Researchers at the Cancer Center at Illinois (CCIL) are developing an artificial intelligence model to predict how patients with different cancer types may respond to immunotherapy. Accurately identifying patients who are most likely to benefit from treatment is an important part of advancing precision cancer immunotherapy.
A project led by CCIL member Kun Wang earned seed funding from a Cancer Digital Insights Planning Grant from the CCIL to perform this research. The CCIL’s internal seed grants continue to spark innovative, impactful cancer-engineering driven research directions.
Wang’s project focuses on developing an interpretable AI foundation model that can predict response to immunotherapy across multiple cancer types.
The model is designed to predict response to immune checkpoint blockade (ICB) therapy, while also helping researchers better understand the biological mechanisms connected to treatment outcomes.
“Our primary goals are to develop an AI foundation model capable of accurately predicting response to ICB therapy across diverse cancer types and to provide robust biological interpretations that reveal cancer-specific mechanisms associated with treatment outcomes,” Wang said.
Cancer Center at Illinois member Kun Wang earned a CCIL Planning Grant to develop a new AI model for immunotherapy research
The model could also provide a broader framework for studying therapeutic response beyond cancer immunotherapy.
The CCIL grant has already supported progress in collecting patient data and developing the model.
“We have successfully collected and curated the necessary patient datasets and developed a prototype model that already outperforms current state-of-the-art approaches,” Wang said. “We are continuing to refine the model architecture and training strategies to further improve its performance.”
For Wang, the project is part of a larger effort to improve precision immuno-oncology through more accurate predictive tools.
“My research focuses on precision immuno-oncology, where the development of accurate predictive biomarkers and response models is critical for improving patient outcomes,” Wang said.
The project aligns with that broader vision by working to identify patients who are most likely to benefit from immune checkpoint blockade therapy.
Looking ahead, Wang said the model could become a useful tool for advancing precision cancer care.
“Successful completion of this work can provide a powerful tool for ICB response prediction and contribute significantly to advancing precision cancer immunotherapy,” Wang said.
Editor’s notes:
This story was written by Hailee Munno, CCIL Communications intern.
Kun Wang is an assistant professor in the Department of Comparative Biosciences and Bioengineering (affiliated) and a member of the Cancer Center at Illinois (CCIL) in the Cancer Technology and Data Science research program at the University of Illinois Urbana-Champaign.
He can be reached at kwang222@illinois.edu.