title: Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium.
abstract: Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including "very weak" agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells.
authors: *Oak Ridge Institute for Science and Education (ORISE) Integrated Systems Toxicology Division, US-EPA.
authors: Natalia
authors: N
authors: Ryan
authors: Integrated Systems Toxicology Division, US-EPA.
authors: Brian
authors: B
authors: Chorley
authors: Division of the National Toxicology Program, National Institute of Environmental Health Sciences.
authors: Raymond R
authors: RR
authors: Tice
authors: National Center for Computational Toxicology, US-EPA, Research Triangle Park, North Carolina 27711.
authors: Richard
authors: R
authors: Judson
authors: Integrated Systems Toxicology Division, US-EPA corton.chris@epa.gov.
authors: J Christopher
authors: JC
authors: Corton
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D019295:Computational Biology; D030541:Databases, Genetic; D052244:Endocrine Disruptors / Q000633:toxicity*; D004965:Estrogen Antagonists / Q000633:toxicity; D047628:Estrogen Receptor alpha / Q000235:genetics* / Q000378:metabolism; D004967:Estrogens / Q000633:toxicity; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic / Q000187:drug effects; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D061986:MCF-7 Cells; D020411:Oligonucleotide Array Sequence Analysis; D034622:RNA Interference; D014162:Transfection
chemical_list: C506487:ESR1 protein, human; D052244:Endocrine Disruptors; D004965:Estrogen Antagonists; D047628:Estrogen Receptor alpha; D004967:Estrogens
keywords: MCF-7 cell line; biomarker.; estrogen receptor; gene expression profiling