Longitudinal prediction of drug response in high-grade serous ovarian cancer organoid cultures aligning with clinical responses

Introduction: High-grade serous ovarian cancer (HGSOC) ranks among the most aggressive gynecological malignancies. Its high mortality is driven by frequent recurrence after primary treatments and the development of platinum resistance. Traditional drug development using animal models is time-consuming and often lacks reproducibility, making it less effective for precision cancer medicine. Objectives: This study aimed to demonstrate the potential of primary patient-derived tumor organoids (PDTOs) as a platform to replicate disease pathophysiology and accelerate drug screening for HGSOC. The focus was on validating the stability of PDTOs in predicting drug responses over time and comparing in vitro results with clinical data. Methods: An expandable HGSOC PDTO platform was developed for rapid drug screening and resistance testing. Seven pairs of organoids underwent low- and high-passage drug screenings over up to a nine-month period. The screenings involved 20 conventional and FDA-approved drugs, and proteomic analyses were conducted to assess the stability of the organoids over time. Results: The comparison of in vitro drug screening outcomes with clinical data confirmed the predictive capacity of the organoid platform. Notably, a PDTO with a BRCA1 mutation exhibited resistance to Carboplatin and PARP inhibitors, reflecting the clinical scenario and reinforcing the platform's predictive power. Conclusion: This study underscores the clinical significance of organoid models in predicting drug resistance and therapeutic response. These models demonstrated their utility in screening novel treatments, such as Peptidylprolyl Cis/Trans Isomerase, NIMA-Interacting 1 (Pin1) inhibitors, which show potential in overcoming resistance to standard ovarian cancer therapies. The organoid platform offers a powerful tool for advancing personalized treatment approaches, with the potential to guide therapeutic decisions and optimize patient outcomes.