Constructing tumor-specific gene regulatory networks based on samples with tumor purity heterogeneity

Pei Wang, PhD

Professor, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai

Recording

Constructing tumor-specific gene regulatory networks based on samples with tumor purity heterogeneity

Tumor tissue samples often contain an unknown fraction of normal cells. This problem well known as tumor purity heterogeneity (TPH) was recently recognized as a severe issue in omics studies. Specifically, if TPH is ignored when inferring co-expression networks, edges are likely to be estimated among genes with mean shift between normal and tumor cells rather than among gene pairs interacting with each other in tumor cells. To address this issue, we propose TSNet a new method which constructs tumor-cell specific gene/protein co-expression networks based on gene/protein expression profiles of tumor tissues. TSNet treats the observed expression profile as a mixture of expressions from different cell types and explicitly models tumor purity percentage in each tumor sample. The advantage of TSNet over existing methods ignoring TPH is illustrated through extensive simulation examples. We then apply TSNet to estimate tumor specific co-expression networks based on ovarian cancer expression profiles. We identify novel co-expression modules and hub structure specific to tumor cells.

Bio: Pei Wang is a Professor in Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai. Dr. Wang obtained her B.S. in Mathematics from Peking University, China, in 2000. She then pursued her graduate study in the U.S. and received a Ph.D. in Statistics from Stanford University in 2004. Between 2004-2013, Dr. Wang served as a faculty at Fred Hutchinson Cancer Research Center and University of Washington, Seattle, WA. In Oct 2013, she joined Icahn Medical School at Mount Sinai, New York. Dr. Wang’s research has been focused on developing statistical and computational methods to address scientific questions based on data from high throughput biology/genetics experiments as well as modern digital health studies. Dr. Wang and her team have developed numerous novel statistical methods for analyzing and integrating various genetic/genomic/proteomic data. In the past decade, Dr. Wang has been actively involved in the NCI funded CPTAC (Clinical Proteomic Tumor Analysis Consortium). Currently, Dr. Wang is the MPI of the national Proteomics and Genomics data analysis center of CPTAC.