An integrated transcriptomic definition and therapeutic strategy for heterogeneous tumors

Speaker: Wenyi Wang, Ph.D.

Professor of Bioinformatics and Computational Biology and Biostatistics at the University of Texas MD Anderson Cancer Center

Abstract:

Cancer is characterized by vast transcriptional variations in genes and pathways. Cancer tissues are complex heterogeneous mixtures of epithelial, stromal, and immune cells, with each group comprising multiple distinct cell types and states. This heterogeneity has likely led to numerous contradictory findings in the literature over the past 30 years of high-throughput transcriptomic profiling of tumor tissues, thereby impeding a clearer understanding of cancer biology. Two approaches to address this issue are single-cell RNA-seq profiling and bulk RNA-seq deconvolution. Due to the higher cost and sample quality requirements of single-cell profiling, bulk RNA sequencing remains widely used for a vast amount of patient tissues. An important analytical challenge is how to utilize information from the two technologies to fully uncover the tumor-microenvironment (TME) landscape of cancer. In this talk, I will present our recently developed methods TmS for calculating tumor-specific total mRNA expression, at scale, in matched bulk RNA/DNA sequencing data (Cao et al. Nat Biotech 2022; Dai et al. Nature Reviews Cancer 2025; Dai et al., Cell Reports Medicine 2026) and TmSNet for TmS-informed neural network prediction. Our studies demonstrate the wide-open opportunities in delineating therapeutic resistance-specific tumor-immune dynamics across cancers using integrative deconvolution models

Speaker Bio:

Dr. Wenyi Wang is a Professor of Bioinformatics and Computational Biology at the University of Texas MD Anderson Cancer Center. She received her PhD in biostatistics from Johns Hopkins University and postdoctoral training in statistical genomics at UC Berkeley and genome technology at Stanford concurrently. Wenyi's research includes significant contributions to statistical bioinformatics in cancer, including MuSE for subclonal mutation calling and DeMixT for transcriptomic deconvolution. She currently serves on the inaugural executive committee for StatsUpAI, an initiative to empower statisticians in AI research and leadership.  Her current research programs are 1) multi-omic deconvolution to study DNA–RNA dynamics in cancer, and 2) cancer risk modeling using machine learning and Bayesian models.

Host: Chao Xing, Ph.D.