Molecular and genomic analysis of microscopic quantities of tumor from formalin-fixed and paraffin-embedded (FFPE) biopsies has many unique challenges. Here we evaluated the feasibility of obtaining transcriptome-wide RNA expression to measure prognostic classifiers from diagnostic prostate needle core biopsies.
Application of a Clinical Whole-Transcriptome Assay for Staging and Prognosis of Prostate Cancer Diagnosed in Needle Core Biopsy Specimens.
Specimen part, Subject
View SamplesTo 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).
The Landscape of Prognostic Outlier Genes in High-Risk Prostate Cancer.
Age
View SamplesPurpose: 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.
Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy.
No sample metadata fields
View SamplesPurpose: 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.
Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population.
Age
View SamplesTo 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.
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.
Age
View SamplesBACKGROUND: 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.
A genomic classifier predicting metastatic disease progression in men with biochemical recurrence after prostatectomy.
Specimen part
View SamplesTo test whether a genomic classifier (GC) predicts development of metastatic disease in patients treated with salvage radiation therapy (SRT) after radical prostatectomy (RP).
Utilization of a Genomic Classifier for Prediction of Metastasis Following Salvage Radiation Therapy after Radical Prostatectomy.
Specimen part
View SamplesBackground: To date, few studies have systematically characterized microarray gene expression signal performance with degraded RNA from formalin-fixed paraffin-embedded (FFPE) specimens in comparison to intact RNA from unfixed fresh-frozen (FF) specimens.
Quantitative expression profiling in formalin-fixed paraffin-embedded samples by affymetrix microarrays.
Specimen part, Subject
View SamplesYB-1 controls epithelial-mesenchymal transitions by restricting translation of growth-related mRNAs and enabling expression of EMT-inducing transcription factors. We used microarrays to characterize the direct transcriptional and indirect translational regulation of mRNAs by exogenous YB-1 in breast cancer cell lines.
Translational activation of snail1 and other developmentally regulated transcription factors by YB-1 promotes an epithelial-mesenchymal transition.
No sample metadata fields
View SamplesThis SuperSeries is composed of the SubSeries listed below.
No associated publication
Age, Specimen part
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