Mathematical modeling of the evolution of resistance and aggressiveness of high-grade serous ovarian cancer from patient CA-125 time series

A time-series analysis of serum Cancer Antigen 125 (CA-125) levels was performed in 791 patients with high-grade serous ovarian cancer (HGSOC) from the Australian Ovarian Cancer Study to evaluate the development of chemoresistance and response to therapy. To investigate chemoresistance and better predict the treatment effectiveness, we examined two traits: resistance (defined as the rate of CA-125 change when patients were treated with therapy) and aggressiveness (defined as the rate of CA-125 change when patients were not treated). We found that as the number of treatment lines increases, the data-based resistance increases (a decreased rate of CA-125 decay). We use mathematical models of two distinct cancer cell types, treatment-sensitive cells and treatment-resistant cells, to estimate the values and evolution of the two traits in individual patients. By fitting to individual patient HGSOC data, our models successfully capture the dynamics of the CA-125 level. The parameters estimated from the mathematical models show that patients with inferred low growth rates of treatment-sensitive cells and treatment-resistant cells (low model-estimated aggressiveness) and a high death rate of treatment-resistant cells (low model-estimated resistance) have longer survival time after completing their second-line of therapy. These findings show that mathematical models can characterize the degree of resistance and aggressiveness in individual patients, which improves our understanding of chemoresistance development and could predict treatment effectiveness in HGSOC patients.Author summary: Ovarian cancer is a major cause of death in women worldwide due to the emergence of treatment resistance and eventual treatment failure. Serum levels of the biomarker Cancer Antigen-125 (CA-125) can be used to monitor treatment response in patients with epithelial ovarian cancer. We used time series of CA-125 in 791 patients with high-grade serous ovarian cancer (HGSOC) from the Australian Ovarian Cancer Study to quantify the evolution of resistance and aggressiveness as a response to therapy in individual patients to predict the dynamics of CA-125 and the survival outcomes. We present two mathematical models that include treatment-resistant cells and treatment-sensitive cells. These models accurately fit the data and characterize patients with the best outcomes as those with the least model-estimated aggressively growing cells and the least model-estimated resistant cells. Models with only a single cell type provide poor fits to the data. These minimal models with just two cell types could provide a valuable tool for rapidly and robustly understanding the dynamics of individual patients and pointing the way to identifying specific mechanisms.