DOI: 10.1101/19009712Oct 18, 2019Paper

Computational Modeling of Ovarian Cancer: Implications for Therapy and Screening

MedRxiv : the Preprint Server for Health Sciences
S. S. GuBenjamin G Neel

Abstract

High-grade serous ovarian carcinoma (HG-SOC) is a major cause of cancer-related death. Whether treatment order - primary debulking surgery followed by adjuvant chemotherapy (PDS) or neo-adjuvant chemotherapy with interval surgery (NACT) - affects outcome is controversial. We developed a mathematical framework that holds for hierarchical or stochastic models of tumor initiation and reproduces HG-SOC clinical course. After estimating parameter values, we infer that most patients harbor chemo-resistant HG-SOC cells at diagnosis, PDS is inherently superior to NACT due to better depletion of resistant cells, and earlier diagnosis could improve survival of primary, but not relapsed, patients. Our predictions are supported by primary clinical data from multiple cohorts. Our results have clear implications for these key issues in HG-SOC management.

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