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Genome Med. 2016 Jan 29;8(1):11. doi: 10.1186/s13073-016-0264-5.

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

Hundal J1, Carreno BM2, Petti AA3, Linette GP4, Griffith OL5,6,7,8, Mardis ER9,10,11,12,13, Griffith M14,15,16.

Author information

1
McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA. jhundal@genome.wustl.edu.
2
Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO, USA. bcarreno@DOM.wustl.edu.
3
McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA. apetti@genome.wustl.edu.
4
Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO, USA. glinette@DOM.wustl.edu.
5
McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA. ogriffit@genome.wustl.edu.
6
Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO, USA. ogriffit@genome.wustl.edu.
7
Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA. ogriffit@genome.wustl.edu.
8
Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA. ogriffit@genome.wustl.edu.
9
McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA. emardis@wustl.edu.
10
Department of Medicine, Division of Genomics and Bioinformatics, Washington University School of Medicine, St. Louis, MO, USA. emardis@wustl.edu.
11
Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA. emardis@wustl.edu.
12
Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA. emardis@wustl.edu.
13
Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO, USA. emardis@wustl.edu.
14
McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA. mgriffit@genome.wustl.edu.
15
Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA. mgriffit@genome.wustl.edu.
16
Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA. mgriffit@genome.wustl.edu.

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 .

PMID:
26825632
PMCID:
PMC4733280
DOI:
10.1186/s13073-016-0264-5
[Indexed for MEDLINE]
Free PMC Article

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