neoepiscope Improves Neoepitope Prediction with Multi-variant Phasing

Abstract

The vast majority of tools for neoepitope prediction from DNA sequencing of complementary tumor and normal patient samples do not consider germline context or the potential for co-occurrence of two or more somatic variants on the same mRNA transcript. Without consideration of these phenomena, existing approaches are likely to produce both false positive and false negative results, resulting in an inaccurate and incomplete picture of the cancer neoepitope landscape. We developed neoepiscope chiefly to address this issue for single nucleotide variants (SNVs) and insertions/deletions (indels), and herein illustrate how germline and somatic variant phasing affects neoepitope prediction across multiple datasets. We estimate that up to [~]5% of neoepitopes arising from SNVs and indels may require variant phasing for their accurate assessment. neoepiscope is performant, flexible, and supports several major histocompatibility complex binding affinity prediction tools. We have released neoepiscope as open-source software (MIT license, https://github.com/pdxgx/neoepiscope) for broad use.\n\nKEY POINTSO_LIGermline context and somatic variant phasing are important for neoepitope prediction\nC_LIO_LIMany popular neoepitope prediction tools have issues of performance and reproducibility\nC_LIO_LIWe describe and provide performant software for accurate neoepitope prediction from DNA-seq data\nC_LI