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.