Speaker
Description
High-Grade Serous Ovarian Cancer (HGSOC) is the deadliest gynaecological cancer and the fourth leading cause of cancer deaths in women. Most patients experience treatment failure and recurrence, primarily driven by resistance to chemotherapy, contributing to a 5-year survival rate of 45%. Existing literature lacks immunological models of HGSOC resistance and developing them could provide critical insights into mechanisms of resistance and interactions within the tumour microenvironment. We address this by constructing a mechanistic immunobiological model of HGSOC using ordinary differential equations that couples the synergistic cytotoxicity of paclitaxel and carboplatin to specific phases of the cell cycle. We then optimise chemotherapy regimens to maximise the time to first subsequent therapy (TFST) across three distinct patient profiles with varying resistance dynamics, while maintaining comparable toxicity to the standard regimen. The results indicate that optimised weekly and 21-day cycles can extend TFST and transition the patient's treatment dynamic toward a multiple response state, where acquired resistance is managed through low-dose, high-frequency chemotherapy. This model provides a robust foundation for optimising personalised treatment strategies, while offering new insight into the immunological dynamics underpinning chemotherapy resistance in HGSOC.
