Abstract B2-09: A systems biology approach to understanding estrogen responsiveness in breast cancer cells using the MCF7 model
Authors: Shajahan-Haq Ayesha N. , Li Lang , Liu Yunlong , Jin Lu , Miller David F. , Pilrose Jay , Cheema Amrita K. , Boca Simina M. , Bhuvaneshwar Krithika , Madhavan Subha , Clarke Robert , Nephew Kenneth P. 
Published: May 2016

Estrogen receptor alpha (ER) is expressed in 70% of breast cancers and is critical for breast cancer cell proliferation, differentiation, and apoptosis. ER functions primarily as a nuclear transcription factor that dimerizes upon binding of the natural ligand, 17beta estradiol (E2). ER is a potent regulator of gene expression, altering the transcriptome, and ultimately both the proteome and metabolome. In the clinic, ER+ tumors are often treated with antiestrogens or aromatase inhibitors. However, resistance to these endocrine therapies is common, and there is an urgent need to understand how estrogen signaling contributes to the malignancy of breast cancer cells. The MCF7 ER+ breast cancer cell line is a commonly used cell model to study estrogen responsiveness in ER+ breast cancers. However, the field lacks a clear and detailed description of all the changes that occur in response to E2 by time and dose in MCF7 cells. Additionally, there is a great need to obtain a consistent view of the estrogen-regulated gene set, resolve the disconnections among epigenetics regulation of estrogen signaling and protein phosphoproteomics, and provide reliable metabolomics data sets for MCF7 cells. In this multi-institutional project, we have generated multi-omics data and cell cycle profile for MCF7 cells treated with 1 pM and 1 nM of E2 for 26-time points, 0 to 72 h. MCF7 cell line was obtained from American Type Culture Collection (ATCC), fingerprinted and confirmed for E2 and Tamoxifen sensitivity prior to omics studies. Moreover, ER expression (basal and with E2 stimulation) was determined using Western blotting and qPCR. Omics data generated include: RNA-seq (coding and non-coding), reverse phase protein array (RPPA; proteins and phospho-proteins), methylome analysis, cell cycle analysis and metabolomics (metabolites). Using our data sets, comprehensive responses to physiologically relevant E2 concentrations, including both rapid and long-term E2 responses, can be determined. Our data were processed using current best practices and the time trends for the 1 pM and 1 nM doses of E2 were assessed via linear models separately for the various omics features. Global differences between samples were further assessed via principal components analysis. The features which showed the most significant differences in time trends between the two dose groups were visualized as heatmaps. The objective of this study is to provide our data and discoveries as a common resource for the systems biology centers and the broader research community. A multi-dimensional dataset such as our will enable the development of dynamic network model(s) to interrogate estrogen signaling, from nucleic acids to proteins, with changes in the MCF7 metabolome.

Citation Format: Ayesha N. Shajahan-Haq, Lang Li, Yunlong Liu, Lu Jin, David F. Miller, Jay Pilrose, Amrita K. Cheema, Simina M. Boca, Krithika Bhuvaneshwar, Subha Madhavan, Robert Clarke, Kenneth P. Nephew. A systems biology approach to understanding estrogen responsiveness in breast cancer cells using the MCF7 model. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-09.