Cell‐cycle risk score more accurately determines the risk for metastases and death in prostatectomy patients compared with clinical features alone

BACKGROUND: Prostate cancer treatment aims to prevent metastases and disease‐specific mortality. Pathologic parameters have limited ability to predict these outcomes, but biomarkers can improve risk discrimination. We evaluated the ability of cell‐cycle progression and combined cell‐cycle risk scores to predict metastases and disease‐specific mortality after prostatectomy. METHODS: Eligibility included (1) treatment with radical prostatectomy (1985–1997); (2) cell‐cycle progression score; (3) preoperative prostate‐specific antigen; (4) no neoadjuvant therapy; and (5) clinical follow‐up (N = 360). Cancer of the prostate risk assessment postsurgical score was combined with cell cycle progression into the prespecified combined cell‐cycle risk score. Hazard ratios (HRs) are reported per unit score. RESULTS: In total, 11% (41/360) developed metastases and 9% (33/360) experienced disease‐specific mortality. Combined cell‐cycle risk score predicted metastases and disease‐specific mortality post‐radical prostatectomy (p < 1 × 10(−8)). Adjusting for cancer of the prostate risk assessment postsurgical score, the combined cell‐cycle risk score remained a predictor of metastases (HR = 3.03 [95% confidence interval (CI): 1.49, 6.20]; p = .003] and disease‐specific mortality (HR = 3.40 [95% CI: 1.52, 7.59]; p = .004). Of patients with biochemical recurrence, 25% (41/163) developed metastases. Cancer of the prostate risk assessment postsurgical score was predictive of metastases postbiochemical recurrence but was improved by the addition of cell cycle progression (HR = 1.70 [95% CI: 1.14, 2.53]; p = .012). The combined cell‐cycle risk was also prognostic of metastases post‐biochemical recurrence (HR = 1.56 [95% CI: 1.20, 2.03]; p = .001). CONCLUSION: Combined cell‐cycle risk and cell cycle progression scores predict metastases and disease‐specific mortality post‐radical prostatectomy and should help identify patients at greatest risk of treatment failure who might benefit from earlier intervention.