Kapur Receives CPRIT Award
Payal Kapur, M.D., and Satwik Rajaram, Ph.D., together with their teams, have received a new Cancer Prevention and Research Institute of Texas (CPRIT) award to develop a machine-learning infrastructure that helps guide treatment decisions for patients with metastatic kidney cancer.
Choosing between the two major classes of therapy for metastatic RCC remains challenging, and no validated biomarkers currently exist to support this decision. At the same time, much of the information that could help personalize therapy—particularly the biological cues within routine H&E pathology slides and the rich clinical details embedded in the electronic health record—remains underused in everyday practice.
This project builds on their recent work showing that digital pathology images contain reproducible signals related to tumor biology, including immune and angiogenic programs, and that machine-learning models can extract these signals with high accuracy. By combining these image-derived features with clinical information from the Kidney Cancer Explorer database, they aim to generate treatment-specific risk predictions that reflect the individual biology of each patient’s disease.
The study will use both real-world data and clinical trial datasets. Importantly, the project also includes a prospective evaluation in which model predictions will be integrated into a clinician-facing summary dashboard within the EHR to understand how these tools influence real-time decision-making.
Ultimately, the goal is to create a practical, interpretable, and clinically useful platform that improves treatment selection without requiring new assays, and additional cost. By making better use of data that already exist in routine care, this work aims to support more informed, patient-specific decisions for individuals facing metastatic kidney cancer.
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Dr. Kapur is a Professor in the Department of Pathology, and Dr. Rajaram is Assistant Professor in the Lyda Hill Department of Bioinformatics.