Detection of tumor driver genes using a fully integrated Bayesian approach

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 tumor driver genes) can alter the expression levels of many downstream genes through transcription regulation, and cause cancer. Identification of such 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 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 (DE) genes simultaneously, leading to improved detection of tumor driver genes and comprehensive understanding of underlying biological processes. The proposed method was evaluated in simulation studies, and then applied to a head and neck squamous cell carcinoma (HNSCC) dataset. Both simulation studies and data application show that the joint modeling approach can significantly improve the performance in identifying tumor driver genes, when compared to other existing approaches. ; Non UBC ; Unreviewed ; Author affiliation: University of Texas Southwestern Medical Center ; Faculty