Abstract A09: A bioinformatics workflow to identify neoantigens using next-generation sequencing

Abstract

Abstract Immuno-oncology is an emerging field of research that represents tremendous promise for cancer patients. Accumulating evidence suggests that tumor-specific neoantigens can persistently stimulate cytotoxic T lymphocytes, which may eradicate cancer cells in a favorable immune microenvironment. Next-generation sequencing (NGS) is a key technology enabling advances in this field. Here we present a complete bioinformatics workflow using tumor/normal whole-exome sequencing (WES) and tumor-only whole-transcriptome sequencing (WTS) to predict neoantigens presented by cancer cells. We evaluate existing WES probe designs for MHC class I genes and show that accurate four-digit HLA typing can be achieved using standard WES solutions, thus allowing for a simplified experimental workflow by removing the need for targeted HLA assays. Final neoantigen candidates are prioritized based on in silico peptide-HLA binding predictions, variant allele frequencies for clonal somatic mutations, tumor RNA expression, and self-similarities between mutant peptide to wildtype peptide. This workflow will be available through BaseSpace® Sequence Hub, which can be accessed via an intuitive web-based interface or Linux-based command line tool, providing any researcher with rapid analysis of NGS data and neoantigen predictions. Citation Format: Shile Zhang, Alex So, Shannon Kaplan, Kristina Kruglyak. A bioinformatics workflow to identify neoantigens using next-generation sequencing. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2016 Oct 20-23; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2017;5(3 Suppl):Abstract nr A09.