LAUSR

PARP inhibitors (PARPis) are revolutionizing the treatment of ovarian cancer. Yet for many patients these improvements come at the cost of physical and financial toxicity and responses are typically temporary due to emerging drug resistance. A growing body of pre-clinical and clinical work suggests that when cure is unlikely, it is possible to delay progression and reduce drug use through patient-specific drug scheduling. So-called 'adaptive therapy' dynamically adjusts treatment to preserve drug-sensitive cells which interfere with resistant cells through competition. In a prior study, we developed a mathematical model to describe the treatment response of ovarian cancer cells to the PARPi olaparib in vitro, and we proposed a candidate adaptive PARPi algorithm. Here, we extend our model to capture the dynamics in patients and use it to study the feasibility and potential benefit of adaptive PARPi administration in practice. Our prior model posited that treatment induces cell cycle arrest that moves cells from the proliferating subset of the population to an arrested compartment. The model predicted that while there is scope for treatment reductions, these need to be carefully timed and prolonged treatment breaks should be avoided. Based on these results we proposed an adaptive treatment algorithm in which the olaparib dose is switched between high and low doses, depending on the tumor’s growth rate. To test the translational potential of this strategy, we retrospectively collected data from 53 ovarian cancer patients who received olaparib at the H Lee Moffitt Cancer Center between 2014 and 2021. Using serum CA-125 as a proxy for tumor burden, we first examined whether our mathematical model could capture the patients’ longitudinal dynamics. While the response of some patients was consistent with what we had observed in vitro, in others there was evidence of the emergence of a distinct drug-resistant population, and we extended our mathematical model to account for this. After calibrating and validating the model with the patient data, we tested whether these patients would have benefited from adaptive PARPi treatment. Our simulations suggest that our proposed algorithm is feasible and provides a means for reducing treatment in a patient-specific manner. In addition, in a subset of patients we predict that adaptive therapy could delay progression. Overall, this work corroborates the potential for adaptive PARPi therapy and helps to identify outstanding challenges on the way to clinical translation. Citation Format: Maximilian A. Strobl, Alexandra L. Martin, Christopher Gallagher, Mehdi Damaghi, Mark Robertson-Tessi, Robert Gatenby, Robert M. Wenham, Philip K. Maini, Alexander R. Anderson. Adaptive treatment scheduling of PARP inhibitors in ovarian cancer: Using mathematical modeling to assess clinical feasibility and estimate potential benefits. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5694.