Abstract 6241: Predicting treatment outcomes from adaptive therapy: a new mathematical biomarker

Abstract Standard-of-care cancer therapy regimens are characterized by continual treatment at the maximum tolerated dose, however, this approach often fails on 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 representation of drug-sensitive and -resistant tumor populations' dynamics, we obtain a predictive expression for the expected benefit from Adaptive Therapy and show that this is effective at identifying the best responders in a clinical dataset. Incorporating techniques from Optimal Control theory, we further derive an analytic expression for the optimal treatment strategy, based on individual patient tumor dynamics. In contrast to prior approaches, this strategy also accounts for clinical limitations such as fixed time intervals between clinical appointments, to produce clinically realistic treatment strategies. We show that this strategy varies significantly between patients, and present a framework to generate individual treatment schedules from a patient's initial treatment data. Finally, we develop metrics to predict which patients have the greatest sensitivity to unplanned schedule changes, such as delayed appointments, allowing clinicians to identify high-risk patients that need to be monitored more closely and potentially more frequently. Overall, the proposed strategies offer personalized treatment schedules that consistently outperform clinical standard-of-care protocols. Citation Format: Kit Gallagher, Maximilian A. Strobl, Philip K. Maini, Alexander R. Anderson. Predicting treatment outcomes from adaptive therapy: a new mathematical biomarker [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6241.