A quantitative proteomics analysis of MCF7 breast cancer stem and progenitor cell populations |
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| Authors: | Song Nie Sean P McDermott Yadwinder Deol Zhijing Tan Max S Wicha David M Lubman |
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| Affiliation: | 1. Department of Surgery, University of Michigan, Ann Arbor, MI, USA;2. Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan, Ann Arbor, MI, USA;3. Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI, USA |
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| Abstract: | Accumulating evidence has demonstrated that breast cancers are initiated and develop from a small population of stem‐like cells termed cancer stem cells (CSCs). These cells are hypothesized to mediate tumor metastasis and contribute to therapeutic resistance. However, the molecular regulatory networks responsible for maintaining CSCs in an undifferentiated state have yet to be elucidated. In this study, we used CSC markers to isolate pure breast CSCs fractions (ALDH+ and CD44+CD24‐ cell populations) and the mature luminal cells (CD49f‐EpCAM+) from the MCF7 cell line. Proteomic analysis was performed on these samples and a total of 3304 proteins were identified. A label‐free quantitative method was applied to analyze differentially expressed proteins. Using the criteria of greater than twofold changes and p value <0.05, 305, 322 and 98 proteins were identified as significantly different in three pairwise comparisons of ALDH+ versus CD44+CD24‐, ALDH+ versus CD49f‐EpCAM+ and CD44+CD24‐ versus CD49f‐EpCAM+, respectively. Pathway analysis of differentially expressed proteins by Ingenuity Pathway Analysis (IPA) revealed potential molecular regulatory networks that may regulate CSCs. Selected differential proteins were validated by Western blot assay and immunohistochemical staining. The use of proteomics analysis may increase our understanding of the underlying molecular mechanisms of breast CSCs. This may be of importance in the future development of anti‐CSC therapeutics. |
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| Keywords: | Breast cancer Cancer stem cell Quantitative proteomics LC‐MS/MS Pathway analysis Systems biology |
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