Abstract 5036: pVACview: An integrative visualization tool for efficient neoantigen prioritization and selection
Authors: Xia Huiming , Kiwala Susanna , Skidmore Zachary L. , Fisk Bryan , Goedegebuure S. Peter , Miller Christopher A. , Gillanders William E. , Griffith Obi L. , Griffith Malachi 
Published: June 2022

Neoantigen vaccines have demonstrated strong efficacy in the treatment of cancers in the realm of personalized medicine when used in combination with checkpoint blockade therapy. Numerous clinical trials involving these therapies are underway across the world. Accurate identification and prioritization of neoantigens is highly relevant to the design of these trials, and for predicting treatment response and understanding mechanisms of resistance. With the advent of whole exome and RNA sequencing technologies, researchers and clinicians are now able to computationally predict neoantigens and design personalized vaccines based on patient-specific mutation information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized medicine, including somatic variant identification and expression, peptide processing and transportation, peptide-MHC binding and stability, recognition by cytotoxic T cells, etc. There has been a rapid development of computational tools that attempt to account for these complexities. Pipelines have been developed to allow researchers to run an ensemble of many potentially informative tools for individual patients. However, output results from these complex pipelines are often unorganized, difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. Additionally, while current pipelines analyze neoantigens from the perspective of variants, transcripts and peptides, they fail to present the underlying details for efficient prioritization of the candidates. In addition to variant detection, gene expression and predicted peptide binding affinities, recent studies have also shown the importance of mutation location, allele-specific anchor locations, and variation of T-cell response to long versus short peptides. Due to the intricate nature of neoantigen features, presenting all relevant information to experimentalists and immunogenomics tumor boards for candidate selection is a difficult challenge. In response, we have created pVACview, an interactive tool designed to aid in the prioritization and selections of neoantigen candidates for personalized cancer vaccines. pVACview has a user-friendly and intuitive interface where users can upload, explore, select and export their neoantigen candidates. The tool allows users to visualize neoantigen candidates across three different levels, including variant, transcript and peptide level information. We hope pVACview will allow researchers and clinicians to analyze and prioritize neoantigen candidates with greater efficiency and accuracy. The application is available as part of the pVACtools pipeline at https://github.com/griffithlab/pVACtools and the online server can be accessed at pvacview.org.

Citation Format: Huiming Xia, Susanna Kiwala, Zachary L. Skidmore, Bryan Fisk, S. Peter Goedegebuure, Christopher A. Miller, William E. Gillanders, Obi L. Griffith, Malachi Griffith. pVACview: An integrative visualization tool for efficient neoantigen prioritization and selection [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 5036.