Statistically identifying tumor suppressors and oncogenes from pan-cancer genome-sequencing data. (25th July 2015)

• Record Type: Journal Article
• Authors: Kumar, Runjun D.; Searleman, Adam C.; Swamidass, S. Joshua; Griffith, Obi L.; Bose, Ron
• Is Part Of: Bioinformatics. Volume 31:Number 22(2015)
• Page Start: 3561
• Page End: 3568
• Publication Date: 2015-07-25
• Subjects: Bioinformatics -- Periodicals; Genomics -- Data processing -- Periodicals; Computational biology -- Periodicals; 572.80285
• DOI: 10.1093/bioinformatics/btv430
• Languages: English
• ISSNs: 1367-4803

Abstract

Abstract : Motivation : Several tools exist to identify cancer driver genes based on somatic mutation data. However, these tools do not account for subclasses of cancer genes: oncogenes, which undergo gain-of-function events, and tumor suppressor genes (TSGs) which undergo loss-of-function. A method which accounts for these subclasses could improve performance while also suggesting a mechanism of action for new putative cancer genes. Results: We develop a panel of five complementary statistical tests and assess their performance against a curated set of 99 HiConf cancer genes using a pan-cancer dataset of 1.7 million mutations. We identify patient bias as a novel signal for cancer gene discovery, and use it to significantly improve detection of oncogenes over existing methods (AUROC = 0.894). Additionally, our test of truncation event rate separates oncogenes and TSGs from one another (AUROC = 0.922). Finally, a random forest integrating the five tests further improves performance and identifies new cancer genes, including CACNG3, HDAC2, HIST1H1E, NXF1, GPS2 and HLA-DRB1. Availability and implementation : All mutation data, instructions, functions for computing the statistics and integrating them, as well as the HiConf gene panel, are available at www.github.com/Bose-Lab/Improved-Detection-of-Cancer-Genes . Contact : [e-mail] Supplementary information: Supplementary data are available at Bioinformatics online.