Experimental models for ovarian cancer research

Area of Science:

• Oncology
• Translational Research
• Biomedical Models

Background:

• Ovarian cancer (OC) has the highest mortality among gynecological malignancies.
• Therapeutic resistance, late diagnosis, and limited effective treatments contribute to poor outcomes.
• There is a critical need for preclinical models that accurately reflect OC subtypes' characteristics.

Purpose of the Study:

• To review the merits and challenges of various preclinical models for ovarian cancer.
• To summarize the current applications of these models in patient stratification and therapeutic research.
• To discuss the clinical translatability of ovarian cancer models.

Main Methods:

• Review of existing literature on ovarian cancer preclinical models.
• Discussion of mouse models: tumor xenografts, genetically modified models.
• Exploration of 3D tumor models: organoids and organotypic co-culture models.
• Emphasis on patient-derived models for addressing heterogeneity.

Main Results:

• Various preclinical models, including 3D and patient-derived options, are available for ovarian cancer research.
• These models can recapitulate histological, molecular, and pathophysiological features of OC.
• Patient-derived models show promise in simulating OC progression and informing personalized treatments.

Conclusions:

• Preclinical models are valuable assets for understanding ovarian cancer heterogeneity and progression.
• Further optimization is needed to enhance the clinical translatability of these models.
• Improved models will advance patient stratification and therapeutic research in ovarian cancer.