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DrGaP: a powerful tool for identifying driver genes and pathways in cancer sequencing studies. Am J Hum Genet 2013 Sep 05;93(3):439-51

Date

08/21/2013

Pubmed ID

23954162

Pubmed Central ID

PMC3769934

DOI

10.1016/j.ajhg.2013.07.003

Scopus ID

2-s2.0-84883799242   41 Citations

Abstract

Cancers are caused by the accumulation of genomic alterations. Driver mutations are required for the cancer phenotype, whereas passenger mutations are irrelevant to tumor development and accumulate through DNA replication. A major challenge facing the field of cancer genome sequencing is to identify cancer-associated genes with mutations that drive the cancer phenotype. Here, we describe a powerful and flexible statistical framework for identifying driver genes and driver signaling pathways in cancer genome-sequencing studies. Biological knowledge of the mutational process in tumors is fully integrated into our statistical models and includes such variables as the length of protein-coding regions, transcript isoforms, variation in mutation types, differences in background mutation rates, the redundancy of genetic code, and multiple mutations in one gene. This framework provides several significant features that are not addressed or naively obtained by previous methods. In particular, on the observation of low prevalence of somatic mutations in individual tumors, we propose a heuristic strategy to estimate the mixture proportion of chi-square distribution of likelihood ratio test (LRT) statistics. This provides significantly increased statistical power compared to regular LRT. Through a combination of simulation and analysis of TCGA cancer sequencing study data, we demonstrate high accuracy and sensitivity in our methods. Our statistical methods and several auxiliary bioinformatics tools have been incorporated into a computational tool, DrGaP. The newly developed tool is immediately applicable to cancer genome-sequencing studies and will lead to a more complete identification of altered driver genes and driver signaling pathways in cancer.

Author List

Hua X, Xu H, Yang Y, Zhu J, Liu P, Lu Y

Author

Pengyuan Liu PhD Adjunct Professor in the Physiology department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Base Sequence
Computational Biology
Computer Simulation
Genes, Neoplasm
Humans
Models, Statistical
Mutation
Mutation Rate
Neoplasms
ROC Curve
Sequence Analysis, DNA
Signal Transduction
Software
jenkins-FCD Prod-484 8aa07fc50b7f6d102f3dda2f4c7056ff84294d1d