Citations

Abstract Background ETS gene fusions and PTEN loss are the most common genomic alteration events in prostate cancer that stratify patients into molecularly distinct subtypes. Recently, a commercially available genomic classifier (GC, DecipherTM) has been validated to predict metastasis in patients with high-risk prostate cancer. In this study we evaluated the robustness of the GC model in the light of prostate cancer molecular heterogeneity. Methods: Analysis of PTEN expression loss, ERG overexpression and GC scores were performed on a previously reported genome-wide microarray dataset (Karnes et al., 2013). Microarray probesets mapping to exons of PTEN were benchmarked to identify a representative exon whose loss of expression was highly correlated to FISH analysis for PTEN deletion (PTEN-loss) performed on an independent dataset. ERG overexpression (ERG+) was determined similarly and the microarray probesets that best correlate with the FISH status (in independent dataset) were used to annotate the ERG status of the samples in this study. Receiver operating characteristic area under the curve (AUC), and multivariable regression analysis were used to assess GC performance for predicting metastasis in subsets of patient tumors with genomic alterations. Results Using the microarray method 42% of the tumors had ERG overexpression, 23% had PTEN-loss and 11% had both events. ERG+ and PTEN-loss were not on their own predictive of metastasis with AUCs of 0.52 (CI:0.44-0.6) and 0.47(CI 0.39-0.56), respectively. In ERG+ tumors GC had an AUC of 0.82 (p=4e-7) and high GC score ERG+ patients had a hazard ratio (HR) of 100 (p=6e-7) for metastasis. Similarly, in PTEN-loss tumors GC had an AUC of 0.83 (p=7.9e-5) and high GC score PTEN-loss patients had an HR of 62 (p=1.8e-4) for metastasis. When GC was evaluated in patient tumors with both ERG+ and PTEN-loss the AUC was 0.85 (p=2e-15) and high GC score patients had an HR of 95 (p=4e-4) for metastasis. In multivariable analysis, Decipher was the only significant risk factor with an HR of 55 (CI 12.2-249, p=1.8e-7) for metastasis after adjusting for ERG+, PTEN-loss, Gleason score, PSA, tumor stage, margins and adjuvant treatment. Conclusion The Decipher genomic classifier is a powerful prognostic marker that retains its significance for predicting metastasis in subsets of patients with tumors that harbor common genomic alterations. Patients with high GC scores and these alterations may represent an extremely high-risk subset of prostate cancer. Citation Format: Mohammed Alshalalfa, Ismael Vergara, Nicholas Erho, Elai Davicioni, Robert Jenkins, Kollmeyer Thomas. Evaluation of a genomic classifier (Decipher®) in subsets of primary tumors with common prostate cancer genomic alterations. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4720. doi:10.1158/1538-7445.AM2014-4720

Objective To better characterize the genomics of patients with biochemical recurrence ( BCR ) who have metastatic disease progression in order to improve treatment decisions for prostate cancer. Methods The expression profiles of three clinical outcome groups after radical prostatectomy ( RP ) were compared: those with no evidence of disease ( NED ; n = 108); those with BCR (rise in prostate‐specific antigen [ PSA ] level without metastasis; n = 163); and those with metastasis ( n = 192). The patients were profiled using Human Exon 1.0 ST microarrays, and outcomes were supported by a median 18 years of follow‐up. A metastasis signature was defined and verified in an independent RP cohort to ensure the robustness of the signature. Furthermore, bioinformatics characterization of the signature was conducted to decipher its biology. Results Minimal gene expression differences were observed between adjuvant treatment‐naïve patients in the NED group and patients without metastasis in the BCR group. More than 95% of the differentially expressed genes (metastasis signature) were found in comparisons between primary tumours of metastasis patients and the two other outcome groups. The metastasis signature was validated in an independent cohort and was significantly associated with cell cycle genes, ubiquitin‐mediated proteolysis, DNA repair, androgen, G‐protein coupled and NOTCH signal transduction pathways. Conclusion This study shows that metastasis development after BCR is associated with a distinct transcriptional programme that can be detected in the primary tumour. Patients with NED and BCR have highly similar transcriptional profiles, suggesting that measurement of PSA on its own is a poor surrogate for lethal disease. Use of genomic testing in patients undergoing RP with an initial rise in PSA level may be useful to improve secondary therapy decision‐making.