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

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…

• NI
  National Institutes of Health

  Awards: U54 HG003079, HG003079
• NH
  National Human Genome Research Institute

  Awards: NIH NHGRI U54 HG003079, U54 HG003079, HG003079, HG007940, NHGRI U54 HG003079, K99 HG007940
• NC
  National Cancer Institute

  Awards: R21 CA179695, K22 CA188163, CA188163, U54 HG003079