Molecular classifications of prostate cancer: basis for individualized risk stratification and precision therapy

Abstract

AbstractTumour classifications play a pivotal role in prostate cancer (PCa) management. It can predict the clinical outcomes of PCa as early as the disease is diagnosed and then guide therapeutic schemes, such as active monitoring, standalone surgical intervention, or surgery supplemented with postoperative adjunctive therapy, thereby circumventing disease exacerbation and excessive treatment. Classifications based on clinicopathological features, such as prostate cancer-specific antigen, Gleason score, and TNM stage, are still the main risk stratification strategies and have played an essential role in standardized clinical decision-making. However, mounting evidence indicates that clinicopathological parameters in isolation fail to adequately capture the heterogeneity exhibited among distinct PCa patients, such as those sharing identical Gleason scores yet experiencing divergent prognoses. As a remedy, molecular classifications have been introduced. Currently, molecular studies have revealed the characteristic genomic alterations, epigenetic modulations, and tumour microenvironment associated with different types of PCa, which provide a chance for urologists to refine the PCa classification. In this context, numerous invaluable molecular classifications have been devised, employing disparate statistical methodologies and algorithmic approaches, encompassing self-organizing map clustering, unsupervised cluster analysis, and multifarious algorithms. Interestingly, the classifier PAM50 was used in a phase-2 multicentre open-label trial, NRG-GU-006, for further validation, which hints at the promise of molecular classification for clinical use. Consequently, this review examines the extant molecular classifications, delineates the prevailing panorama of clinically pertinent molecular signatures, and delves into eight emblematic molecular classifications, dissecting their methodological underpinnings and clinical utility.

Keywords

• Prostate cancer
• molecular classification
• prognosis prediction
• precision treatment