title: pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens.
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 .
authors.affiliation: McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA. jhundal@genome.wustl.edu.
authors.forename: Jasreet
authors.initials: J
authors.lastname: Hundal
authors.affiliation: Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO, USA. bcarreno@DOM.wustl.edu.
authors.forename: Beatriz M
authors.initials: BM
authors.lastname: Carreno
authors.affiliation: McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA. apetti@genome.wustl.edu.
authors.forename: Allegra A
authors.initials: AA
authors.lastname: Petti
authors.affiliation: Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO, USA. glinette@DOM.wustl.edu.
authors.forename: Gerald P
authors.initials: GP
authors.lastname: Linette
authors.affiliation: McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA. ogriffit@genome.wustl.edu.|Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO, USA. ogriffit@genome.wustl.edu.|Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA. ogriffit@genome.wustl.edu.|Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA. ogriffit@genome.wustl.edu.
authors.forename: Obi L
authors.initials: OL
authors.lastname: Griffith
authors.affiliation: McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA. emardis@wustl.edu.|Department of Medicine, Division of Genomics and Bioinformatics, Washington University School of Medicine, St. Louis, MO, USA. emardis@wustl.edu.|Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA. emardis@wustl.edu.|Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA. emardis@wustl.edu.|Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO, USA. emardis@wustl.edu.
authors.forename: Elaine R
authors.initials: ER
authors.lastname: Mardis
authors.affiliation: McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA. mgriffit@genome.wustl.edu.|Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA. mgriffit@genome.wustl.edu.|Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA. mgriffit@genome.wustl.edu.
authors.forename: Malachi
authors.initials: M
authors.lastname: Griffith
mesh_terms: D000951:Antigens, Neoplasm / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D015894:Genome, Human; D006801:Humans; D009154:Mutation; D009369:Neoplasms / Q000235:genetics / Q000276:immunology*; D017422:Sequence Analysis, DNA / Q000379:methods*; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software
chemical_list: D000951:Antigens, Neoplasm
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