Computational approaches to identify functional genetic variants in cancer genomes

• Published in: Nature methods Vol. 10; no. 8; pp. 723 - 729
• Main Authors: Gonzalez-Perez, Abel, Mustonen, Ville, Reva, Boris, Ritchie, Graham R S, Creixell, Pau, Karchin, Rachel, Vazquez, Miguel, Fink, J Lynn, Kassahn, Karin S, Pearson, John V, Bader, Gary D, Boutros, Paul C, Muthuswamy, Lakshmi, Ouellette, B F Francis, Reimand, Jüri, Linding, Rune, Shibata, Tatsuhiro, Valencia, Alfonso, Butler, Adam, Dronov, Serge, Flicek, Paul, Shannon, Nick B, Carter, Hannah, Ding, Li, Sander, Chris, Stuart, Josh M, Stein, Lincoln D, Lopez-Bigas, Nuria
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01-08-2013; Nature Publishing Group; Nature Research
• Subjects: 631/114; 631/1647/514/2184; 631/208/726/649; 631/67/69; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical Engineering/Biotechnology; Cancer; Cancer genomics; Computational biology; Computational Biology - methods; Computational biology and bioinformatics; Genetic aspects; Genetic variance; Genetic Variation; Genome, Human; Genomics; Genotype & phenotype; Humans; Life Sciences; Mutation; Neoplasms - genetics; perspective; Physiological aspects; Proteomics; Sequence annotation; Tumors; United States
• ISSN: 1548-7091; 1548-7105
• DOI: 10.1038/nmeth.2562

International Cancer Genome Consortium members review and recommend computational approaches for identifying mutations that drive cancer progression from among the many sequence variants present in tumor genomes.
The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype.