PVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Hundal, Jasreet; Carreno, Beatriz; Petti, Allegra; Linette, Gerald; Griffith, Obi; Mardis, Elaine; Griffith, Malachi
• Is Part Of: Genome medicine. Volume 8:Issue 1(2016)
• Page Start: 1
• Page End: 11
• Publication Date: 2016-12
• Subjects: Genomics -- Periodicals; Medical genetics -- Periodicals; 616.042
• DOI: 10.1186/s13073-016-0264-5
• Languages: English
• ISSNs: 1756-994X

Abstract

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 .