Optimising chemotherapy for advanced high-grade serous ovarian cancer via delay-differential equations

Ovarian cancer is the deadliest gynaecological cancer and the fourth leading cause of cancer deaths in women. High-grade serous ovarian cancer (HGSOC) accounts for 75% of cases, and chemotherapy resistance and relapse occur in 85% of patients, leading to a 5-year survival of 45%. Currently, the literature lacks comprehensive immunobiological models of HGSOC, and developing such models could provide critical insights into the disease's underlying mechanisms and interactions within the tumour microenvironment. We address this by constructing an immunobiological model using delay differential equations and then optimise chemotherapy regimens to maximise efficacy, minimise toxicity, and improve treatment efficiency for first-line treatment. The model consists of two compartments, the tumour site and tumour-draining lymph node, with immune processes such as dendritic cell (DC) maturation, T cell priming and proliferation, and cytokine interactions modelled. Parameter values are estimated using experimental data from ovarian cancer tissue samples as well as the TCGA OV database. The results indicate that low-dose, more frequent chemotherapy provides comparable results to the standard regimen with a lower toxicity, and alternative dosing strategies with rest weeks can allow patients to recover from the toxic side effects of chemotherapy.