PVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens. Issue 1 (December 2016) - Hundal, Jasreet; Carreno, Beatriz; Petti, Allegra; Linette, Gerald; Griffith, Obi; Mardis, Elaine; Griffith, Malachi
Abstract Cancer immunotherapy has gained significant momentum from recent clinical successes of checkpoint blockade inhibition. Massively parallel sequence analysis suggests a connection between mutational load and response to this class of therapy. Methods to identify which tumor-specific mutant peptides (neoantigens) can elicit anti-tumor T cell immunity are needed to improve predictions of checkpoint therapy response and to identify targets for vaccines and adoptive T cell therapies. Here, we present a flexible, streamlined computational workflow for identification ofp ersonalizedV ariantA ntigens byC ancerSeq uencing (pVAC-Seq) that integrates tumor mutation and expression data (DNA- and RNA-Seq). pVAC-Seq is available athttps://github.com/griffithlab/pVAC-Seq .