Unveiling the Clinical Potential of Prostate Cancer Three-dimensional Models: A Systematic Review

Patient-derived organoids (PDOs) and organotypic slice tissues have emerged as promising platforms to model prostate cancer (PCa) in three dimensions (3D), preserving tumor architecture and molecular features. Their relevance as translational tools for precision oncology, however, remains incompletely defined.

To systematically assess the feasibility, molecular fidelity, and translational applications of 3D models in PCa.

We conducted a comprehensive systematic literature review (PROSPERO CRD42025643117) in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies published until December 2024 were screened across PubMed, EMBASE, and Web of Science. Original studies involving human-derived PCa 3D models were included. Data were extracted on organoid generation efficiency, molecular profiling, biomarker exploration, and drug testing outcomes.

Success rates for PDO establishment varied widely (15-90%), influenced by sample type, disease stage, and matrix conditions. Long-term expansion beyond 5-10 passages was achieved in a minority of models, particularly from radical prostatectomy. Despite these limitations, PDOs showed high genomic, transcriptomic, and epigenetic concordance with patient tumors, including key alterations in androgen receptor (AR) signaling, Tumor Protein (TP) 53, Phosphatase and TENsin homolog (PTEN), Phosphoinositide-3-Kinase/Protein Kinase B (PI3K/AKT), and neuroendocrine markers. Organoids retained intratumoral heterogeneity and were suitable for single-cell sequencing. Biomarker studies identified Enhancer of Zest Homolog 2 (EZH2), SeCretoGranin 2 (SCG2), Human Epidermal Growth factor receptor 3 (HER3), and methylation patterns as relevant for subtype classification. Drug screening recapitulated known therapeutic responses and highlighted actionable resistance mechanisms, including differential sensitivity to androgen receptor pathway inhibitors, taxanes, poly(adenosine diphosphate-ribose)-polymerase inhibitors, and PI3K/AKT-targeted therapies. However, the lack of microenvironment components and the time-intensive nature of organoid establishment remain key limitations.

Prostate cancer 3D models offer a relevant, patient-specific platform to study tumor biology, predict drug responses, and identify novel biomarkers. While standardization and scalability challenges persist, organoids have a strong potential for integration into precision oncology pipelines.