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Bioinformatics. 2015 Nov 15;31(22):3561-8. doi: 10.1093/bioinformatics/btv430. Epub 2015 Jul 25.

Statistically identifying tumor suppressors and oncogenes from pan-cancer genome-sequencing data.

Author information

1
Division of Oncology, Department of Medicine, Washington University School of Medicine, Computational and Systems Biology Program, Washington University in St Louis.
2
Division of Oncology, Department of Medicine, Washington University School of Medicine.
3
Computational and Systems Biology Program, Washington University in St Louis, Department of Pathology and Immunology, Washington University School of Medicine and.
4
Division of Oncology, Department of Medicine, Washington University School of Medicine, Division of Oncology, Department of Medicine, Washington University School of Medicine.

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:

rbose@dom.wustl.edu

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
26209800
PMCID:
PMC4757952
DOI:
10.1093/bioinformatics/btv430
[Indexed for MEDLINE]
Free PMC Article

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