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- Title
pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens.
- Authors
Hundal, Jasreet; Carreno, Beatriz M.; Petti, Allegra A.; Linette, Gerald P.; Griffith, Obi L.; Mardis, Elaine R.; 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 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.
- Subjects
CANCER treatment; IMMUNOTHERAPY; T cells; SEQUENCE analysis; CELLULAR therapy
- Publication
Genome Medicine, 2016, Vol 8, p1
- ISSN
1756-994X
- Publication type
Article
- DOI
10.1186/s13073-016-0264-5