Title |
pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens
|
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Published in |
Genome Medicine, January 2016
|
DOI | 10.1186/s13073-016-0264-5 |
Pubmed ID | |
Authors |
Jasreet Hundal, Beatriz M. Carreno, Allegra A. Petti, Gerald P. Linette, Obi L. Griffith, Elaine R. Mardis, Malachi Griffith |
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 . |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 16 | 48% |
United Kingdom | 4 | 12% |
Germany | 1 | 3% |
India | 1 | 3% |
Canada | 1 | 3% |
France | 1 | 3% |
Austria | 1 | 3% |
Unknown | 8 | 24% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 21 | 64% |
Members of the public | 11 | 33% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 1% |
Korea, Republic of | 1 | <1% |
United Kingdom | 1 | <1% |
Canada | 1 | <1% |
Germany | 1 | <1% |
Belgium | 1 | <1% |
Taiwan | 1 | <1% |
Japan | 1 | <1% |
China | 1 | <1% |
Other | 0 | 0% |
Unknown | 452 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 113 | 24% |
Student > Ph. D. Student | 90 | 19% |
Student > Master | 45 | 10% |
Student > Bachelor | 30 | 6% |
Other | 26 | 6% |
Other | 70 | 15% |
Unknown | 91 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 117 | 25% |
Agricultural and Biological Sciences | 86 | 18% |
Immunology and Microbiology | 49 | 11% |
Medicine and Dentistry | 46 | 10% |
Computer Science | 29 | 6% |
Other | 37 | 8% |
Unknown | 101 | 22% |