Genomic Prostate Cancer Classifier Predicts Biochemical Failure and Metastases in Patients After Postoperative Radiation Therapy

Abstract

Purpose To test the hypothesis that a genomic classifier (GC) would predict biochemical failure (BF) and distant metastasis (DM) in men receiving radiation therapy (RT) after radical prostatectomy (RP). Methods and Materials Among patients who underwent post-RP RT, 139 were identified for pT3 or positive margin, who did not receive neoadjuvant hormones and had paraffin-embedded specimens. Ribonucleic acid was extracted from the highest Gleason grade focus and applied to a high-density-oligonucleotide microarray. Receiver operating characteristic, calibration, cumulative incidence, and Cox regression analyses were performed to assess GC performance for predicting BF and DM after post-RP RT in comparison with clinical nomograms. Results The area under the receiver operating characteristic curve of the Stephenson model was 0.70 for both BF and DM, with addition of GC significantly improving area under the receiver operating characteristic curve to 0.78 and 0.80, respectively. Stratified by GC risk groups, 8-year cumulative incidence was 21%, 48%, and 81% for BF (P<.0001) and for DM was 0, 12%, and 17% (P=.032) for low, intermediate, and high GC, respectively. In multivariable analysis, patients with high GC had a hazard ratio of 8.1 and 14.3 for BF and DM. In patients with intermediate or high GC, those irradiated with undetectable prostate-specific antigen (PSA ≤0.2 ng/mL) had median BF survival of >8 years, compared with <4 years for patients with detectable PSA (>0.2 ng/mL) before initiation of RT. At 8 years, the DM cumulative incidence for patients with high GC and RTwith undetectable PSA was 3%, compared with 23% with detectable PSA (P=.03). No outcome differences were observed for low GC between the treatment groups. Conclusion The GC predicted BF and metastasis after post-RP irradiation. Patients with lower GC risk may benefit from delayed RT, as opposed to those with higher GC; however, this needs prospective validation. Genomic-based models may be useful for improved decision-making for treatment of high-risk prostate cancer.

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

“…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. 18 RNA extraction and microarray hybridization were performed using clinicalgrade techniques in a Clinical Laboratory Improvement Amendmentscertified laboratory facility (GenomeDx Biosciences, San Diego, CA, USA).…”

“…The normalization and summarization of the microarray samples were done with the Single Channel Array Normalization algorithm with quality control performed as described previously. [16][17][18][19] Gene expression for each gene was calculated using the Affymetrix Core level summaries for annotated genes. Microarray data are available on the NCBI Gene Expression Omnibus as accession numbers GSE46691 (MCI), GSE62116 (MCII) and GSE62667 (CC).…”

Active Surveillance of Prostate Cancer: Use, Outcomes, Imaging, and Diagnostic Tools

“…Beyond the platforms discussed above, additional tests continue to emerge and may prove useful. For example, the Decipher genomic classifier (GenomeDx Biosciences, Vancouver, BC, Canada), which has been previously validated for predicting metastasis after surgery based on prostatectomy tissue, 90–92 has more recently demonstrated prognostic value in biopsy specimens. 93 At the same time, the majority of current evidence has been gleaned from retrospective analyses of cohorts that were not established with a surveillance approach in mind.…”

Clinical Utility of Biomarkers in Localized Prostate Cancer

“…Decipher assay independent predictor of 5-year metastatic progression (AUC 0.79), out-performed clinical characteristics Den [44] 139 patients identified who received post-RP radiation therapy for pT3 disease or PSM. FFPE formalin-fixed paraffin-embedded, RP radical prostatectomy, BCR biochemical recurrence, MFS metastasis free survival, CCP cell cycle progression, CAPRA Cancer of the Prostate Risk Assessment, Bx biopsy, CSS cancer-specific survival, EBRT external beam radiation therapy, GS Gleason score and intermediate-clinical-risk disease-ostensibly candidates for AS-the GPS assay was examined for the prediction of adverse surgical pathology, defined as primary Gleason pattern 4 or higher, and/or non-organ confined disease (≥pT3a or lymph node positive) [39].…”

“…Refinement of the high-density micro-arrays from roughly 1.4 million features was performed using machine learning algorithms to select a panel of markers highly associated with clinical outcomes. The Decipher score (reported on a 0-1 scale) predicting early metastatic progression following RP was examined in a cohort of men with adverse pathological features, where increases in the GC score were significantly associated with metastatic progression and PCSM, even after adjustment for the CAPRA-S score or other models incorporating postprostatectomy data [42,43] The Decipher genomic classifier was evaluated for the ability to selectively identify men who may benefit from adjuvant radiation therapy following RP [44]. Den et al reported on 188 men with pathological T3 or marginpositive PCa at surgery who were treated with adjuvant radiation therapy at two institutions between 1990 and 2009.…”