Abstract PR16: Computational modeling of serous ovarian carcinoma dynamics: Implications for screening and therapy.

Abstract High-grade serous ovarian carcinoma (HG-SOC) is the most common subtype of ovarian cancer and has the worst prognosis. There is intense controversy on whether the temporal order of cytoreductive surgery and chemotherapy affects treatment outcome, with the main options being primary debulking surgery with adjuvant chemotherapy (PDS) versus neo-adjuvant chemotherapy with interval debulking surgery (NACT). Although some studies report that PDS-treated patients survive significantly longer than those receiving NACT, other reports showed no significant difference patient outcome. To address this question in an unbiased way, we used computational modeling and simulated HG-SOC progression dynamics with different treatments. We developed a mathematical framework to predict the evolution of chemotherapy resistance, and populated our model with clinical data from nearly 300 patients receiving PDS or NACT from multiple institutes. After estimating the rates of proliferation and mutation of carcinoma cells, we determined that most HG-SOC patients likely harbor chemotherapy-resistant cancer cells at diagnosis. Furthermore, we predicted the effects of PDS and NACT on the number of sensitive and resistant cells, as well as patient survival following treatment, and found that our model closely recapitulated clinical observations in both training and test sets. Based on our results, we predict that PDS with optimal debulking (<1mm residual tumor) has the potential to be curative because surgery can sometimes remove all chemo-resistant cells, while adjuvant chemotherapy depletes the remaining chemo-sensitive cells. By contrast, NACT is unlikely to cure the disease because it depletes chemo-sensitive cells that can mark the location of accompanying “passenger” chemo-resistant cells. Our model also predicts that PDS should have a better outcome than NACT, when controlled for residual tumor size. Finally, we evaluated the potential benefits of early diagnosis of naive or relapsed HG-SOC. We recapitualted the clinical finding that CA125-based earlier diagnosis of relapsed cancer does not improve survival compared to physical-symptom-based diagnosis. We also predict that more sensitive detection methods (such as ctDNA-based diagnosis) are unlikely to improve survival post-relapse with current chemotherapy, because earlier diagnosis does not decrease the number of chemo-resistant cells, which are already enriched at recurrence. By contrast, our model predicts that with sufficiently sensitive assays, early detection of treatment-naive HG-SOC could improve survival time and increase chance of cure. This abstract is also presented as Poster B19. Citation Format: Shengqing Gu, Stephanie Lheureux, Azin Sayad, Liat Hogen Ben-David, Iryna Vyarvelska, Paulina Cybulska, Marcus Bernardini, Barry Rosen, Amit Oza, Benjamin G. Neel. Computational modeling of serous ovarian carcinoma dynamics: Implications for screening and therapy. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: Exploiting Vulnerabilities; Oct 17-20, 2015; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(2 Suppl):Abstract nr PR16.

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