Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Expression data from ERBB2 over-expression and EGF stimulation in MCF10A cells

ABSTRACT: Expression data from ERBB2 over-expression and EGF stimulation in MCF10A cells The cells were transduced with retroviruses encoding vector control (pBabe) or pBabe-ErbB2. After infection cells were switched to assay medium supplemented with only 2% horse serum and no EGF. For EGF stimulation, cells transduced with empty vector were treated with EGF at 50 ng/ml for 2 hours before harvesting.

ORGANISM(S): Homo sapiens

SUBMITTER: Ben Wittner

PROVIDER: E-GEOD-14987 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

MYC regulation of a "poor-prognosis" metastatic cancer cell state.

Wolfer Anita A Wittner Ben S BS Irimia Daniel D Flavin Richard J RJ Lupien Mathieu M Gunawardane Ruwanthi N RN Meyer Clifford A CA Lightcap Eric S ES Tamayo Pablo P Mesirov Jill P JP Liu X Shirley XS Shioda Toshi T Toner Mehmet M Loda Massimo M Brown Myles M Brugge Joan S JS Ramaswamy Sridhar S

Proceedings of the National Academy of Sciences of the United States of America 20100204 8

Gene expression signatures are used in the clinic as prognostic tools to determine the risk of individual patients with localized breast tumors developing distant metastasis. We lack a clear understanding, however, of whether these correlative biomarkers link to a common biological network that regulates metastasis. We find that the c-MYC oncoprotein coordinately regulates the expression of 13 different "poor-outcome" cancer signatures. In addition, functional inactivation of MYC in human breast ...[more]

PMID: 20133671

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Project description:Epidemiological studies have revealed concurrence of specific cancers with other disease states such as metabolic syndrome, inflammatory disease and autoimmune disease. Patients with these chronic conditions have a higher incidence of various cancers, more aggressive tumors, and a higher mortality rate. It has been proposed that obesity, inflammation and chronic disease should be correlated with cancer at the molecular level, but common gene signatures or networks have yet to be described. Here, we identify genes regulated during the process of cellular transformation in both a breast epithelial cell line and a set of isogenic fibroblastic cell lines. The resulting gene signature strongly mimics those of many different cancer types, thereby validating these experimental systems as models of oncogenesis. Furthermore, it uncovers transcriptional factors and common gene regulatory networks linking cancer with other disease states such as atheroscelorsis, type II diabetes, obesity, lupus, and cardiomyopathy. We suggest that this common transcriptional program can contribute to the concurrence of different diseases, and that the interplay between this program and cell-type specific factors can give rise to a variety of human diseases. Two isogenic cell lines were used to model transformation. The first employed an inducible oncogene: src fused to the ligand binding domain of the estrogen receptor. ER-src and pBABE control cells were dosed with Tamoxifen or Etoh control across several time points to monitor gene expression during transformation. The other model consists of three isogenic cell lines derived from primary fibroblasts in a serial manner. The first is immortalized by overexpression of telomerase (hTERT), and exhibits normal fibroblast morphology. The second expresses hTERT along with both large and small T antigens of Simian virus 40, and it displays an altered morphology but is not transformed. The third cell line expresses hTERT, T antigens, and an oncogenic derivative of Ras (H-RasV12); it is morphologically transformed and has tumorigenic potential in soft agar and nude mice. Thus, three stages of transformation are represented by these cells.

Dataset Information

Expression data from ERBB2 over-expression and EGF stimulation in MCF10A cells

Publications

MYC regulation of a "poor-prognosis" metastatic cancer cell state.

Similar Datasets

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