Gene Expression Deconvolution in Heterogenous Tumor Samples

• Activity Number: 466
• Type: Invited
• Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
• Sponsor: International Society for Bayesian Analysis (ISBA)
• Author(s): Wenyi Wang*+
• Companies: UT MD Anderson Cancer Center
• Keywords: differential expression ; cancer transcriptomes

Abstract

Clinically derived tumor tissues are often times made of both cancer and normal stromal cells. The expression measures of these samples are therefore partially derived from the non-tumor cells. This may explain why some previous studies have identified only a fraction of differentially expressed genes between tumor and normal samples. What makes the in silico estimation of mixture components more difficult is that the percentage of normal cells varies from one tissue sample to another. Until recently, there has been limited work on statistical methods development that accounts for tumor heterogeneity in gene expression data. To this end, we have built a Bayesian hierarchical model and developed a Markov chain Monte Carlo approach to simultaneously estimate, in each tumor sample, the normal cell fractions (i.e. level of stromal contamination), as well as cancer cell-specific gene expressions. We illustrate the performance of our model in synthetic data as well as in real data.