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Using Gene Expression Biomarkers to Identify Chemicals that Induce Key Events in Cancer and Endocrine Disruption AOPs: Androgen Receptor as an Example
Citation:
Corton, C., N. Kleinstreuer, AND J. Rooney. Using Gene Expression Biomarkers to Identify Chemicals that Induce Key Events in Cancer and Endocrine Disruption AOPs: Androgen Receptor as an Example. SETAC, Durham, NC, April 17 - 19, 2018.
Impact/Purpose:
High-throughput transcriptomic (HTTr) technologies are increasingly being used to screen environmental chemicals in vitro to provide mechanistic context for regulatory testing. The development of gene expression biomarkers that accurately predict molecular and toxicological effects including cancer and endocrine disruption is needed to accurately interpret HTTr data streams. We have developed strategies to build and test gene expression biomarkers that can assess the modulation of key events in AOPs. Biomarkers were built using microarray profiles from human cells in which the key event (e.g., androgen receptor; AR) was either activated or suppressed by chemical modulators followed by a genetic filter that included only those genes that were also regulated when the factor was overexpressed or knocked down. Using this strategy, we built biomarkers that assess the activity in chemically-treated cells of ~40 transcription factors, signaling pathways, or phenotypes and examine the effects of ~2500 chemicals across multiple cell lines. As one example, AR biomarker genes were identified by their consistent expression after exposure to 4 AR agonists and opposite expression after exposure to 4 AR antagonists. A genetic filter was used to include only those genes that were regulated by modulation of the AR protein. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm which compares the expression of AR biomarker genes under various treatment conditions. Using 163 comparisons from cells treated with 98 chemicals, the biomarker gave balanced accuracies for prediction of AR activation or AR suppression of 97% or 98%, respectively. The biomarker was able to correctly classify 16 out of 17 AR reference antagonists including those that are “weak” and “very weak”. Predictions from AR-positive LAPC-4 cells treated with 28 chemicals in antagonist mode were compared to those from the ToxCast AR pathway model based on 11 in vitro high-throughput screening assays that queried different steps in AR signaling. The balanced accuracy was 93% for suppression. These results demonstrate that the AR gene expression biomarker could be a useful tool in HTTr to identify AR modulators in large collections of microarray data derived from AR-positive prostate cancer cell lines.
Description:
High-throughput transcriptomic (HTTr) technologies are increasingly being used to screen environmental chemicals in vitro to provide mechanistic context for regulatory testing. The development of gene expression biomarkers that accurately predict molecular and toxicological effects including cancer and endocrine disruption is needed to accurately interpret HTTr data streams. We have developed strategies to build and test gene expression biomarkers that can assess the modulation of key events in AOPs. Biomarkers were built using microarray profiles from human cells in which the key event (e.g., androgen receptor; AR) was either activated or suppressed by chemical modulators followed by a genetic filter that included only those genes that were also regulated when the factor was overexpressed or knocked down. Using this strategy, we built biomarkers that assess the activity in chemically-treated cells of ~40 transcription factors, signaling pathways, or phenotypes and examine the effects of ~2500 chemicals across multiple cell lines. As one example, AR biomarker genes were identified by their consistent expression after exposure to 4 AR agonists and opposite expression after exposure to 4 AR antagonists. A genetic filter was used to include only those genes that were regulated by modulation of the AR protein. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm which compares the expression of AR biomarker genes under various treatment conditions. Using 163 comparisons from cells treated with 98 chemicals, the biomarker gave balanced accuracies for prediction of AR activation or AR suppression of 97% or 98%, respectively. The biomarker was able to correctly classify 16 out of 17 AR reference antagonists including those that are “weak” and “very weak”. Predictions from AR-positive LAPC-4 cells treated with 28 chemicals in antagonist mode were compared to those from the ToxCast AR pathway model based on 11 in vitro high-throughput screening assays that queried different steps in AR signaling. The balanced accuracy was 93% for suppression. These results demonstrate that the AR gene expression biomarker could be a useful tool in HTTr to identify AR modulators in large collections of microarray data derived from AR-positive prostate cancer cell lines.