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Br J Cancer ; 111(6): 1201-12, 2014 Sep 09.
Article in English | MEDLINE | ID: mdl-25032733

ABSTRACT

BACKGROUND: Key challenges of biopsy-based determination of prostate cancer aggressiveness include tumour heterogeneity, biopsy-sampling error, and variations in biopsy interpretation. The resulting uncertainty in risk assessment leads to significant overtreatment, with associated costs and morbidity. We developed a performance-based strategy to identify protein biomarkers predictive of prostate cancer aggressiveness and lethality regardless of biopsy-sampling variation. METHODS: Prostatectomy samples from a large patient cohort with long follow-up were blindly assessed by expert pathologists who identified the tissue regions with the highest and lowest Gleason grade from each patient. To simulate biopsy-sampling error, a core from a high- and a low-Gleason area from each patient sample was used to generate a 'high' and a 'low' tumour microarray, respectively. RESULTS: Using a quantitative proteomics approach, we identified from 160 candidates 12 biomarkers that predicted prostate cancer aggressiveness (surgical Gleason and TNM stage) and lethal outcome robustly in both high- and low-Gleason areas. Conversely, a previously reported lethal outcome-predictive marker signature for prostatectomy tissue was unable to perform under circumstances of maximal sampling error. CONCLUSIONS: Our results have important implications for cancer biomarker discovery in general and development of a sampling error-resistant clinical biopsy test for prediction of prostate cancer aggressiveness.


Subject(s)
Biomarkers, Tumor/analysis , Prostate/pathology , Prostatic Neoplasms/mortality , Prostatic Neoplasms/pathology , Actinin/analysis , Aged , Alkyl and Aryl Transferases/analysis , Area Under Curve , Biopsy, Fine-Needle , Cullin Proteins/analysis , DNA-Binding Proteins/analysis , Follow-Up Studies , HSP70 Heat-Shock Proteins/analysis , Humans , Image Processing, Computer-Assisted , Male , Membrane Proteins/analysis , Middle Aged , Mitochondrial Proteins/analysis , Neoplasm Grading , Neoplasm Staging , Phosphorylation , Prostate/chemistry , Prostatic Neoplasms/chemistry , Proteomics , RNA-Binding Protein FUS , ROC Curve , Ribosomal Protein S6/analysis , Ribosomal Protein S6/metabolism , Selection Bias , Smad2 Protein/analysis , Smad4 Protein/analysis , Tissue Array Analysis , Voltage-Dependent Anion Channel 1/analysis , Y-Box-Binding Protein 1/analysis
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