Neoantigen vaccination is an emerging modality of cancer immunotherapy with many ongoing trials. One central question of neoantigen vaccination is the method for selecting which mutated tumor-specific antigens to include in a patient’s vaccine. Many in-silico pipelines for neoantigen selection have been published in the past few years, but no comprehensive evaluation has compared them directly on the same tumor/normal sequencing data. We evaluate several publicly available commonly used neoantigen pipelines (pVACtools [1], MuPeXI [2], TIminer [3], OpenVax [4]) on both murine and human cancer samples. Our evaluation highlights the salient differences between these pipelines and shows the divergent results they achieve. References: 1. Kiwala S, Hundal J, …, Griffith M. pVACtools: Computational selection and visualization of neoantigens for personalized cancer vaccine design. Cancer Genetics 2018. 2. Bjerregaard A-M, Nielsen M, ..., Eklund AC. MuPeXI: Prediction of neo-epitopes from tumor sequencing data. Cancer Immunology Immunotherapy 2018. 3. Tappeiner E, Finotello F, ..., Trajanoski Z. TIminer: NGS data mining pipeline for cancer immunology and immunotherapy. Bioinformatics 2017. 4. Rubinsteyn A, Kodysh J, …, Hammerbacher J. Computational pipeline for the PGV-001 Neoantigen Vaccine Trial. Frontiers in Immunology 2018.

Citation Format: Julia Kodysh, John P. Finnigan, Alex Rubinsteyn. Evaluation of tools for predicting mutated tumor antigens from exome and RNA sequencing [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B079.

©2019 American Association for Cancer Research.
2019
American Association for Cancer Research.