Validation of a Genomic Classifier that Predicts Metastasis Following Radical Prostatectomy in an At Risk Patient Population

Abstract

Purpose Prostate cancer patients with locally advanced disease after radical prostatectomy (RP) are candidates for secondary therapy. However, this higher risk population is heterogeneous and many will not metastasize even when conservatively managed. Given the limited specificity of pathologic features to predict metastasis, newer risk-prediction models are needed. This represents a validation study of a genomic classifier (GC) that predicts post-RP metastasis in a high-risk population. Materials and Methods A case-cohort design was used to sample 1,010 post-RP patients at high risk of recurrence treated between 2000-2006. Patients had preoperative PSA >20 ng/mL, Gleason ≥8, pT3b or GPSM score ≥10. Patients with metastasis at diagnosis or any prior treatment for prostate cancer were excluded. 20% random sampling created a subcohort that included all cases with metastasis. 22-marker GC scores were generated for 219 patients with available genomic data. Receiver operating characteristic and decision curves, competing risk, and weighted regression models assessed GC performance. Results GC had area under the curve of 0.79 for predicting 5-year metastasis post-RP. Decision curves showed that net benefit of GC exceeded clinical-only models. GC was the predominant predictor of metastasis in multivariable analysis. Cumulative incidence of metastasis at 5 years post-RP was 2.4%, 6.0% and 22.5% for patients with low (60% of patients), intermediate (21% of patients), and high (19% of patients) GC scores, respectively (p<0.001). Conclusions These results indicate that genomic information from the primary tumor can identify patients with adverse pathology who are most at risk for metastasis and potentially lethal prostate cancer.

Genomic Predictors of Outcome in Prostate Cancer

“…This panel has also been externally validated in multiple cohorts and is commercially available as the Decipher genetic test (GenomeDX Biosciences, Vancouver, BC, Canada). Four studies reported the utilization of this gene panel to predict BCR, metastatic progression, or DSS after RP plus or minus EBRT [61,[71][72][73]. The prognostic accuracy was highest when the genomic classifier and clinical models (CAPRA-S) were combined [61].…”

High-Throughput Transcriptomic Analysis Nominates Proteasomal Genes As Age Specific Biomarkers and Therapeutic Targets in Prostate Cancer

“…15 The MCII cohort consisted of a case-cohort study that sampled a cohort of 1010 high-risk men that underwent RP to generate a final cohort of 232 samples as described previously. 16 The TJU cohort is comprised of 143 patients with pT3 or margin-positive disease who underwent RP and post-RP radiotherapy of whom 130 microarray samples were available. 17 Patients from the CC cohort were obtained from a case-control study in which 2317 conservatively treated high-risk RP patients who did not receive adjuvant therapy were sampled to achieve a 3:1 ratio for nonmetastatic versus metastatic progression, for a total of 183 samples.…”

Construction of a set of novel and robust gene expression signatures predicting prostate cancer recurrence

“…Both the 17‐gene Oncotype DX and the 31‐gene Prolaris improve risk stratification of patients with high risk of PC recurrence at time of diagnosis (Albala et al ., 2016; Cuzick et al ., 2011; Klein et al ., 2014; Knezevic et al ., 2013; Oderda et al ., 2017) and after radical prostatectomy (RP) (Cooperberg et al ., 2013; Cullen et al ., 2015). The 22‐gene Decipher predicts metastasis following RP (Erho et al ., 2013; Karnes et al ., 2013; Klein et al ., 2016). While these and other biomarkers assist decision making and thus improve patient management, their clinical application requires further validation (Lamy et al ., 2017; Martin, 2016; McGrath et al ., 2016; Patel and Gnanapragasam, 2016; Ross et al ., 2016; Zhuang and Johnson, 2016).…”