A Patient-Derived Organoid Platform from TUR-P Samples Enables Precision Drug Screening in Advanced Prostate Cancer

Background: Patient-derived advanced prostate cancer organoids have been developed to mimic tumor heterogeneity and beneficially predict optimized drugs for specific patients. The organoids are promising functional drug screening models which can capture patient outcomes. However, organoid development from transurethral resection of the prostate (TUR-P) has been hampered by a low success rate, and the cost of culture should be reduced for realistically clinical settings. In our study, we aimed to improve the success rate and reduce the cost of establishing advanced prostate cancer organoids from TUR-P specimens. Methods: We optimized and improved both the organoid culture protocol and the fetal bovine serum (FBS) based-organoid culture medium, which is suitable for performing drug testing in a short turnaround time. To confirm that the generated organoids could recapitulate the tumor heterogeneity of original tissues, the organoids were validated with histological, immunohistochemical, and genomic analyses. Results: Following the optimized protocol, we successfully generated organoids in approximately 18 out of 29 cases (or 62.07%), which exhibited effective growth and survival. In addition, we found that the established organoids efficiently identified and captured tumor characteristics present in their matched original tissues, as indicated by histological, immunohistochemical, and comprehensive genomic analysis. As a proof of concept for personalized medicine, the generated organoids were treated with anti-cancer drugs, including docetaxel and enzalutamide in parallel with the clinical treatments. Interestingly, the in vitro drug screening results were positively correlated with the patient outcomes at the clinical level. Conclusions: Taken together, the established APC organoids were able to precisely predict patients’ outcomes for treatment decision-making within a month in a cost-effective manner.