Speaker
Description
High-grade serous ovarian cancer (HGSOC) is the leading cause of gynecologic cancer mortality. Late diagnosis, widespread disease, and frequent relapse limit the effectiveness of current therapies. p53 deficiency indicates HGSOCs are vulnerable to cell-cycle inhibitors; however, currently available agents have shown less clinical benefit than expected. We hypothesize that maximizing the efficacy of these drugs requires treatment schedules that exploit HGSOC cell-cycle dynamics.
Experimental results showed the CDC7 inhibitor Simurosertib (active in G1 and S) and the PKMYT1 inhibitor Lunresertib (active in G2 and M) have strong synergy, even with sequential administration. Our associated ODE model, incorporating mechanistic knowledge of the cell cycle and drug action, showed that cell-cycle dynamics were key to this synergy. Subsequent time- and cell-cycle-resolved tracking data revealed cell-line-specific differences in cell-cycle distribution, motivating an expansion of the model that better integrated drug effects across phases and cell lines.
The calibrated model predicts schedule-dependent differences in cell death and tumor burden driven by cell-cycle dynamics and pre-existing mutations. These results highlight the potential of quantitative modeling to guide scheduling of cell-cycle inhibitors and support the development of personalized treatment strategies for HGSOC.
