Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA189549 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Radical prostatectomy remains one of the more widely-used treatment options for men with prostate cancer. However, few molecular biomarkers have been established to predict patients who are at high risk of biochemical failure. To our knowledge, this is the first such report on miRNA profiling in radical prostatectomy tissue using Nanostring. In addition, this is the first report to look at the prognostic value of miRNAs in the setting of biochemical failure and salvage radiation therapy post-radical prostatectomy.
2015-01-23 | GSE65061 | GEO
Project description:Purpose: Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis. Methods: A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry that underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 that experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set. Results: Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67 - 0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression. Conclusion: A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer. 545 formalin-fixed paraffin-embedded (FFPE) tissue samples from primary prostate cancer obtained from Radical Prostatectomy.
2013-07-01 | E-GEOD-46691 | biostudies-arrayexpress
Project description:Radical prostatectomy remains one of the more widely-used treatment options for men with prostate cancer. However, few molecular biomarkers have been established to predict patients who are at high risk of biochemical failure. To our knowledge, this is the first such report on miRNA profiling in radical prostatectomy tissue using Nanostring. In addition, this is the first report to look at the prognostic value of miRNAs in the setting of biochemical failure and salvage radiation therapy post-radical prostatectomy. RNA was extracted from tumor-enriched 1mm cores from 43 radical prostatectomy paraffin tissue blocks. 800 miRNAs were profiled using the NanoString nCounter human miRNA assay. mirNAs were then correlated to clinical outcomes.
2015-01-23 | E-GEOD-65061 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the following subset Series: GSE26022: [Gene Expression Training Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy GSE26242: [Gene Expression Validation Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy GSE26245: [miRNA Training Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy GSE26247: [miRNA Validation Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy Refer to individual Series
2011-06-01 | E-GEOD-26367 | biostudies-arrayexpress
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:Clinical manifestation of PCa is highly variable. Aggressive tumors require radical treatment, while clinically non-significant ones may be suitable for active surveillance. We have previously developed the prognostic ProstaTrend signature mainly on prostatectomy specimens by application of transcriptome‐wide microarray and RNA-sequencing (RNA-Seq) analyses. We used a cohort of 185 tumor specimens obtained from FFPE biopsies for RNA-Seq to facilitate the application of ProstaTrend at the beginning of routine PCa diagnostic. All patients of the FFPE biopsy cohort were treated by radical prostatectomy (RPx) and median follow-up for biochemical recurrence (BCR) was 9 years. Based on the transcriptome data of the FFPE biopsies, we filtered ProstaTrend for genes susceptible to FFPE-associated degradation via regression analysis. ProstaTrend was additionally restricted to genes with concordant prognostic effects in the RNA-Seq TCGA prostate adenocarcinoma (PRAD) cohort to ensure robust and broad applicability. The prognostic relevance of the refined Transcriptomic Risk Score (TRS) was analyzed by Kaplan-Meier curves, Cox-regression models in our FFPE-biopsy cohort and 9 other public datasets from PCa patients with BCR as primary endpoint. The TRS based on the revised ProstaTrend signature, which included 204 genes, was significantly associated with BCR in the FFPE biopsy cohort (Cox-regression p-value <0.001). The TRS retained prognostic relevance when adjusted for Gleason Score (GS). We confirmed a significant association with BCR in 9 independent cohorts with a total of 1109 PCa patients. Comparison of the prognostic performance of the TRS with 17 other prognostically relevant PCa panels revealed that the revised ProstaTrend was among the best-ranked panels.
2023-09-08 | GSE220095 | 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:Purpose: Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis. Methods: A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry that underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 that experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set. Results: Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67 - 0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression. Conclusion: A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.
2013-07-01 | GSE46691 | GEO
Project description:The histopathological and molecular heterogeneity of prostate cancer and the limited availability of human tumor tissue make unraveling the mechanisms of prostate carcinogenesis a challenging task. Our goal was to develop an ex vivo model that could be reliably utilized to define a prognostic signature based on gene expression profiling of cell cultures that maintained the tumor phenotype. To this end, we derived epithelial cultures from tissue explanted from 59 patients undergoing radical prostatectomy or cistoprostatectomy because of Prostate Benign Hyperplasia/Prostate Cancer or Bladder Carcinoma. Patient selection criteria were absence of hormonal neo-adjuvant treatment before surgery and diagnosis of clinically localized disease. Using this unique experimental material we analyzed expression of 22.500 transcripts on the Affymetrix Human U133A Gene Chips platform. Cultures from normal/hyperplastic tissues with a prevalent luminal phenotype, and from normal prostate epithelial tissue with basal phenotype (PrEC) served as controls.,We have established a large number of prostate primary cultures highly enriched in the secretory phenotype. From them we derived an epithelial-restricted transcriptional signature that: 1) differentiated normal from tumor cells; and 2) clearly separated cancer derived lines into two distinct groups which correlated with indolent and aggressive clinical behavior of the disease. ,Our findings provide:1) a method to expand human primary prostate carcinoma cells with a luminal phenotype; 2) a powerful experimental model to study primary prostate cancer biology; and 3) a novel means to characterize these tumors from a molecular genetic standpoint for prognostic and/or predictive purposes.
2007-06-30 | E-GEOD-3868 | biostudies-arrayexpress
Project description:Androgens are a prequisite for the development of human prostate and prostate cancer. Androgen action is mediated via androgen receptor. Androgen ablation therapy is used for the treatment of metastasized prostate cancer. The aim of the study was to identify genes differentially expressed in benign human prostate, prostate cancer and in prostate tissue three days after castration. These genes are potential diagnostic and therapeutic targets for prostate cancer and benign prostatic hyperplasia. We used microarrays to examine the gene expression profiles in benign prostate adjacent to prostate cancer and prostate cancer in radical prostatectomy specimens and in prostate tissue samples taken 3 days after surgical castration performed for treatment of prostate cancer. Human prostate tissue was obtained from radical prostatectomy samples and from prostate biopsy samples (castrated samples). Benign and malignant tissues samples were microdissected from prostatectomy samples. Tissues were used for RNA isolation and were further processed as samples for microarray. Three prostatectomy samples were used as replicates (benign and malignant prostate). All prostate cancers were Gleason 3+3 pattern. Castrated tissue samples were taken from patients three days after surgical castration for the treatment of advanced or metastasized prostate cancer. Six biopsies were taken from each subject and individual subject samples were used as three replicates in microarray.
2012-06-19 | E-GEOD-32982 | biostudies-arrayexpress