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Liu NQ, Braakman RBH, Stingl C, Luider TM, Martens JWM, Foekens JA, Umar A. Proteomics pipeline for biomarker discovery of laser capture microdissected breast cancer tissue. J Mammary Gland Biol Neoplasia 2012; 17:155-64. [PMID: 22644111 PMCID: PMC3428526 DOI: 10.1007/s10911-012-9252-6] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2012] [Accepted: 05/01/2012] [Indexed: 01/15/2023] Open
Abstract
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 α 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.
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Affiliation(s)
- Ning Qing Liu
- Department of Medical Oncology and Daniel Den Hoed Cancer Center, Erasmus University Medical Center, Dr. Molewaterplein 50, Be-401, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands
- Netherlands Proteomics Center, Rotterdam, the Netherlands
| | - René B. H. Braakman
- Department of Medical Oncology and Daniel Den Hoed Cancer Center, Erasmus University Medical Center, Dr. Molewaterplein 50, Be-401, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands
- Center for Translational Molecular Medicine, Rotterdam, the Netherlands
| | - Christoph Stingl
- Department of Neurology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Theo M. Luider
- Department of Neurology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - John W. M. Martens
- Department of Medical Oncology and Daniel Den Hoed Cancer Center, Erasmus University Medical Center, Dr. Molewaterplein 50, Be-401, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands
- Center for Translational Molecular Medicine, Rotterdam, the Netherlands
- Cancer Genomics Centre, Rotterdam, the Netherlands
| | - John A. Foekens
- Department of Medical Oncology and Daniel Den Hoed Cancer Center, Erasmus University Medical Center, Dr. Molewaterplein 50, Be-401, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands
- Netherlands Proteomics Center, Rotterdam, the Netherlands
- Center for Translational Molecular Medicine, Rotterdam, the Netherlands
- Cancer Genomics Centre, Rotterdam, the Netherlands
| | - Arzu Umar
- Department of Medical Oncology and Daniel Den Hoed Cancer Center, Erasmus University Medical Center, Dr. Molewaterplein 50, Be-401, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands
- Netherlands Proteomics Center, Rotterdam, the Netherlands
- Center for Translational Molecular Medicine, Rotterdam, the Netherlands
- Cancer Genomics Centre, Rotterdam, the Netherlands
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