Abstract PR005: Mathematical biomarkers enable personalized adaptive therapy based on outcome prediction in prostate cancer

Abstract Purpose Adaptive therapy is an evolution-based treatment paradigm in metastatic cancer, which dynamically adjusts treatment to control, rather than minimize, the tumor burden. Promising clinical results in prostate cancer indicate the potential of adaptive treatment protocols to delay relapse, but demonstrate broad heterogeneity in patient response. There is a significant unmet clinical need for a biomarker to predict patient-specific progression under adaptive therapy, enabling the personalization of treatment protocols. Methods and Patients Using a mathematical model to represent the dynamics of drug-sensitive and -resistant tumor populations, we predict the expected benefit from adaptive therapy and extend this into a trio of mathematical biomarkers that can predict the time to progression and mean daily dose under a range of clinically realistic treatment protocols. These predictions are made following an initial standardized treatment cycle and enable the personalization of subsequent treatment. We apply our mathematical biomarkers to two distinct patient cohorts from trials of intermittent androgen deprivation therapy in castrate-sensitive prostate cancer and adaptive hormone therapy in castrate-resistant prostate cancer. Results Our mathematical framework accurately identifies the patients with the greatest delay to progression, or reduction in mean daily dose, enabled by adaptive therapy. We demonstrate that the most favorable strategy varies among patients and present a possible clinical framework to stratify patients into distinct treatment arms based on their individual treatment responses. Conclusion Our novel mathematical biomarker approach stratifies patients into distinct treatment protocols based on their initial treatment response, allowing for a personalized, mathematically informed approach to treatment scheduling. Citation Format: Kit Gallagher, Maximilian A. Strobl, Robert A. Gatenby, Jingsong Zhang, Philip K. Maini, Alexander R. Anderson. Mathematical biomarkers enable personalized adaptive therapy based on outcome prediction in prostate cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Cancer Evolution: The Dynamics of Progression and Persistence; 2025 Dec 4-6; Albuquerque, NM. Philadelphia (PA): AACR; Cancer Res 2025;85(23_Suppl):Abstract nr PR005.