Applied Mathematics Group

Patient-derived Spatial Modeling of Tumor Progression under Drug Combinations

The cancer tumor microenvironment is composed of different cell types (tumor cells, fibroblasts, blood vessels, and immune cells) that interact via intercellular signaling to determine tumor growth and fate. These complex and patient-specific dynamics may require the application of multiple drugs, known as combination therapies, to afford successful treatment. Simulating tumor growth at this level requires spatial agent-based modeling of individual cells as they are distributed within the tumor, as well as their signaling pathways. In a multi-institutional collaboration led by the Oregon Health & Science University, we will investigate the ability to calibrate such spatial tumor models to experimental data as a first step in determining the clinical utility of this modeling approach to recommend drug combinations.