A genomic classifier improves prediction of metastatic disease within 5 years after surgery in node-negative high-risk prostate cancer patients managed by radical prostatectomy without adjuvant therapy
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE62667 | GEO | 2014/11/21
SECONDARY ACCESSION(S): PRJNA266309
REPOSITORIES: GEO
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Project description:To determine whether adding Decipher to standard risk stratification tools (CAPRA-S and Stephenson nomogram) improves accuracy in prediction of metastatic disease within 5 years after surgery in men with adverse pathologic features after RP. The study population consisted of 182 patients selected from 2,641 men who underwent RP at the Cleveland Clinic between 1987-2008 who met the following criteria: 1) preoperative PSA>20 ng/mL, stage pT3 or margin positive, or Gleason score >/8; 2) pathologic node negative; 3) undetectable post-RP PSA; 4) no neoadjuvant or adjuvant therapy; and 5) minimum of 5 years follow-up for the controls.
2014-11-21 | E-GEOD-62667 | biostudies-arrayexpress
Project description:BACKGROUND: Due to their varied outcomes, men with biochemical recurrence (BCR) following radical prostatectomy (RP) present a management dilemma. Here, we evaluate Decipher, a genomic classifier (GC), for its ability to predict metastasis following BCR. METHODS: The study population included 85 clinically high-risk patients who developed BCR after RP. Time-dependent receiver operating characteristic (ROC) curves, weighted Cox proportional hazard models, and decision curves were used to compare GC scores to Gleason score (GS), PSA doubling time (PSAdT), time to BCR (ttBCR), the Stephenson nomogram, and CAPRA-S for predicting metastatic disease progression. All tests were two-sided with a type I error probability of 5%. RESULTS: GC scores stratified men with BCR into those who would or would not develop metastasis (8% of patients with low versus 40% with high scores developed metastasis, p<0.001). The area under the curve for predicting metastasis after BCR was 0.82 (95% CI, 0.76-0.86) for GC, compared to GS 0.64 (0.58-0.70), PSAdT 0.69 (0.61-0.77) and ttBCR 0.52 (0.46-0.59). Decision curve analysis showed that GC scores had a higher overall net benefit compared to models based solely on clinicopathologic features. In multivariable modeling with clinicopathologic variables, GC score was the only significant predictor of metastasis (p=0.003). CONCLUSIONS: When compared to clinicopathologic variables, GC better predicted metastatic progression among this cohort of men with BCR following RP. While confirmatory studies are needed, these results suggest that use of GC may allow for better selection of men requiring earlier initiation of treatment at the time of BCR.
2016-09-19 | GSE51066 | GEO
Project description: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. Method: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 235 patients with available genomic data. ROC and decision curves, competing risk and weighted regression models were used to assess genomic classifier performance. Patients treated with RP between 2000 and 2006 were identified from the Mayo Clinic tumor registry for a case-cohort study design. This involved identification of all patients with metastasis and a representative of the full cohort .Thus, men at high risk of recurrence post-RP (open/robotic) with no prior neoadjuvant/prostate cancer treatment were selected based on any of preoperative PSA >20 ng/mL, pathological Gleason score (GS) â¥8, SVI, or GPSM (GS; preoperative PSA; SVI; margins) score â¥10.The cohort of 1,010 men included 73 cases with metastasis as evidenced by CT or bone scan. A 20% random sample (n=202) was drawn from the cohort. This included 19 of 73 metastatic cases. To increase sampling of metastasis, the remaining 54 metastatic cases were added, resulting in a final study set of 256 patients. 21 samples did not pass QC and were eliminated from this study. Patients not experiencing metastasis regardless of BCR (defined as follow-up PSA â¥0.4 ng/mL >30 days post-RP) were censored at last follow-up. The study was approved by Mayo Clinic Institutional Review Board.
2014-11-13 | E-GEOD-62116 | biostudies-arrayexpress
Project description:To test whether a genomic classifier (GC) predicts development of metastatic disease in patients treated with salvage radiation therapy (SRT) after radical prostatectomy (RP).
