Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population

Abstract

Purpose: Patients with locally advanced prostate cancer after radical prostatectomy are candidates for secondary therapy. However, this higher risk population is heterogeneous. Many cases do not metastasize even when conservatively managed. Given the limited specificity of pathological features to predict metastasis, newer risk prediction models are needed. We report a validation study of a genomic classifier that predicts metastasis after radical prostatectomy in a high risk population.

Materials and methods: A case-cohort design was used to sample 1,010 patients after radical prostatectomy at high risk for recurrence who were treated from 2000 to 2006. Patients had preoperative prostate specific antigen greater than 20 ng/ml, Gleason 8 or greater, pT3b or a Mayo Clinic nomogram score of 10 or greater. Patients with metastasis at diagnosis or any prior treatment for prostate cancer were excluded from analysis. A 20% random sampling created a subcohort that included all patients with metastasis. We generated 22-marker genomic classifier scores for 219 patients with available genomic data. ROC and decision curves, competing risk and weighted regression models were used to assess genomic classifier performance.

Results: The genomic classifier AUC was 0.79 for predicting 5-year metastasis after radical prostatectomy. Decision curves showed that the genomic classifier net benefit exceeded that of clinical only models. The genomic classifier was the predominant predictor of metastasis on multivariable analysis. The cumulative incidence of metastasis 5 years after radical prostatectomy was 2.4%, 6.0% and 22.5% in patients with low (60%), intermediate (21%) and high (19%) genomic classifier scores, respectively (p<0.001).

Conclusions: Results indicate that genomic information from the primary tumor can identify patients with adverse pathological features who are most at risk for metastasis and potentially lethal prostate cancer.

Keywords: AUC; BCR; CC; ECE; GC; GPSM; GS; Gleason score; Gleason score, preoperative PSA, SVI, SM; MVA; N+; PSA; RP; SM+; SVI; area under ROC curve; biochemical recurrence; clinical only classifier; extracapsular extension; genomic classifier; lymph node involvement; multivariable analysis; ncRNA; neoplasm metastasis; noncoding RNA; positive surgical margin; prognosis; prostate; prostate specific antigen; prostatic neoplasms; radical prostatectomy; seminal vesicle invasion; transcriptome.

Copyright © 2013 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1. Cumulative survival ROC curves comparing the GC score and individual clinicopathologic factors for predicting clinical metastasis at 5 years post-RP

GC demonstrates noticeably higher discrimination than individual clinicopathologic factors. GC, genomic classifier; Path GS, pathological Gleason score; Preop PSA, preoperative prostate specific antigen; SVI, seminal vesicle invasion; ECE, extracapsular extension; SM+; positive surgical margins; N+, lymph node involvement.

Figure 2. Survival decision curve analysis comparing the net benefit of genomic-based classifiers GC and GC combined with clinical variables, with clinical-only models (CC,GPSM, Stephenson nomogram)

Performance of models is compared to extremes of classifying all patients as at risk for clinical metastasis (thus warranting treatment of all patients; sloping gray dotted line), versus classifying no patients at risk (thus treating none; horizontal black dashed line). The “decision-to-treat” threshold, the probability of metastasis used to trigger the decision to treat is varied from 0 to 1, with sensitivity and specificity of each prediction model calculated at each threshold to determine net benefit. An optimal classifier has high net benefit above the gray dotted “treat all” line. At a wide range of “decision-to-treat” thresholds the net benefit of the GC-based models are superior. GC, genomic classifier; GC+clinical variables, genomic classifier combined with clinical variables; CC, clinical-only classifier; GPSM, (Gleason score, preoperative PSA, SVI and margins algorithm from Mayo Clinic); Stephenson 5 year, (Stephenson nomogram derived 5-year probability of survival).

Figure 3. Cumulative incidence of clinical metastasis based on GC score risk groups

The grey curve indicates the underlying cumulative incidence rate of metastasis in the full cohort obtained by resampling controls. Sixty percent of patients had low GC scores (0.6, red) had a much higher cumulative incidence reaching 22.5% at 5 years. Total number of patients at risk was 803 at t=0 from weighting controls sampled from the original population after excluding those with unavailable tissue. The dashed grey line indicates the 5-year time point following radical prostatectomy.

Figure 4. Distribution of GC scores across Gleason score groups

GC scores (y-axis) are plotted for each Gleason score group (x-axis). Clinical metastasis patients (red) and those without metastasis patients (blue) on study follow-up are shown for each group. The horizontal lines indicate the GC risk groups depicted in Figure 3. The median value of GC scores increases with Gleason score, but GC discriminates clinical metastasis cases in all Gleason score groups.