Kun Wang, Ph.D.
Assistant Professor of Computational Biology
Department of Comparative Biosciences
University of Illinois Urbana-Champaign
Seminar Title: Advancing Precision Immuno-oncology by Deciphering Cellular Immune Crosstalk
Abstract: Immune checkpoint therapy (ICB) shows great promise in immuno-oncology, but its efficacy varies due to differences in the Tumor Microenvironment (TME) among patients. To better understand the role of the TME in ICB responses, we developed computational tools and analyzed publicly available multi-modal transcriptomics data in clinical contexts. In this presentation, I’ll introduce four of these tools: CODEFACS (a tumor deconvolution tool), LIRICS (which prioritizes clinically relevant ligand-receptor interactions), IRIS (a machine learning model identifying ligand-receptor interactions linked to ICB resistance) and SPECIAL (a statistical method for identifying ligand-receptor interactions across tumor regions using spatial transcriptomics). Applying these tools to over 8,000 tumor samples from 21 cancer types revealed unique ligand-receptor interactions in microsatellite instability (MSI) tumors, which help explain their increased sensitivity to ICB therapy. Additionally, we found that ICB resistance may result from negative selection of chemotaxis-related interactions, shifting the TME from "hot" to "cold" post-therapy.
Bio: Before joining the Department of Comparative Biosciences as an Assistant Professor of Computational Biology, Kun obtained a Ph.D. in Computational Biology from the University of Maryland, College Park and completed postdoctoral training at the National Cancer Institute (NCI). Kun’s research focuses on developing computational methodologies, as well as AI and machine learning models, to advance translational cancer research and precision immuno-oncology by analyzing omics, imaging and clinical trial data.