Neuroendocrine differentiation predicts the therapeutic efficacy of abiraterone and docetaxel as first-line therapy in metastatic castration-resistant prostate cancer

Journal of Cancer Research and Clinical Oncology(2023)

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摘要
Purpose We aim to explore the predictive value of neuroendocrine differentiation (NED) in patients with metastatic castration-resistant prostate cancer (mCRPC) receiving abiraterone or docetaxel as first-line therapy. Methods We retrospectively analyzed data of 262 mCRPC patients receiving abiraterone or docetaxel as first-line mCRPC treatment. NED was evaluated using prostate biopsy samples at the time of mCRPC by immunohistochemical staining. Kaplan–Meier curves and Cox regression were used to assess the association between NED and treatment outcomes including PSA progression-free survival (PSA-PFS), radiographic progression-free survival (rPFS), and overall survival (OS). Results NED was confirmed in 100/262 (38.2%) mCRPC patients, with 76/100 (76.0%) and 24/100 (24.0%) men harboring NED < 10% and NED ≥ 10%, respectively. 203/262 (77.5%) and 59/262 (22.5%) patients received abiraterone and docetaxel, respectively. In abiraterone treatment, NED was associated with a significantly shorter median PSA-PFS (mPSA-PFS, 7.5 vs. 10.3-Mo, P < 0.001), median rPFS (mrPFS, 15.9 vs. 19.5-Mo, P = 0.010), and median OS (mOS, 23.2 vs. 34.3-Mo, P = 0.014)). Likewise, for mCRPC patients receiving docetaxel, the positive detection of NED also predicted shorter mPSA-PFS (3.8 vs. 5.9-Mo, P = 0.052), mrPFS (8.4 vs. 20.4-Mo, P = 0.016) and mOS (13.6 vs. 29.0-Mo, P = 0.033). The adverse prognostic trait of NED is consistent in most subgroups. Additionally, patients’ survival outcomes deteriorated as the NED proportion grew in both therapies. After propensity score matching, NED-positive patients showed comparable prognosis in abiraterone and docetaxel therapy. Conclusion For mCRPC patients receiving abiraterone or docetaxel, NED and its proportion were critical predictive factors. NED detection at mCRPC might aid in predicting patients’ outcomes and optimizing treatment decisions.
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