pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens.

Genome Medicine
Jasreet HundalMalachi Griffith

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 of personalized Variant Antigens by Cancer Sequencing (pVAC-Seq) that integrates tumor mutation and expression data (DNA- and RNA-Seq). pVAC-Seq is available at https://github.com/griffithlab/pVAC-Seq .

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Methods Mentioned

BETA
RNA-Seq
pVAC-Seq
genotyping
exome sequencing
Whole Genome Sequencing
RNA-CapSeq

Clinical Trials Mentioned

NCT00683670

Software Mentioned

Samtools
Galaxy
Picard
NovoAlign
EpiToolKit
GMS ‘ snp - filter
Strelka
RNA
GMS
Tophat

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