Wubing Zhang - 02/18/2026

Wubing Zhang, PhD - Stanford University

 

Wednesday, February 18, 2026
3:00 - 4:00 PM  
James H. Clark Center, Room S360, 3rd floor 
 

Abstract:  Multicellular programs in the tumor microenvironment (TME) drive cancer pathogenesis and response to therapy but remain challenging to identify and profile clinically. Here, we present a machine learning framework for multi-analyte profiling of spatially dependent cell states and multicellular ecosystems, termed spatial ecotypes (SEs). By integrating 10M single-cell and spot-level spatial transcriptomes from diverse human carcinomas and melanomas, we identified nine SEs with broad conservation—each with unique biology, geospatial features, and clinical outcome associations, including several linked to immunotherapy response. Notably, SEs were distinguishable by DNA methylation profiling and recoverable from plasma cell-free DNA (cfDNA) using deep learning. In cfDNA from nearly 100 melanoma patients, SE levels exhibited striking associations with immunotherapy response. Our data reveal fundamental units of TME organization and demonstrate a multimodal platform for solid and liquid TME profiling, with implications for improved risk stratification and therapy personalization.

Biography:  Wubing Zhang is a postdoctoral scholar in Dr. Aaron Newman lab. His research focuses on developing computational methods and integrating large-scale single-cell and spatial genomics data to characterize cellular ecosystems in the tumor microenvironment, with the goal of discovering novel biomarkers and therapeutic targets to advance precision cancer medicine.