Speakers
Description
Recent efforts to improve cancer treatment focus on identifying new therapeutic targets, enhancing delivery methods, and optimizing treatment combinations and sequencing to address the pronounced spatial and temporal heterogeneity of tumors. Mathematical models of cancer progression and therapeutic response have emerged as powerful tools for personalized tumor forecasting, adaptive treatment design, and optimization of dosing schedules to maximize efficacy while limiting adverse effects. However, many current modeling frameworks emphasize tumor response, treating toxicity through simplified formulations and relying on coarse pharmacokinetic/pharmacodynamic (PK/PD) descriptions. Nonetheless, the PK/PD literature offers a rich set of mathematical models that could substantially enhance tumor forecasting by more accurately capturing drug delivery, efficacy, and toxicity. Moreover, the growing availability of longitudinal, multimodal clinical data (from omics to imaging) calls for refined modeling and data assimilation pipelines to support reliable digital twins for clinical decision-making and experimental discovery. This minisymposium brings together a selected group of researchers advancing mathematical approaches to modeling treatment effects and toxicity across cancer sites and therapeutic modalities, highlighting recent progress and key open problems for the next generation of predictive cancer models.
