Trial design and objectives for prostate cancer: Recommendations from the Prostate Cancer Clinical Trials Working Group 4 (PCWG4).

Clinical trial conduct in advanced prostate cancer has changed dramatically with the continued development of new imaging approaches, molecular phenotypes and genetic subtypes, prognosis assessments, and effective therapies across a range of disease states. This created a need to redefine terminology and best practices for clinical trials. We convened PCWG4, an international expert committee of multidisciplinary working groups, between 2016 and 2025 to update and expand PCWG2-3 recommendations based on emerging evidence and clinical trial data in an innovative biomarker and imaging context to provide guidance for clinical trial design, eligibility, and endpoint assessments. PCWG4 redefines terminology around disease states and prior therapies in a patient-centric context, considering imaging modalities, with a particular focus on PET-defined disease. New recommendations are provided for disease state terminology, defining eligibility criteria, imaging and non-imaging-based responses. We define delay/prevent endpoints including responses by pathology, ctDNA, circulating tumor cells, and PSA declines, and specify intervals for re-assessments including imaging, biomarker assessments, and patient reported outcomes. We propose new imaging-specific rPFS criteria including guidance with serial PSMA PET/CT imaging (Table). We provide recommendations in a biomarker-based context for the indication, reflective of patient benefit for specific interventions. We emphasize the need for development of validated PET imaging and molecular and phenotypic criteria as well as trial designs to appropriately risk stratify patients, predict and assess benefit, and measure post-treatment outcomes reliably in a trial framework. PCWG4 expands guidance on patient and tumor profiling, as well as therapy development, to include both androgen deprivation therapy sensitive and resistant settings, reflecting today’s more heterogeneous and diverse patient population to optimize outcomes.

Duke Scholars

Author Susan Halabi Biostatistics & Bioinformatics, Division of Biostatistics