Detection of candidate tumor driver genes using a fully integrated Bayesian approach. (18th December 2013)

• Record Type: Journal Article
• Authors: Yang, Jichen; Wang, Xinlei; Kim, Minsoo; Xie, Yang; Xiao, Guanghua
• Is Part Of: Statistics in medicine. Volume 33:Number 10(2014)
• Page Start: 1784
• Page End: 1800
• Publication Date: 2013-12-18
• Subjects: Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6066
• Languages: English
• ISSNs: 0277-6715

Abstract

<abstract abstract-type="main" id="sim6066-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="sim6066-para-0001">DNA copy number alterations (CNAs), including amplifications and deletions, can result in significant changes in gene expression and are closely related to the development and progression of many diseases, especially cancer. For example, CNA‐associated expression changes in certain genes (called candidate tumor driver genes) can alter the expression levels of many downstream genes through transcription regulation and cause cancer. Identification of such candidate tumor driver genes leads to discovery of novel therapeutic targets for personalized treatment of cancers. Several approaches have been developed for this purpose by using both copy number and gene expression data. In this study, we propose a Bayesian approach to identify candidate tumor driver genes, in which the copy number and gene expression data are modeled together, and the dependency between the two data types is modeled through conditional probabilities. The proposed joint modeling approach can identify CNA and differentially expressed genes simultaneously, leading to improved detection of candidate tumor driver genes and comprehensive understanding of underlying biological processes. We evaluated the proposed method in simulation studies, and then applied to a head and neck squamous cell carcinoma data set. Both simulation studies and data application show that the joint modeling approach can significantly improve the performance in identifying candidate tumor driver genes, when compared with other existing approaches. Copyright © 2013 John Wiley & Sons, Ltd.</p> </abstract>