2017-04-04 | GSE79915 | GEO
Project description:Accurate estimation of recurrence risk is needed for optimal treatment of clinically localized prostate cancer (PCa) patients. We combined classical clinicopathological predictors of biochemical recurrence (BCR) and molecular markers into a new risk score for predicting BCR after radical prostatectomy (RP). A retrospective study which included 122 PCa patients who attended our department between 2000 and 2007 was conducted. Total RNA was isolated from formalin-fixed paraffin embedded PCa tissue. Gene expression patterns were analyzed in 21 selected samples from 7 localized, 6 locally advanced, and 8 metastatic PCa patients using Illumina microarrays. Expression levels of 41 genes were validated by quantitative PCR in an independent retrospective series of 101 patients with ≥ 10 years follow-up who underwent RP for clinically localized PCa. Univariate and multivariate logistic regression analysis were used to identify individual predictors of BCR. A risk score (RS) for predicting BCR including clinicopathological and gene expression data was developed and Kaplan-Meier curves were generated. Interaction networks between the genes of the model were built by GeneMANIA Cytoscape plugin. In a median follow-up of 120 months (range 3-190), 37 patients developed BCR (36.6%). Expression levels of 7,930 transcripts differed between clinically localized and locally advanced-metastatic PCa groups (FDR<0.1). A set of 41 genes were validated by quantitative PCR. Regression analysis showed that expression of ASF1B and MCL1 as well as Gleason score, extracapsular extension, seminal vesicle invasion and positive margins were independent prognostic factors of BCR. A RS generated using these gene expression and clinicopathological variables was able to discriminate between two groups of patients with a significantly different probability of BCR (HR 6.24; CI 3.23-12.4, p<0.01), improving the individual discriminative performance of each of these variables on their own. Direct interactions between the two genes of the model were not found. In conclusion, identification of new gene expression patterns associated with a high probability of BCR in clinically localized PCa patients treated with RP, and their combination with clinicopathological variables in a robust, easy-to-use and reliable classifier may contribute to improve PCa risk stratification and, consequently, to tailor treatment and surveillance strategies in these patients.
2024-04-23 | GSE149226 | GEO
Project description: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. Method: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 235 patients with available genomic data. ROC and decision curves, competing risk and weighted regression models were used to assess genomic classifier performance.
2014-11-13 | GSE62116 | GEO
Project description:PurposeThe optimal timing of postoperative radiotherapy (RT) after radical prostatectomy (RP) is unclear. We hypothesized that a genomic classifier (GC) would provide prognostic and predictive insight into the development of clinical metastases in men receiving post-RP RT and inform decision making.Patients and methodsGC scores were calculated from 188 patients with pT3 or margin-positive prostate cancer, who received post-RP RT at Thomas Jefferson University and Mayo Clinic between 1990 and 2009. The primary end point was clinical metastasis. Prognostic accuracy of the models was tested using the concordance index for censored data and decision curve analysis. Cox regression analysis tested the relationship between GC and metastasis.ResultsThe cumulative incidence of metastasis at 5 years after RT was 0%, 9%, and 29% for low, average, and high GC scores, respectively (P = .002). In multivariable analysis, GC and pre-RP prostate-specific antigen were independent predictors of metastasis (both P < .01). Within the low GC score (< 0.4), there were no differences in the cumulative incidence of metastasis comparing patients who received adjuvant or salvage RT (P = .79). However, for patients with higher GC scores (≥ 0.4), cumulative incidence of metastasis at 5 years was 6% for patients treated with adjuvant RT compared with 23% for patients treated with salvage RT (P < .01).ConclusionIn patients treated with post-RP RT, GC is prognostic for the development of clinical metastasis beyond routine clinical and pathologic features. Although preliminary, patients with low GC scores are best treated with salvage RT, whereas those with high GC scores benefit from adjuvant therapy. These findings provide the first rational selection of timing for post-RP RT.
| S-EPMC4884273 | biostudies-literature
Project description:Purpose: Available tools for prostate cancer (PC) diagnosis and prognosis are suboptimal and novel biomarkers are urgently needed. Here, we investigated the regulation and biomarker potential of the GABRE~miR-452~miR-224 genomic locus. Experimental design: GABRE/miR-452/miR-224 transcriptional expression was quantified in 80 non-malignant and 281 PC tissue samples. GABRE promoter methylation was determined by methylation-specific qPCR (MethyLight) in 35 non-malignant, 293 PC (radical prostatectomy (RP) cohort 1) and 198 PC tissue samples (RP cohort 2). Diagnostic/prognostic biomarker potential of GABRE methylation was evaluated by ROC, Kaplan-Meier, uni- and multivariate Cox regression analyses. Functional roles of miR-224 and miR-452 were investigated in PC3 and DU145 cells by viability, migration, and invasion assays and gene-set enrichment analysis (GSEA) of post-transfection transcriptional profiling data. Results: GABRE~miR-452~miR-224 was significantly downregulated in PC compared to non-malignant prostate tissue and had highly cancer-specific aberrant promoter hypermethylation (AUC=0.98). Functional studies and GSEA suggested that miR-224 and miR-452 inhibit proliferation, migration, and invasion of PC3 and DU145 cells by direct/indirect regulation of pathways related to the cell cycle and cellular motility. Finally, in uni- and multivariate analyses, high GABRE promoter methylation was significantly associated with biochemical recurrence in RP cohort 1, which was successfully validated in RP cohort 2. Conclusion: The GABRE~miR-452~miR-224 locus is downregulated and hypermethylated in PC and is a new promising epigenetic candidate biomarker for PC diagnosis and prognosis. Tumor suppressive functions of the intronic miR-224 and miR-452 were demonstrated in two PC cell lines, suggesting that epigenetic silencing of GABRE~miR-452~miR-224 may be selected for in PC.
