Mathematical modeling predicts response to chemotherapy and drug combinations in ovarian cancer

Platinum-based chemotherapy constitutes the backbone of clinical care in advanced solid cancers such as high-grade serous ovarian cancer (HGSOC) and has prolonged survival of millions of cancer patients. Most of these patients, however, become resistant to chemotherapy, which generally leads to a fatal refractory disease. We present a comprehensive stochastic mathematical model and simulator approach to describe platinum resistance and standard-of-care (SOC) therapy in HGSOC. We used pre- and post-treatment clinical data, including 18F-FDG-PET/CT images, to reliably estimate the model parameters and simulate "virtual HGSOC patients." Treatment responses of the virtual patients generated by our mathematical model were indistinguishable from real-life HGSOC patients. We demonstrated the utility of our approach by evaluating the survival benefit of combination therapies that contain up to six drugs targeting platinum resistance mechanisms. Several resistance mechanisms were already active at diagnosis, but combining SOC with a drug that targets the most dominant resistance subpopulation resulted in a significant survival benefit. This work provides a theoretical basis for a cancer treatment paradigm in which maximizing platinum's killing effect on cancer cells requires overcoming resistance mechanisms with targeted drugs. This freely available mathematical model and simulation framework enable rapid and rigorous evaluation of the benefit of a targeted drug or combination therapy in virtual patients before clinical trials, which facilitates translating preclinical findings into clinical practice.

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