Proteomic and Bioinformatic Approaches to the Development of Biomarker Signatures of Clinical Utility for Prostate Cancer: From Discovery to Targeted Verification

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December 12, 2012

Stephen Pennington, Professor of Proteomics, University College Dublin

Abstract

Prostate cancer (PCa) is the most common male cancer and third leading cause of cancer deaths among men in western world. Current tests including; the serum biomarker PSA, digital rectal examination, Gleason grading of biopsies and MRI imaging provide an effective means diagnosis however, their prognostic and predictive capabilities are limited. Notably PSA, the widely used serum biomarker cannot distinguish indolent from aggressive disease – a critical distinction for the selection of appropriate therapeutic intervention. So, biomarkers that could identify aggressive PCa, predict biochemical/clinical recurrence or determine prognosis following treatment are of upmost clinical importance.Using high quality clinical samples and robust analytical methods we have undertaken a label-free LC-MS/MS and MRM workflow for the discovery and verification of biomarker signatures of potential clinical utility. Pre-treatment serum samples were collected, prepared and stored under highly controlled (SOP) conditions as part of the Irish prostate cancer research consortium bioresource. After removing the 14 most abundant serum proteins, differentially expressed proteins were identified by LC-MS/MS and candidates assembled and prioritized using feature selection and classification methods. Candidate protein biomarkers (64) have been included in an MRM development programme and high quality multiplexed MRM assays have been developed, refined and optimized. In an initial iteration an assay for 31 proteins has used to develop a protein biomarker signature for the prediction of extra-capsular extension of the prostate. With improvements to include additional candidates and careful validation on large patient cohorts, this approach to biomarker signature development has the potential to improve diagnosis and help to identify the most beneficial treatments for PCa patients

Proteomics and Metabolomics

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