Group Leader: Quantitative Personalized Oncology Lab @ Moffitt Cancer Center
Tampa, Florida, USAKeywords (click a keyword to view other similar labs): theoretical • experimental • Quantitative Personalized Oncology • cancer modeling • radiotherapy • immunotherapy • patient-specific mathematical biomarkers.
Description: Incremental improvements in cancer therapy with each iteration of clinical trials have continued because they have produced reasonable outcomes. The fields of cancer research and cancer therapy are collecting a wealth of data that both support and challenge our understanding of the biology underlying the responses seen in the clinic. Of utmost importance in the immediate future becomes the search for patient-specific treatment combinations. Only few protocols have been modeled experimentally due to logistic limitations, and even fewer have been evaluated prospectively in the clinic. To fully decipher the complex dynamics of cancer therapy, a concerted effort is needed that integrates disciplines that have not traditionally been consulted in experimental design and clinical studies. Mathematical modeling may provide the necessary tools to provide a mechanistic understanding of the many biological players and their interactions. In our “Quantitative Personalized Oncology lab, we take available preclinical data and outcomes from clinical studies to iteratively formulate, calibrate, and validate purposely built mechanistic mathematical models. We then take patient-specific clinical data as initial conditions for these models or learn patient-specific model parameters, such that we can create a virtual patient and simulate response to clinician-prescribed therapies. If the predicted response is not good enough, our mathematical models can then be used to simulate previously untested treatment protocols. This helps guide clinical decision making and provides new clinical data to improve the mathematical models for the next patient.