Robust Meta-Analysis of a Clinical Cell-Cycle Risk (CCR) Score Demonstrates Broadly Applicable Metastasis and Disease-Specific Mortality Risk Stratification in Men With Localized Prostate Cancer.

PURPOSE: To systematically review and synthesize the evidence for Prolaris, a combined clinical risk (CCR) score that stratifies individual patient risk and provides a decision-making tool that impacts medical management across key decision points in the localized prostate cancer care continuum.

PATIENTS AND

METHODS

A systematic literature search was performed, and individual participant data (IPD) were collected where possible to perform a two-step IPD analysis. The primary endpoint was composite distant metastasis (DM) and prostate cancer-specific mortality (PCSM), also analyzed individually. Cox proportional hazards models were fit in individual cohorts. Random-effects meta-analyses with Knapp-Hartung adjustment were used to create combined hazard ratio (HR) estimates across studies.

RESULTS

Fourteen studies (8478 patients, including 7924 with IPD) were identified as eligible. The cohort consisted of 20.0%, 33.9%, 32.5%, and 13.6% NCCN low-, favorable intermediate-, unfavorable intermediate-, and high-risk patients, respectively. Initial management was 43.0% noninterventional (eg, AS), 23.6% surgery, 16.4% radiation therapy, and 13.1% radiation plus androgen deprivation therapy. CCR was prognostic for composite DM-PCSM endpoint after accounting for initial management (HR 2.28 (95% confidence interval 1.98, 2.62), P = 9.1 × 10-9), as well as DM (P = 1.9 × 10⁻⁶) and PCSM (P = 9.7 × 10⁻⁴) individually. Additional meta-analyses demonstrated CCR adds independent prognostic information to Gleason, CAPRA, and NCCN (all P < 10-5), and Prolaris thresholds are prognostic for composite DM-PCSM, as well as the endpoints individually (all P < .005).

CONCLUSION

CCR is prognostic across NCCN risk groups and management strategies in localized prostate cancer. Prognostic value persists after adjusting for initial management and established clinicopathologic factors, highlighting utility in supplementing conventional risk models.