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Proteomic characterization of Her2/neu‐overexpressing breast cancer cells
Author(s) -
Chen Hexin,
Pimienta Genaro,
Gu Yiben,
Sun Xu,
Hu Jianjun,
Kim MinSik,
Chaerkady Raghothama,
Gucek Marjan,
Cole Robert N.,
Sukumar Saraswati,
Pandey Akhilesh
Publication year - 2010
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201000297
Subject(s) - cancer research , biology , breast cancer , oncogene , her2/neu , cancer , phenotype , proteomics , metastasis , receptor tyrosine kinase , microbiology and biotechnology , gene , kinase , cell cycle , genetics
The receptor tyrosine kinase HER2 is an oncogene amplified in invasive breast cancer and its overexpression in mammary epithelial cell lines is a strong determinant of a tumorigenic phenotype. Accordingly, HER2 ‐ overexpressing mammary tumors are commonly indicative of a poor prognosis in patients. Several quantitative proteomic studies have employed two‐dimensional gel electrophoresis in combination with MS/MS, which provides only limited information about the molecular mechanisms underlying HER2/neu signaling. In the present study, we used a SILAC‐based approach to compare the proteomic profile of normal breast epithelial cells with that of Her2/neu‐overexpressing mammary epithelial cells, isolated from primary mammary tumors arising in mouse mammary tumor virus‐Her2/neu transgenic mice. We identified 23 proteins with relevant annotated functions in breast cancer, showing a substantial differential expression. This included overexpression of creatine kinase, retinol‐binding protein 1, thymosin 4 and tumor protein D52, which correlated with the tumorigenic phenotype of Her2‐overexpressing cells. The differential expression pattern of two genes, gelsolin and retinol binding protein 1, was further validated in normal and tumor tissues. Finally, an in silico analysis of published cancer microarray data sets revealed a 23‐gene signature, which can be used to predict the probability of metastasis‐free survival in breast cancer patients.