Treatment Trajectories in Metastatic Hormone-sensitive Prostate Cancer: A PIONEER+ Big Data Analysis

BACKGROUND AND OBJECTIVE: The treatment landscape for metastatic hormone-sensitive prostate cancer (mHSPC) is evolving rapidly. Real-world data (RWD) are essential to understand actual treatment use and outcomes. This study aimed to describe treatment trajectories and clinical outcomes in a large, multicenter RWD cohort under the PIONEER project. METHODS: Eight European and US databases (2016-2020), including electronic health records, insurance claims, primary care data, and cancer registries, were standardized to the Observational Medical Outcome Partnership Common Data Model. Patients diagnosed with mHSPC and those receiving treatment were identified. The compared and analyzed outcomes included treatment switch, symptomatic progression, adverse events, and death. KEY FINDINGS AND LIMITATIONS: In total, 107 438 patients with mHSPC were identified, and 67 909 received treatment. Most of the patients received androgen deprivation therapy (ADT) monotherapy (69.4%), followed by ADT + an androgen receptor pathway inhibitor (ARPI; 15.2%), ADT + chemotherapy (14.0%), and triplet therapy (1.2%). The use of ARPIs increased over time. ADT + ARPI showed the highest persistence (53.8%) and a 5-yr switch-free survival rate of up to 72.3%. ADT monotherapy had 5-yr switch-free survival rates of 21.3-58.6% and adverse event-free survival rates of 64.7-81.2%. Addition of an ARPI improved switch-free survival (24.1-72.3%) but lowered adverse event-free survival (55.2-82.7%). Chemotherapy-based and triplet therapies showed variable results without consistent survival benefit. Limitations include residual confounding, inconsistent adverse event reporting, and possible data overlap. CONCLUSIONS AND CLINICAL IMPLICATIONS: This is the largest RWD study of systemic mHSPC treatment. Despite evidence, ADT monotherapy remains the most used first-line therapy. Increased use ADT + ARPI is associated with better persistence and improved outcomes in the real-world setting, supporting its broader adoption in clinical practice.