Growing amounts of patient data has led to the promise of precision medicine for cancer patients. However, most therapy decisions continue to be made based on ‘one size fits most’ approaches, primarily since there exist few theoretical and practical tools to deal with a patient’s data over time. In parallel with this growing interest in personalized medicine, cancer is being increasingly recognized as an eco-evolutionary system that adapts to resist treatments, suggesting that static therapy regimens are often doomed to eventual failure. Here, we present preliminary results from a novel pilot clinical trial (NCT04343365), the Evolutionary Tumor Board (ETB), which uses eco-evolutionary theory (based on experiments and modeling) to assist with clinical decision making for each patient. We developed an informational and computational framework for applying evolutionary therapy approaches to individual patients in a dynamic fashion, using their clinical data in real time. The framework relies on detailed data curation and imaging measurements for each patient, as well as a mathematical modeling approach that accounts for multi-lesion tumor growth, treatment-induced death, and the evolution of resistance. The models are calibrated by clinical trial cohort data, historical datasets of similar individual patients, as well as the patient’s own temporal data. We use a “Phase i trial” approach to account for prediction uncertainty and provide decision support for therapy options available to the patient at any given time point during care. Crucially, this is presented in a way that harmonizes with the treating oncologist’s intuition. To date, twenty-one patients at Moffitt have been enrolled into the ETB, many of whom have proceeded through the entire process, including follow-up analysis. The ETB generated therapy recommendations and outcome predictions for each case, and in many cases subsequent follow-up predictions were generated. Our current results demonstrate that the ETB approach has provided both novel and useful decision support for the clinicians. At the same time, numerous opportunities for further research and development have been identified. There are both challenges and opportunities in the area of personalized therapy, particularly in the context of providing real-time decision support for clinical care. Early results from the ETB show great promise for predicting and improving patient outcomes in cancer using mathematical modeling and evolutionary therapy.