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Stephen Pennington, Ph.D., Steve is currently Professor of Proteomics in the UCD Conway Institute of Biomolecular and Biomedical Research at University College Dublin. He graduated from Imperial College of Science and Technology (University of London) with a joint honours degree (Chemistry and Biochemistry) before completing a PhD in Biochemistry and taking up Elmore Medical Research Fellowship at the University of Cambridge. Steve’s research interests have always involved proteins and has established multi-user proteomics facilities in the UK and Ireland. His research team is currently focussed on clinical translational projects that are driven by well identified clinical needs particularly in the area of protein biomarker discovery by LC-MS and validation using MRM-based multiplexed measurements. Steve’s team were early adopters of Skyline and remain active users. Steve is a lead investigator in a Dublin based ‘Prostate Cancer Research Consortium’ and an EU consortium working on Innate Immunity in Inflammatory Arthritis.
Label-free LC-MS and MRM assay development for discovery and verification of biomarkers for organ confined prostate cancer
In Western countries, prostate cancer is the most common cancer diagnosed and the second most common cause of cancer-related deaths in men. Currently, serum prostate-specific antigen is the most widely used biomarker for the diagnosis and management of patients with PCa. However, it lacks sensitivity and specificity to adequately diagnose prostate cancer and is not effective for guiding key treatment decisions. Here label-free LC-MS/MS was used to investigate protein expression in affinity-depleted serum samples from prostate cancer patients and differentially expressed proteins (p< 0.05) were included in a candidate biomarker panel of 64 proteins that was assembled using this data as well as data from previous gel-based proteomics studies and the literature. Using Skyline, a multiple reaction monitoring (MRM) assay was developed for 31 of these proteins and its reproducibility established on replicates of affinity depleted and crude serum samples for which CV’s of 6.5% and 7.0% were observed. An initial verification of the 31 protein MRM assay on 63 independent PCa serum samples demonstrated the robustness of the assay and random forest analysis of the data revealed that it had a favorable predictive performance (AUC 0.824) for discriminating organ confined from non-organ confined prostate cancer. We are currently developing this assay for clinical use to support prostate cancer patient treatment decisions.
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