The Cancer Center at Illinois (CCIL), in collaboration with the National Center for Supercomputing Applications (NCSA), is pleased to announce the awardees of the 2025 Cancer Digital Insights (CDI) Planning Grants—a new initiative designed to accelerate cancer research at Illinois through discovery fueled by data science, artificial intelligence and machine learning.
The CDI Planning Grants are one-time awards intended to catalyze deep CCIL–NCSA collaborations that generate novel biological or physical insights into cancer mechanisms and treatment, leveraging unique datasets and advanced computational expertise. Each awarded project may receive up to $40,000 in non-faculty project funding from both CCIL and NCSA’s Health Innovation Program Office, and up to 20,000 GPU-hours on DeltaAI, a powerful supercomputing system at NCSA that supports the demanding computational needs of modern AI research. Teams will also receive additional support to effectively utilize NCSA resources.
Proposal Title: “An Interpretable AI Foundation Model for Predicting Immunotherapy Response across Cancer Types”
PI – Kun Wang (CCIL)
Co-PIs – Shirui Luo (NCSA) & Joseph Chan (Memorial Sloan Kettering Cancer Center)
This project will develop an interpretable AI model trained on cross-cancer immunotherapy datasets to improve prediction accuracy and generalizability across diverse patient cohorts—supporting more precise identification of who is most likely to benefit from immunotherapy.
Proposal Title: “A Spatially Aware AI-Augmented Retrieval Framework for Spatial Omics Data”
PI – Aiman Soliman (NCSA)
Co-PIs – Zeynep Madak-Erdogan (CCIL) & Volodymyr Kindratenko (NCSA)
This team will build a spatially aware retrieval-augmented AI framework paired with a high-throughput spatial omics pipeline to help researchers navigate and interpret large collections of spatial images and pathway patterns through efficient, conversational querying.
Proposal Title: “Virtual Spatial Proteomics for Colorectal Cancer Risk Stratification”
Yang Liu (CCIL) & Weihao Ge (NCSA), with Mohith Manjunath (NCSA) and team
This project will use label-free quantitative phase imaging (QPI) and AI to generate “virtual protein maps” as a more scalable alternative to high-cost spatial proteomics—advancing tools to identify recurrence-linked features in colon polyps and improve risk stratification.
What’s Next?
Awarded projects are getting underway, and the research teams will pursue proof-of-concept milestones over a one-year period, positioning the teams for future external funding and broader community impact.