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Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error

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2014

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Nature Publishing Group
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Shipitsin, M., C. Small, S. Choudhury, E. Giladi, S. Friedlander, J. Nardone, S. Hussain, et al. 2014. “Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error.” British Journal of Cancer 111 (6): 1201-1212. doi:10.1038/bjc.2014.396. http://dx.doi.org/10.1038/bjc.2014.396.

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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.

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prostate cancer, biopsy, biomarkers, prognosis, sampling error, tumour heterogeneity

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