Prognostic model for second progression-free survival and overall survival in patients with high-risk metastatic hormone-sensitive prostate cancer treated with abiraterone acetate and androgen deprivation therapy.

Abstract

To develop and validate a prognostic risk model for high-risk metastatic hormone-sensitive prostate cancer (mHSPC) patients treated with upfront abiraterone acetate (ABI). This retrospective multicenter study involved 233 high-risk mHSPC patients who received upfront ABI, developed by three academic centers. The model was externally validated with an independent cohort of 282 patients. To identify independent prognostic factors for second progression-free survival (PFS2) and develop the best-fitted model, Cox proportional hazards regression, followed by the Akaike information criterion, was used. Patients were categorized into three groups based on their risk scores. PFS2 and overall survival (OS) were evaluated according to the risk groups in the discovery and validation cohorts. The median age was 72 (range 51-89) years, with a median follow-up duration of 27 months. Independent factors linked to PFS2 included an Eastern Cooperative Oncology Group performance status ≥2, a primary Gleason score of 5, an extent of disease score of ≥3 or liver metastasis, and lactate dehydrogenase >220 U/L. Median PFS2 for favorable-, intermediate-, and poor-risk groups were not reached, 43 months, and 16 months, respectively. The median OS was 29 months in the poor-risk group, whereas it was not reached in the favorable- and intermediate-risk groups. The 2-year OS rates in the favorable-, intermediate- and poor-risk groups were 94.5%, 80.1%, and 60.3%, respectively. The validation cohort confirmed the risk model's relationship with PFS2 and OS. The median PFS2 and OS in the high-risk group were 21 months and 32 months, respectively. Our prognostic model, including five clinical factors, is useful for patient care and treatment selection in high-risk mHSPC patients treated with ADT plus ABI. The developed model could provide more accurate information, guide treatment decisions, or classify patients in future clinical trials.