NeoHunter: Flexible software for systematically detecting neoantigens from sequencing data

Abstract

Complicated molecular alterations in tumors generate various mutant peptides. Some of these mutant peptides can be presented to the cell surface and then elicit immune responses, and such mutant peptides are called neoantigens. Accurate detection of neoantigens could help to design personalized cancer vaccines. Although some computational frameworks for neoantigen detection have been proposed, most of them can only detect SNV- and indel-derived neoantigens. In addition, current frameworks adopt oversimplified neoantigen prioritization strategies. These factors hinder the comprehensive and effective detection of neoantigens. We developed NeoHunter, flexible software to systematically detect and prioritize neoantigens from sequencing data in different formats. NeoHunter can detect not only SNV- and indel-derived neoantigens but also gene fusion- and aberrant splicing-derived neoantigens. NeoHunter supports both direct and indirect immunogenicity evaluation strategies to prioritize candidate neoantigens. These strategies utilize binding characteristics, existing biological big data, and T-cell receptor specificity to ensure accurate detection and prioritization. We applied NeoHunter to the TESLA dataset, cohorts of melanoma and non-small cell lung cancer patients. NeoHunter achieved high performance across the TESLA cancer patients and detected 79% (27 out of 34) of validated neoantigens in total. SNV- and indel-derived neoantigens accounted for 90% of the top 100 candidate neoantigens while neoantigens from aberrant splicing accounted for 9%. Gene fusion-derived neoantigens were detected in one patient. NeoHunter is a powerful tool to ‘catch all’ neoantigens and is available for free academic use on Github (XuegongLab/NeoHunter).

Keywords

cancer vaccine / molecular alteration / neoantigen / neoantigen prioritization

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