Introduction

Prostate cancer is the most prevalent malignancy among men, accounting for nearly one-third of all newly diagnosed cancers, and the second leading cause of cancer-related death after lung cancer [1,2]. Traditionally, clinical management and prognostic decision-making in prostate cancer have been based on key clinicopathologic parameters, including patient age, overall health status, serum prostate-specific antigen (PSA) levels, pathologic staging (TNM classification system), histologic grading (Gleason scoring system), and single biomarkers such as Ki-67 [3-5].

In recent years, the management of prostate cancer, similar to other malignancies, has evolved with the integration of molecular and genomic profiling tools, which enhance risk stratification and enable more personalized therapeutic approaches. Genomic profiling tools utilizing scoring systems have been classified into two broad categories: polygenic risk scores, which predict susceptibility via germline variations, and tumor-derived genomic classifiers (GCs), which evaluate gene expression patterns within tumor specimens [6].

The Decipher genomic classifier (GenomeDx Biosciences, Vancouver, BC, Canada) is a 22-gene assay utilizing microarray analysis of whole-transcriptome RNA expression from formalin-fixed, paraffin-embedded (FFPE) prostate tumor tissue. By stratifying patients into low-, intermediate-, and high-risk categories, Decipher GC was initially validated to predict metastatic risk following radical prostatectomy. Subsequent research has demonstrated its superior prognostic accuracy relative to traditional clinicopathologic variables, establishing it as one of the most broadly adopted genomic tests in clinical practice. Accordingly, it has been incorporated into the National Comprehensive Cancer Network (NCCN) guidelines [7-11].

Most of the existing validation and correlation studies for Decipher GC have been conducted using radical prostatectomy specimens, where tumor tissue is more abundant and histopathologic parameters can be assessed comprehensively across the entire gland [7-9]. In contrast, diagnostic biopsy specimens are limited by tissue sampling and heterogeneity. Consequently, the relationship between routinely assessed histopathologic features in biopsy specimens and Decipher GC risk classification remains incompletely understood. Clarifying this relationship in the biopsy setting is clinically important, as it may determine whether readily available pathologic data can inform expectations about genomic risk before definitive therapy, and whether Decipher testing offers independent prognostic value beyond what conventional biopsy parameters already capture.

In this study, we hypothesized that histopathologic markers of tumor burden and aggressiveness identified on diagnostic prostate biopsies would be associated with Decipher genomic classifier groups, reflecting the extent to which routinely available pathologic data correspond with underlying tumor genomic risk in a real-world clinical setting.