Development and Validation of a Novel Integrated Clinical-Genomic Risk Group Classification for Localized Prostate Cancer.

Purpose It is clinically challenging to integrate genomic-classifier results that report a numeric risk of recurrence into treatment recommendations for localized prostate cancer, which are founded in the framework of risk groups. We aimed to develop a novel clinical-genomic risk grouping system that can readily be incorporated into treatment guidelines for localized prostate cancer. Materials and Methods Two multicenter cohorts (n = 991) were used for training and validation of the clinical-genomic risk groups, and two additional cohorts (n = 5,937) were used for reclassification analyses. Competing risks analysis was used to estimate the risk of distant metastasis. Time-dependent c-indices were constructed to compare clinicopathologic risk models with the clinical-genomic risk groups. Results With a median follow-up of 8 years for patients in the training cohort, 10-year distant metastasis rates for National Comprehensive Cancer Network (NCCN) low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.3%, 9.2%, 38.0%, and 39.5%, respectively. In contrast, the three-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.5%, 29.4%, and 54.6%, for low-, intermediate-, and high-risk, respectively, which were consistent in the validation cohort (0%, 25.9%, and 55.2%, respectively). C-indices for the clinical-genomic risk grouping system (0.84; 95% CI, 0.61 to 0.93) were improved over NCCN (0.73; 95% CI, 0.60 to 0.86) and Cancer of the Prostate Risk Assessment (0.74; 95% CI, 0.65 to 0.84), and 30% of patients using NCCN low/intermediate/high would be reclassified by the new three-tier system and 67% of patients would be reclassified from NCCN six-tier (very-low- to very-high-risk) by the new six-tier system. Conclusion A commercially available genomic classifier in combination with standard clinicopathologic variables can generate a simple-to-use clinical-genomic risk grouping that more accurately identifies patients at low, intermediate, and high risk for metastasis and can be easily incorporated into current guidelines to better risk-stratify patients.

Ability of a Genomic Classifier to Predict Metastasis and Prostate Cancer-specific Mortality after Radiation or Surgery based on Needle Biopsy Specimens.

BACKGROUND: Decipher is a validated genomic classifier developed to determine the biological potential for metastasis after radical prostatectomy (RP). OBJECTIVE: To evaluate the ability of biopsy Decipher to predict metastasis and Prostate cancer-specific mortality (PCSM) in primarily intermediate- to high-risk patients treated with RP or radiation therapy (RT). DESIGN, SETTING, AND PARTICIPANTS: Two hundred and thirty-five patients treated with either RP (n=105) or RT±androgen deprivation therapy (n=130) with available genomic expression profiles generated from diagnostic biopsy specimens from seven tertiary referral centers. The highest-grade core was sampled and Decipher was calculated based on a locked random forest model. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Metastasis and PCSM were the primary and secondary outcomes of the study, respectively. Cox analysis and c-index were used to evaluate the performance of Decipher. RESULTS AND LIMITATIONS: With a median follow-up of 6 yr among censored patients, 34 patients developed metastases and 11 died of prostate cancer. On multivariable analysis, biopsy Decipher remained a significant predictor of metastasis (hazard ratio: 1.37 per 10% increase in score, 95% confidence interval [CI]: 1.06-1.78, p=0.018) after adjusting for clinical variables. For predicting metastasis 5-yr post-biopsy, Cancer of the Prostate Risk Assessment score had a c-index of 0.60 (95% CI: 0.50-0.69), while Cancer of the Prostate Risk Assessment plus biopsy Decipher had a c-index of 0.71 (95% CI: 0.60-0.82). National Comprehensive Cancer Network risk group had a c-index of 0.66 (95% CI: 0.53-0.77), while National Comprehensive Cancer Network plus biopsy Decipher had a c-index of 0.74 (95% CI: 0.66-0.82). Biopsy Decipher was a significant predictor of PCSM (hazard ratio: 1.57 per 10% increase in score, 95% CI: 1.03-2.48, p=0.037), with a 5-yr PCSM rate of 0%, 0%, and 9.4% for Decipher low, intermediate, and high, respectively. CONCLUSIONS: Biopsy Decipher predicted metastasis and PCSM from diagnostic biopsy specimens of primarily intermediate- and high-risk men treated with first-line RT or RP. PATIENT SUMMARY: Biopsy Decipher predicted metastasis and prostate cancer-specific mortality risk from diagnostic biopsy specimens.

Decipher Genomic Classifier Measured on Prostate Biopsy Predicts Metastasis Risk.

OBJECTIVES: To evaluate the ability of the Decipher genomic classifier in predicting metastasis from analysis of prostate needle biopsy diagnostic tumor tissue specimens. MATERIALS AND METHODS: Fifty-seven patients with available biopsy specimens were identified from a cohort of 169 men treated with radical prostatectomy in a previously reported Decipher validation study at Cleveland Clinic. A Cox multivariable proportional hazards model and survival C-index were used to evaluate the performance of Decipher. RESULTS: With a median follow up of 8 years, 8 patients metastasized and 3 died of prostate cancer. The Decipher plus National Comprehensive Cancer Network (NCCN) model had an improved C-index of 0.88 (95% confidence interval [CI] 0.77-0.96) compared to NCCN alone (C-index 0.75, 95% CI 0.64-0.87). On multivariable analysis, Decipher was the only significant predictor of metastasis when adjusting for age, preoperative prostate-specific antigen and biopsy Gleason score (Decipher hazard ratio per 10% increase 1.72, 95% CI 1.07-2.81, P = .02). CONCLUSION: Biopsy Decipher predicted the risk of metastasis at 10 years post radical prostatectomy. While further validation is required on larger cohorts, preoperative knowledge of Decipher risk derived from biopsy could indicate the need for multimodality therapy and help set patient expectations of therapeutic burden.