title: Statistically identifying tumor suppressors and oncogenes from pan-cancer genome-sequencing data.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  BACKGROUND
  rbose@dom.wustl.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Division of Oncology, Department of Medicine, Washington University School of Medicine, Computational and Systems Biology Program, Washington University in St Louis.
authors.forename: Runjun D
authors.initials: RD
authors.lastname: Kumar
authors.affiliation: Division of Oncology, Department of Medicine, Washington University School of Medicine.
authors.forename: Adam C
authors.initials: AC
authors.lastname: Searleman
authors.affiliation: Computational and Systems Biology Program, Washington University in St Louis, Department of Pathology and Immunology, Washington University School of Medicine and.
authors.forename: S Joshua
authors.initials: SJ
authors.lastname: Swamidass
authors.affiliation: Division of Oncology, Department of Medicine, Washington University School of Medicine, Division of Oncology, Department of Medicine, Washington University School of Medicine.
authors.forename: Obi L
authors.initials: OL
authors.lastname: Griffith
authors.affiliation: Division of Oncology, Department of Medicine, Washington University School of Medicine.
authors.forename: Ron
authors.initials: R
authors.lastname: Bose
mesh_terms: D019540:Area Under Curve; D030541:Databases, Genetic; D016147:Genes, Tumor Suppressor; D015894:Genome, Human; D006801:Humans; D008957:Models, Genetic; D009154:Mutation; D009369:Neoplasms / Q000235:genetics*; D009857:Oncogenes; D011786:Quality Control; D012372:ROC Curve; D015203:Reproducibility of Results; D017422:Sequence Analysis, DNA / Q000379:methods*; D013223:Statistics as Topic