Predicting Treatment Outcomes from Adaptive Therapy — A New Mathematical Biomarker

Standard-of-care cancer therapy regimens are characterized by continuous 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 propose two new mathematical biomarkers (the Delta AT Score and the eTTP) that can identify the best responders in a clinical dataset after the first cycle of treatment. Based on prior theoretical analyses, we derive personalized and clinically-feasible optimal treatment strategies, based on individual patient’s tumor dynamics. These strategies vary significantly between patients, and so we present a framework to generate individual treatment schedules based on a patient’s response to the first treatment cycle. Finally, we develop metrics to identify 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.