Genomic testing is now embedded in contemporary prostate cancer care, yet the clinical meaning of different genomic platforms varies substantially by disease state and clinical context. In localized disease, tissue-based genomic classifiers primarily serve prognostic functions by refining risk estimates beyond clinicopathologic variables, whereas in advanced disease, germline and somatic testing identify predictive biomarkers linked to therapy selection. This distinction is clinically consequential because the supporting evidence, endpoints, and implementation challenges differ across assays and across points on the disease continuum. In this review, we position tissue-based assays, germline testing, somatic sequencing, circulating tumor DNA (ctDNA), and artificial intelligence-enabled biomarkers within a unified clinical framework spanning localized disease, biochemical recurrence, and metastatic progression. We critically compare commercially available genomic assays with respect to methodology, specimen type, intended use, validation cohorts, and clinically relevant outcomes. We distinguish prognostic classifiers from predictive biomarkers such as homologous recombination repair deficiency and mismatch repair deficiency, and we evaluate emerging approaches, including liquid biopsy, multimodal integration with imaging, and digital pathology-based algorithms. We further address implementation barriers that may limit real-world impact, including reimbursement uncertainty, disparities in access to next-generation sequencing, limited provider familiarity with genomic interpretation, and the need for patient-centered communication and navigation in genomics-informed care. A clinically useful framework for prostate cancer genomics must therefore move beyond cataloging tests and instead clarify when genomic results change management, where evidence remains immature, and how implementation strategies can improve equity and actionability.
Cancer genetics. 2026 Jun 04 [Epub ahead of print]
Ewan K Cobran, N Jewel Samadder, Daniel J Schaid, David V Conti, Christopher Haiman, Luciana Vargas, Michael R Gionfriddo, Jon C Tilburt
Department of Quantitative Health Science, Mayo Clinic College of Medicine and Science, Scottsdale, Arizona, USA. Electronic address: ., Division of Gastroenterology and Hepatology, Mayo Clinic College of Medicine and Science, Phoenix, Arizona, USA., Department of Quantitative Health Science, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA., Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA., Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA., Knowledge and Evaluation Research Unit, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA., Department of Internal Medicine, Mayo Clinic College of Medicine and Science, Phoenix, Arizona, USA.