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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 .

SUBMITTER: Hundal J 

PROVIDER: S-EPMC4733280 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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

Hundal Jasreet J   Carreno Beatriz M BM   Petti Allegra A AA   Linette Gerald P GP   Griffith Obi L OL   Mardis Elaine R ER   Griffith Malachi M  

Genome medicine 20160129 1


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  ...[more]

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