Adaptive Therapy for Metastatic Melanoma: Predictions from Patient Calibrated Mathematical Models

Abstract

Simple Summary Tumors are composed of different cancer cells with varying degrees of treatment resistance, which compete for a shared resource. Adaptive therapy is an evolution-based treatment approach that exploits this competition between heterogeneous cancer cells. The approach permits a significant number of drug-sensitive cells to survive, with less dose or with treatment breaks, so that they suppress the proliferation of drug-resistant cells via competition. How can one decide when to stop or resume treatment for each patient? This study presents two mathematical models that guide therapy on and off cycles in a patient-specific manner. The models were applied to melanoma patients and predicted patient-specific adaptive therapy schedules that significantly delayed disease progression with far less therapy (in terms of time on treatment) than the current standard of care. The benefits of adaptive therapy varied between patients. Model-based predictive factors were identified to predict the clinical time gain of individual patients. Adaptive therapy is an evolution-based treatment approach that aims to maintain tumor volume by employing minimum effective drug doses or timed drug holidays. For successful adaptive therapy outcomes, it is critical to find the optimal timing of treatment switch points in a patient-specific manner. Here we develop a combination of mathematical models that examine interactions between drug-sensitive and resistant cells to facilitate melanoma adaptive therapy dosing and switch time points. The first model assumes genetically fixed drug-sensitive and -resistant popul tions that compete for limited resources. The second model considers phenotypic switching between drug-sensitive and -resistant cells. We calibrated each model to fit melanoma patient biomarker changes over time and predicted patient-specific adaptive therapy schedules. Overall, the models predict that adaptive therapy would have delayed time to progression by 6-25 months compared to continuous therapy with dose rates of 6-74% relative to continuous therapy. We identified predictive factors driving the clinical time gained by adaptive therapy, such as the number of initial sensitive cells, competitive effect, switching rate from resistant to sensitive cells, and sensitive cell growth rate. This study highlights that there is a range of potential patient-specific benefits of adaptive therapy and identifies parameters that modulate this benefit.