Abstract
Abstract Background: Prior studies have investigated “intermittent therapy” to delay hormone resistance in prostate cancer but did not explicitly include quantitative analysis of the evolutionary dynamics. We hypothesize that evolution-informed strategies may prolong time to progression but require a multidisciplinary effort to simulate intratumoral Darwinian dynamics and design a clinically feasible treatment protocol. Methods: We investigate intratumoral evolutionary dynamics during treatment of metastatic castrate resistant prostate cancer (mCRPC) with abiraterone, which blocks CYP17A1 autosynthesis of testosterone from endocrine precursors. We build a mathematical model assuming three competing phenotypes: (i) TP cells express CYP17A1 and produce testosterone; (ii) T+ cells require exogenous androgen; and (iii) T- cells are androgen-independent and resistant to abiraterone. Model predictions were tested in a pilot clinical trial. Results: Mathematical Model: Computer simulations demonstrate continuous maximum dose abiraterone treatment produces competitive release of resistant T- cells. However, limited treatment designed to maintain residual TP and T+ populations and suppress proliferation of T- cells was predicted to prolong response while lowering the required drug dose. Clinical Trial: In a pilot clinical trial, 11 men with asymptomatic mCRPC were treated with abirateone according to an evolution-informed, patient-specific algorithm based on the modeling results. Pre-treatment biopsies (available for 3 patients) demonstrated the predicted tumor subpopulations. Cycles of response and regrowth similar to model simulations were observed with cycle lengths varying from 3 months to > 1 year. Over a median follow up period of 18 months, 10 subjects remain responsive to Abiraterone without PSA or radiographic progression. Median time to PSA progression significantly (P<0.001) exceeds the 11.1 months historical control. As predicted by the simulations, tumor control required significantly less abiraterone with average cumulative dose less than 40% of standard of care (P<0.001). Conclusion: Integration of mathematical models and Darwinian first principles into trial design, may allow patient-specific modifications to prolong response while lowering drug doses in mCRPC. Citation Format: Jingsong Zhang, Jessica J. Cunningham, Joel S. Brown, Robert A. Gatenby. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer: a pilot multidisciplinary study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5562. doi:10.1158/1538-7445.AM2017-5562
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