pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens
By Jasreet Hundal, B. Carreno, Allegra Petti, G. Linette, Obi L. Griffith, Elaine R. Mardis, Malachi Griffith. Genome Medicine, 2016
392 citations recorded by the source corpus.
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.
Subfields: Immunology, Molecular Biology, Oncology
View Paper: https://www.semanticscholar.org/paper/d3f7d2dbac49e5bbdbc8e0b99b503a0093bcb956
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- Swapnil Mahajan
- Sandeep Kumar Dhanda
Citations in the corpus
- IEDB-AR: immune epitope database—analysis resource in 2019 (2019, methodology): “As an example, a separate resource queue has been configured in collaboration with the Griffith lab to support the CPU-intensive requests of their pVACSeq pipeline (21) while keeping the IEDB systems responsive to requests from other users.”