Identification of a Prognostic Protein Profile for Triple Negative Breast Cancer
Conference Recording Jan 30, 2013
About the Speaker
Dr. Arzu Umar obtained her MSc degree in Biology from the University of Utrecht in 1998. Dr. Umar has been appointed as junior group leader of breast cancer proteomics within the Cancer Genomics Centre (2009), and the Molecular Medicine post-graduate School of the Erasmus MC (2010). She is currently expanding her research activities in the direction of identification and validation of markers for chemotherapy-resistance, prognostic markers for aggressive disease, early detection markers, as well as metabolomics, phosphoproteomics, and functional interaction studies.Abstract
Breast cancer is a very heterogeneous disease, consisting of different molecular subtypes. Women with the so-called ‘triple negative’ subtype of breast cancer have poor prognosis and survival compared to other subtypes, due to the aggressive nature of these tumors and current absence of suitable targets for therapy. Evidently, identification of appropriate prognostic protein targets for this group of patients is of vital importance. We have used a comparative tissue proteomics approach for the identification of prognostic protein markers for triple negative breast cancer.
For biomarker discovery, we selected 63 fresh frozen primary tumor tissues, of which 25 patients developed local and/or distant metastasis within 60 months (poor prognosis), and 38 remained metastasis free for > 60 months (good prognosis). Tissues were subjected to laser capture microdissection to selectively collect ~4,000 tumor epithelial cells, corresponding to sub-microgram protein, per tissue. Proteins were extracted, trypsin digested and subjected to nanoscale liquid chromatography coupled to high resolution mass spectrometry. Label-free quantitation was performed to retrieve protein abundance data, which served as input for comparative proteome analysis using mixed-effect analysis of variance and leave-one-out cross-validation.
Using our tissue proteomics pipeline, we identified a 15-protein predictor that significantly correlates with metastasis free survival (p<0.001).
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