Background: Prostate cancer exhibits biological and clinical heterogeneity even within established clinico-pathologic risk groups. The Decipher genomic classifier (GC) is a validated method to further risk-stratify disease in patients with prostate cancer, but its performance solely within National Comprehensive Cancer Network (NCCN) high-risk disease has not been undertaken to date. Methods: A multi-institutional retrospective study of 405 men with high-risk prostate cancer who underwent primary treatment with radical prostatectomy (RP) or radiation therapy (RT) with androgen-deprivation therapy (ADT) at 11 centers from 1995 to 2005 was performed. Cox proportional hazards models were used to determine the hazard ratios (HR) for the development of metastatic disease based on clinico-pathologic variables, risk groups, and GC score. The area under the receiver operating characteristic curve (AUC) was determined for regression models without and with the GC score. Results: Over a median follow-up of 82 months, 104 patients (26%) developed metastatic disease. On univariable analysis, increasing GC score was significantly associated with metastatic disease ([HR]: 1.34 per 0.1 unit increase, 95% confidence interval [CI]: 1.19–1.50, p < 0.001), while age, serum PSA, biopsy GG, and clinical T-stage were not (all p > 0.05). On multivariable analysis, GC score (HR: 1.33 per 0.1 unit increase, 95% CI: 1.19–1.48, p < 0.001) and GC high-risk (vs low-risk, HR: 2.95, 95% CI: 1.79–4.87, p < 0.001) were significantly associated with metastasis. The addition of GC score to regression models based on NCCN risk group improved model AUC from 0.46 to 0.67, and CAPRA from 0.59 to 0.71. Conclusions: Among men with high-risk prostate cancer, conventional clinico-pathologic data had poor discrimination to risk stratify development of metastatic disease. GC score was a significant and independent predictor of metastasis and may help identify men best suited for treatment intensification/de-escalation.
ASJC Scopus subject areas
- Cancer Research