Current Precision Medicine (CPM) matches cancer therapies to consensus molecular characteristics at one or more timepoints. However, cancers contain extensive subclonal heterogeneity leading to dynamic evolution in response to therapy. Mathematical modeling has the potential to optimize timing and sequencing of therapies in a more effective and personalized manner than CPM. Clinical trial designs testing Evolutionary Guided Precision Medicine (EGPM) strategies for preventing or delaying relapse to improve outcomes, are needed. In a simulation, we evaluated Dynamic Precision Medicine (DPM), an EGPM, in a stratified randomized design based on whether the patient was predicted to benefit from DPM, using an evolutionary classifier. We present this new proof-of-concept clinical trial design and perform simulations which show high power, control of false positive rates, and robust performance in the face of anticipated challenges to clinical translation. The design is distinct from common biomarker-driven designs and can provide a robust evaluation of EGPM.

The authors have declared no competing interest.

This work is supported by The Royal Society International Exchanges Award IES\R3\183092 to DP and RAB, and by Department of Defense (DoD) Breast Cancer Research Program Breakthrough Award W81XWH-20-1-0760 to RAB. RAB was partially funded by the National Institutes of Health Cancer Center Support Grant (CCSG) 5P30CA051008. CHY was partially funded by a grant (108-2118-M-001-001-MY2) from National Science & Technology Council in the Republic of China (Taiwan).

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