Cancer treatment response is often driven less by a tumor’s baseline molecular features than by how it changes after therapy begins, including shifts in cell state, pathway activity, and interactions with the tumor microenvironment. In this talk, I will draw on recent translational cancer studies from my group to show how mathematical approaches can be used to model these treatment-induced changes and translate complex experimental and clinical data into clinically meaningful insight. I will describe a math-to-medicine framework for characterizing tumor adaptation during therapy and for developing predictive biomarkers that support treatment selection for patients over time.
Copyright. All Rights Reserved.