Standard-of-care cancer therapy regimens are characterized by continuous treatment at the maximum tolerated dose; however, this approach often fails for metastatic cancers due to the emergence of drug resistance. An evolution-based treatment paradigm known as Adaptive Therapy' has been proposed to counter this, dynamically adjusting treatment to control, rather than minimize, the tumor burden, thus suppressing the growth of treatment-resistant cell populations and hence delaying patient relapse. Promising clinical results in prostate cancer indicate the potential of adaptive treatment protocols, but demonstrate broad heterogeneity in patient response. This naturally leads to the question: why does this heterogeneity occur, and is a one-size-fits-all’ protocol best for patients across this spectrum of responses? Using a Lotka-Volterra model to represent the dynamics of drug-sensitive and -resistant tumor populations, we derive a predictive expression for the expected benefit from Adaptive Therapy and demonstrate that this can identify the best responders in a clinical dataset. We extend this into a trio of mathematical biomarkers that can predict the time to progression and mean daily dose under a range of Adaptive Therapy protocols, accounting for clinical limitations such as fixed time intervals between clinical appointments to compare clinically realistic treatment strategies. We show that the most favorable strategy varies between patients, and present a framework to stratify patients into different treatment arms based on their individual treatment responses. Overall, the proposed strategies offer personalized treatment schedules that consistently outperform clinical standard-of-care protocols.