel sources of neoantigens for personalized cancer vaccines.


Megan Richtersa, Kelsy Cottoa, Susanna Kiwalaa, Huiming Xiaa, Beatriz Carrenob, Gavin Dunna, Antoni Ribasc, Obi L. Griffitha, Malachi Griffitha

aWashington University School of Medicine, St. Louis, MO, USA; bUniversity of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; cUniversity of California Los Angeles, Los Angeles, CA, USA

Neoantigens are tumor-specific peptides presented on the cell surface by MHC 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 contribute to the overall neoantigen landscape, including aberrantly spliced transcripts arising from cis-acting regulatory mutations. In this study, we explore the potential immunogenicity of alternative splicing events and present 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 peptide sequences. Matched whole exome sequencing and RNA sequencing datasets from glioblastoma, melanoma, and colorectal cancer cohorts will 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 personalized 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.