2013-12-11 | GSE51053 | GEO
Project description:Purpose: Available tools for prostate cancer (PC) diagnosis and prognosis are suboptimal and novel biomarkers are urgently needed. Here, we investigated the regulation and biomarker potential of the GABRE~miR-452~miR-224 genomic locus. Experimental design: GABRE/miR-452/miR-224 transcriptional expression was quantified in 80 non-malignant and 281 PC tissue samples. GABRE promoter methylation was determined by methylation-specific qPCR (MethyLight) in 35 non-malignant, 293 PC (radical prostatectomy (RP) cohort 1) and 198 PC tissue samples (RP cohort 2). Diagnostic/prognostic biomarker potential of GABRE methylation was evaluated by ROC, Kaplan-Meier, uni- and multivariate Cox regression analyses. Functional roles of miR-224 and miR-452 were investigated in PC3 and DU145 cells by viability, migration, and invasion assays and gene-set enrichment analysis (GSEA) of post-transfection transcriptional profiling data. Results: GABRE~miR-452~miR-224 was significantly downregulated in PC compared to non-malignant prostate tissue and had highly cancer-specific aberrant promoter hypermethylation (AUC=0.98). Functional studies and GSEA suggested that miR-224 and miR-452 inhibit proliferation, migration, and invasion of PC3 and DU145 cells by direct/indirect regulation of pathways related to the cell cycle and cellular motility. Finally, in uni- and multivariate analyses, high GABRE promoter methylation was significantly associated with biochemical recurrence in RP cohort 1, which was successfully validated in RP cohort 2. Conclusion: The GABRE~miR-452~miR-224 locus is downregulated and hypermethylated in PC and is a new promising epigenetic candidate biomarker for PC diagnosis and prognosis. Tumor suppressive functions of the intronic miR-224 and miR-452 were demonstrated in two PC cell lines, suggesting that epigenetic silencing of GABRE~miR-452~miR-224 may be selected for in PC. Affymetrix GeneChip Human Gene 1.0 ST Arrays were used for whole-genome transcriptional profiling of DU145 and PC3 cells at 48 hours post-transfection with either miR-224, miR-452, or scrambled miRNA mimics, or untransfected. All experiments were performed in duplicate. Transcript expression levels were determined after RMA16 normalization in GeneSpringGX 11.0 software (Agilent). PC3 and DU145 arrays were normalized separately. DU145 48 t Scr2a were excluded from the study because of bad performance of this microarray.
2013-12-11 | E-GEOD-51053 | biostudies-arrayexpress
Project description:OBJECTIVE:To evaluate the performance of the Decipher test in predicting lymph node invasion (LNI) on radical prostatectomy (RP) specimens. METHODS:We identified 1,987 consecutive patients with RP who received the Decipher test between February and August 2015 (contemporary cohort). In the contemporary cohort, only the Decipher score from RP specimens was available for analysis. In addition, we identified a consecutive cohort of patients treated with RP between 2006 and 2012 at the University of California, San Diego, with LNI upon pathologic examination (retrospective cohort). The retrospective cohort yielded seven, 22, and 18 tissue specimens from prostate biopsy, RP, and lymph nodes (LNs) for individual patients, respectively. Univariable and multivariable logistic regression analyses were used to evaluate the performance of Decipher in the contemporary cohort with LNI as the endpoint. In the retrospective cohort, concordance of risk groups was assessed using validated cut-points for low (<0.45), intermediate (0.45-0.60), and high (>0.60) Decipher scores. RESULTS:In the contemporary cohort, 51 (2.6%) patients had LNI. Decipher had an odds ratio of 1.73 (95% confidence interval, 1.46-2.05) and 1.42 (95% confidence interval, 1.19-1.7) per 10% increase in score on univariable and multivariable (adjusting for pathologic Gleason score, extraprostatic extension, and seminal vesicle invasion), respectively. No significant difference in the clinical and pathologic characteristics between the LN positive patients of contemporary and retrospective cohorts was observed (all P>0.05). Accordingly, among LN-positive patients in the contemporary cohort and retrospective cohort, 80% and 77% had Decipher high risk scores (P=1). In the retrospective cohort, prostate biopsy cores with the highest Gleason grade and percentage of tumor involvement had 86% Decipher risk concordance with both RP and LN specimens. CONCLUSION:Decipher scores were highly concordant between pre- and post-surgical specimens. Further, Decipher scores from RP tissue were predictive of LNI at RP. If validated in a larger cohort of prostate biopsy specimens for prediction of adverse pathology at RP, Decipher may be useful for improved pre-operative staging.
| S-EPMC4935177 | biostudies-literature