Experimental models for ovarian cancer research

Among all gynecological malignancies, ovarian cancer (OC) accounts for the highest mortality rate due to high therapeutic resistance, prolonged latency and a lack of effective treatments. This calls for preclinical models that could recapitulate the histological, molecular and pathophysiological features of distinct OC subtypes. Various mouse models including tumor xenografts, genetically modified models, and novel 3D tumor models including organoids and organotypic co-culture models have been developed, and they serve as valuable assets to fulfill this demand. These models, particularly those patient-derived, can address the heterogeneity of OC and simulate OC progression in patients, hence bringing important insights for personalized treatments. In this review, we will discuss the merits and challenges of these models, and summarize their current preclinical applications in patient stratification and therapeutic research. Though limitations are inevitable, further optimization will render these models more clinically translatable in OC research.

Automated summary

Ovarian cancer has the highest mortality rate among gynecological malignancies due to high therapeutic resistance, prolonged latency, and a lack of effective treatments. Preclinical models that recapitulate the histological, molecular, and pathophysiological features of distinct ovarian cancer subtypes, especially patient-derived models, are valuable assets in addressing the heterogeneity of ovarian cancer and providing important insights for personalized treatments. Further optimization of these models will enhance their clinical translatability in ovarian cancer research.