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Braakman R.B.H.,Erasmus Medical Center | Braakman R.B.H.,Center for Translational Molecular Medicine | Umar A.,Erasmus Medical Center | Umar A.,Center for Translational Molecular Medicine
Current Proteomics | Year: 2013

Posttranslational modifications (PTMs) are dynamic regulators of protein function, and play important roles in diseases such as cancer. PTM analysis can be challenging, the stoichiometry of PTMs is often low, and various combinatorial modifications are possible. Currently, two major techniques are used to detect and characterize PTMs, immunoassays and mass spectrometry. Immunoassays rely on antibodies for detection of the protein of interest, and are therefore limited to targeted analysis. Mass spectrometry, on the other hand, is capable of characterizing posttranslational modifications both in targeted or non-targeted methods. Recently, new immunoassays were introduced that improve current methods, but also appear particularly promising in the analysis of PTMs. Two of these new immunoassays, proximity ligation assay and nanoscale immunoassay, are discussed in this review. In contrast to immunoassays, mass spectrometry enables characterization of a priori unknown PTM sites. A bottom-up approach, in which proteins are digested into smaller peptides, is well suited for targeted assays as well as cataloging PTMs. A top-down approach, where intact proteins are measured, is challenging but allows mapping of combinatorial PTMs. Mass spectrometry and immunoassays are therefore complementary techniques in analysis of PTMs. Advances in these methods now enable extremely sensitive detection of PTMs from very little material (immunoassays), or can fully characterize combinatorial modifications on proteins in both targeted and non-targeted ways (mass spectrometry). Recent developments in these techniques discussed in this review will therefore likely play an important role in current and future PTM analysis, particularly in the field of cancer research. © 2013 Bentham Science Publishers. Source


Liu N.Q.,Erasmus Medical Center | Liu N.Q.,Erasmus University Rotterdam | Liu N.Q.,Netherlands Proteomics Center | Stingl C.,Erasmus University Rotterdam | And 31 more authors.
Journal of the National Cancer Institute | Year: 2014

Background: Clinical outcome of patients with triple-negative breast cancer (TNBC) is highly variable. This study aims to identify and validate a prognostic protein signature for TNBC patients to reduce unnecessary adjuvant systemic therapy. Methods: Frozen primary tumors were collected from 126 lymph node-negative and adjuvant therapy-naive TNBC patients. These samples were used for global proteome profiling in two series: an in-house training (n = 63) and a multicenter test (n = 63) set. Patients who remained free of distant metastasis for a minimum of 5 years after surgery were defined as having good prognosis. Cox regression analysis was performed to develop a prognostic signature, which was independently validated. All statistical tests were two-sided. Results: An 11-protein signature was developed in the training set (median follow-up for good-prognosis patients = 117 months) and subsequently validated in the test set (median follow-up for good-prognosis patients = 108 months) showing 89.5% sensitivity (95% confidence interval [CI] = 69.2% to 98.1%), 70.5% specificity (95% CI = 61.7% to 74.2%), 56.7% positive predictive value (95% CI = 43.8% to 62.1%), and 93.9% negative predictive value (95% CI = 82.3% to 98.9%) for poor-prognosis patients. The predicted poor-prognosis patients had higher risk to develop distant metastasis than the predicted good-prognosis patients in univariate (hazard ratio [HR] = 13.15; 95% CI = 3.03 to 57.07; P =.001) and multivariable (HR = 12.45; 95% CI = 2.67 to 58.11; P =.001) analysis. Furthermore, the predicted poor-prognosis group had statistically significantly more breast cancer-specific mortality. Using our signature as guidance, more than 60% of patients would have been exempted from unnecessary adjuvant chemotherapy compared with conventional prognostic guidelines. Conclusions: We report the first validated proteomic signature to assess the natural course of clinical TNBC. © The Author 2014. All rights reserved. Source


Liu N.Q.,Erasmus Medical Center | Liu N.Q.,Netherlands Proteomics Center | Braakman R.B.H.,Erasmus Medical Center | Braakman R.B.H.,Center for Translational Molecular Medicine | And 13 more authors.
Journal of Mammary Gland Biology and Neoplasia | Year: 2012

Mass spectrometry (MS)-based label-free proteomics offers an unbiased approach to screen biomarkers related to disease progression and therapy-resistance of breast cancer on the global scale. However, multi-step sample preparation can introduce large variation in generated data, while inappropriate statistical methods will lead to false positive hits. All these issues have hampered the identification of reliable protein markers. A workflow, which integrates reproducible and robust sample preparation and data handling methods, is highly desirable in clinical proteomics investigations. Here we describe a label-free tissue proteomics pipeline, which encompasses laser capture microdissection (LCM) followed by nanoscale liquid chromatography and high resolution MS. This pipeline routinely identifies on average ̃10,000 peptides corresponding to ̃1,800 proteins from sub-microgram amounts of protein extracted from ̃4,000 LCM breast cancer epithelial cells. Highly reproducible abundance data were generated from different technical and biological replicates. As a proof-of-principle, comparative proteome analysis was performed on estrogen receptor a positive or negative (ER+/-) samples, and commonly known differentially expressed proteins related to ER expression in breast cancer were identified. Therefore, we show that our tissue proteomics pipeline is robust and applicable for the identification of breast cancer specific protein markers. © The Author(s) 2012. Source

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