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Moving Toward Integrating Gene Expression Profiling into High-throughput Testing:A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium
Citation:
Vanduyn, N., B. Chorley, R. Tice, R. Judson, AND C. Corton. Moving Toward Integrating Gene Expression Profiling into High-throughput Testing:A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 151(1):88-103, (2016).
Impact/Purpose:
This manuscript is significant for a number of reasons. First, we developed computational methods for interpreting the activity of specific molecular targets in gene expression profiles. These procedures are generally applicable to other targets of interest to the EPA. Second, we evaluated the ability of these procedures to predict estrogen receptor alpha (ERalpha) modulation after exposure to chemicals. We found that the methods were very accurate in identifying in one assay both ERalpha activators and suppressors and can detect weak and very weak activators. Lastly, the methods were excellent at replicating the results of the 18 ToxCast/Tox21 assays that are currently used to evaluate ER activity. Our system of screening for ERalpha activity in MCF-7 cell lines could be used as a potential Tier 0 screen to prioritize chemicals for more targeted assays to determine mechanism of action. It should be noted that all of the analysis in this manuscript did not require any additional experimentation, only re-evaluation of a large database of microarray data that is found in a commercially available database.
Description:
Microarray profiling of chemical-induced effects is being increasingly used in medium and high-throughput formats. In this study, we describe computational methods 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 ChIP-Seq 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 data sets 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. Finally, the biomarker was found to be specific for screening in ERα-positive but not ERα-negative human breast cancer cell lines. 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.
