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Development of a label‐free LC‐MS/MS strategy to approach the identification of candidate protein biomarkers of disease recurrence in prostate cancer patients in a clinical trial of combined hormone and radiation therapy
Author(s) -
Morrissey Brian,
O'Shea Carmel,
Armstrong John,
Rooney Cathy,
Staunton Lisa,
Sheehan Martina,
Shan Aoife M.,
Pennington Stephen R.
Publication year - 2013
Publication title -
proteomics – clinical applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 54
eISSN - 1862-8354
pISSN - 1862-8346
DOI - 10.1002/prca.201300004
Subject(s) - prostate cancer , biomarker , medicine , oncology , disease , clinical trial , biomarker discovery , label free quantification , cancer , bioinformatics , proteomics , quantitative proteomics , biology , biochemistry , gene
Purpose Combined hormone and radiation therapy (CHRT) is one of the principle curative regimes for localised prostate cancer (PCa). Following treatment, many patients subsequently experience disease recurrence however; current diagnostics tests fail to predict the onset of disease recurrence. Biomarkers that address this issue would be of significant advantage. Experimental design Label‐free LC‐MS/MS for protein biomarker discovery and MRM for targeted confirmation were applied to patient serum samples accrued in a non‐interventional clinical trial of CHRT. Results Analysis of time‐matched patient samples from a patient with disease recurrence compared with a time match disease‐free individual supported the identification of 287 proteins. Of these, 141 proteins were quantified, 95 proteins changed in their expression ( P ≤ 0.05 and ≥1.5‐fold change) and of these 16 were selected for MRM confirmation. The protein expression changes observed in the label‐free LC‐MS/MS and MRM analysis were found to be highly correlated ( R 2 = 0.85). Conclusions and clinical relevance The establishment of a clinical trial to support the acquisition of samples and development of a pipeline for MS‐based biomarker discovery and validation should contribute to the identification of a serum protein signature to predict or monitor the outcome of treatment of patients with PCa.