Translational applications and clinical utility of organoids in ovarian cancer: a systematic review.

Abstract

Background: Ovarian cancer remains the most lethal gynecological malignancy, largely due to late-stage diagnosis and the rapid emergence of platinum resistance. Traditional treatment selection relies on population-level guidelines that often fail to account for individual tumor heterogeneity. Patient-derived organoids have emerged as potential functional avatars for personalized therapy, providing a real-time read-out of tumor sensitivity.

Methods: This systematic review synthesized data from 13 studies evaluating the feasibility, histological fidelity, and clinical concordance of ovarian cancer patient-derived organoids. Studies were assessed for quality using the Newcastle-Ottawa Scale and QUADAS-2.

Results: A pooled analysis identified a mean success rate of 71% for obtaining patient-derived tumor organoids in early passages, with short-term assays showing high feasibility for immediate therapy guidance. Because the majority of the included studies relied on short-term cultures, these figures represent early-passage obtention rather than the true "establishment" of stable, cryopreservable lines that can be propagated beyond five passages. Indeed, a direct comparison of establishment rates for such long-term lines was precluded by the inconsistent reporting of total sample sizes (denominators) across the included publications-an important reporting gap that should be addressed in future studies. While 92.3% of studies reported high concordance between PDTO drug sensitivity and patient outcomes for platinum and PARP inhibitors, critical discordance in specific models suggests that growth robustness does not always guarantee predictive accuracy.

Conclusion: PDOs represent a robust platform for functional drug screening within a clinically actionable 21-day window. To move into routine practice, the field requires standardized proficiency testing, harmonized reporting of establishment denominators, and the integration of complex stroma-containing models to better reflect the tumor microenvironment.