Abstract Mutations in genes coding for transcription factors (TFs) are frequently observed in tumors, many of which lead to aberrant transcriptional activity. Unfortunately, transcription factors are often considered “undruggable” due to the absence of targetable enzymatic activity and the large surface contacting DNA. To address the transcription factor “druggability” problem, we developed a computational repositioning approach to identify small molecules that can perturb the activity of transcription factors. Our approach involves identifying drugs that mobilize many of the target genes of a transcription factor. This approach uses Gene Set Enrichment Analysis to integrate genomewide binding data (ChIP-seq) with drug perturbation differential gene expression profiles. When applied to ENCODE ChIP-seq and the Connectivity Map expression profiles, our approach predicted 38,000 disruptive drug-TF interactions (FWER<0.1). These predictions included the known inhibitory effect of a BRD4 inhibitor (JQ1, FWER=0.000) on MYC and of HSP90 inhibitors (e.g.17-AAG, FWER=0.031) on HSF1. We used an integrated biological network with 22k genes and 7k drugs to identify predicted disruptive drug-TF interactions. Based on this approach network path length of predicted drug-TF was significantly shorter than non-predictions (p=2.2e-16). Many predicted drug-TF interactions involved only one protein intermediary between the drug and the TF, indicating that our predictions are not random and suggesting that many drugs might disrupt TFs by targeting their regulatory or interacting co-factors. We then decided to apply our approach to identifying candidate molecules that can inhibit ERG, a pro-invasive, oncogenic TF over-expressed in as many as 50% of prostate cancer patients. Using ERG ChIP-seq peaks in prostate cells, dexamethasone (FWER=0.086) was predicted to inhibit ERG transcriptional activity. Using cell invasion and migration assays, we found that dexamethasone significantly decreased cell invasion and migration in DU145 prostate cancer cells over-expressing ERG, but not in isogenic control cells. Dexamethasone also abrogated expression of PLAU, a known ERG target, and substantially decreased binding of ERG at the PLAU promoter. Analysis of ERG ChIP-seq peaks revealed a highly enriched AP-1 DNA motif and preferential mobilization by dexamethasone of genes near peaks containing the AP-1 motif (p=0.06). ChIP-seq experiments showed that dexamethasone reduced AP-1 binding at ERG-JUN promoter sites. These results suggest that dexamethasone inhibits ERG by disruption of AP1, a key co-factor. Altogether, this method provides a novel, broadly applicable strategy to computationally identify drugs that indirectly target transcription factors. This may be of further interest for other factors with oncogenic activity, such as FOXA1 or for reactivating deactivated tumor suppressive transcription factors such as p53. Citation Format: Kaitlyn Gayvert, Cynthia Cheung, David Rickman, Olivier Elemento. Computational drug repositioning identifies dexamethasone as potential ERG inhibitor. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 362. doi:10.1158/1538-7445.AM2014-362
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Jul 1, 2015
Eytan Katzav + 6
Open Access
