Deep-learning–driven Tumor Microenvironment Profiling Improves Immunotherapy Response Prediction

Ruohan Wang, PhD - Stanford University
Postdoctoral Scholar (Gentles Lab)
 

Wednesday, September 23, 2026
3:00 - 4:00 PM  
James H. Clark Center, Room S360, 3rd floor 
 

Abstract:  

Immunotherapy benefits only a fraction of cancer patients, and we still lack reliable ways to predict who will respond. A key reason is that response depends not on any single biomarker but on the collective behavior of diverse cell populations within the tumor microenvironment (TME), communicating through structured signaling networks that existing methods do not capture.We developed EcoNet, a framework that first profiles the TME as co-occurring cell-state ecosystems (ecotypes), then reconstructs the intercellular and intracellular signaling networks connecting them, and feeds these networks into a graph attention model to predict immunotherapy outcome from bulk RNA-seq. Pretrained on 2,532 pan-cancer immunotherapy samples, EcoNet outperformed existing prediction tools by 7.5 to 19.9% and marker-based signatures by 4.4 to 21.2% in AUC. Its high-attention genes were independently validated by a CRISPR screen for T-cell-mediated tumor killing. Applied to clear cell renal cell carcinoma with disease-specific fine-tuning, EcoNet stratified an independent cohort by progression-free survival (P=0.024) and identified an interferon-responsive program linking antigen presentation, immune infiltration, and checkpoint regulation. We further validated these findings using CosMx single-cell spatial transcriptomics on a 46-sample clinical cohort (~450,000 cells), confirming that the therapy-beneficial TME configuration localizes to immune-enriched tissue niches.EcoNet provides a generalizable, biologically grounded approach to predicting immunotherapy response and is readily extensible to other cancers and therapeutic settings.

Biography:  

Ruohan Wang is a postdoctoral researcher in Andrew Gentles' lab at Stanford University School of Medicine, where she studies how tumors interact with the immune system and works on predicting immunotherapy response. She builds AI and computational tools to analyze genomic data, with a focus on single-cell and spatial transcriptomics. Before Stanford, she earned her PhD in Computer Science from City University of Hong Kong, where she developed algorithms and deep learning models for phage genomics and single-cell multi-omics integration, with work published in Nature Biotechnology, Nature Communications, and Nucleic Acids Research, among others.