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A Alterations in transcription factors are frequently observed in tumors, leading to aberrant activity. Our method integrates transcriptional binding data and drug-induced gene expression profiles to make predictions about drugs that may affect transcriptional activity. This disruption can occur through a variety of mechanisms, including the inhibition or reactivation of direct binding to DNA or disruption via cofactors.
B Application of our method to JQ1 expression profiles and MYC ChIP-seq. The (left) panel illustrates the results for the GSEA involving JQ1 and MYC. The lowest plot in the left panel shows the log2 differential expression profile for JQ1, with the locations of the MYC target genes marked directly above. Directly above that are the running enrichment score and a histogram of the MYC target gene frequency across the drug-induced ranked list, which illustrate whether the MYC target gene set is enriched in the under- or over-expression regions. In the (middle) panel, the shortest path between JQ1 and MYC is shown, with BET Bromodomain proteins lying between the two. On the (right), we illustrate that the application of JQ1 results in the downregulation of MYC target genes.

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A Heatmap of the FWER p-values for all TF-drug pairs involving 168 TFs from ENCODE and the 1309 drugs from CMap. In the middle panels, we highlight a subset of non-predictions with high GSEA FWER scores (top) and predictions with low GSEA FWER scores (bottom). On the right, we illustrate that we would expect the candidate TF-drug pairs to have shorter network path lengths than non-predictions. For example, the non-predicted pair ETS1-betazole (p=1, GSEA nominal p-value) has a path length of 4 while the predicted pair FOXA2-prochlorperazine (p<0.001, GSEA nominal p-value) has a path length of 2.
B Normalized network path lengths for the specific predictions (FWER<0.1) and non-predictions (FWER=1). Statistical significance was evaluated using the Mann-Whitney Test.
C Network visualization of HSF1, all three HSP90 inhibitors covered in CMap and our network (monorden, 17AAG, 17DMAG), and four other drugs not predicted to disrupt HSF1 disruption (clomifen, yohimbine, oxprenolol, cortisone

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A Network visualization illustrating path lengths from ERG to five candidate drugs forERG inhibition (dexamethasone, naproxen, acemetacin, ondansetron, epitiostanol). The node sizes correspond to the gene expression levels, with the larger size representing a higher expression level. If low expression genes are removed (RPKM<4), the path lengths for naproxen and acemetacin are increased while the paths from ondansetron and epitiostanol are completely disrupted. The corresponding table shows metrics that describe each of these drugs in relation to ERG: NES is the Normalized Enrichment Score obtained from GSEA, PL is the shortest network path length required to connect ERG to the drug, MI is the modulation index and MI * is modulation index respectively after low expression genes were removed (RPKM<4).
B Application of our method to dexamethasone expression profiles and ERG target genes. The (left) panel illustrates the results of the GSEA for ERG and dexamethasone. The lowest plot of the left panel shows the log2 differential expression profile for dexamethasone, with the ERG target genes marked directly above. Above are the running enrichment score and a histogram of the ERG target gene frequency, which illustrates whether the gene set is enriched in the under- or over-expression regions. The (middle) panel shows a subnetwork including all genes that were members of any shortest path between ERG and dexamethasone. The (right) panel illustrates our prediction that the application of dexamethasone would result in the downregulation of activity of ERG target genes.
C ERG target gene PLAU expression by RT-PCR in cell lines expressing ERG(DU145-ERG, VCaP) and controls (DU145-GFP) after treatment with vehicle or dexamethasone. Data are shown as mean ± SEM. Asterisks indicate statistically significant differences by paired t test and n = 3 for each condition (*p < 0.05,**p < 0.01, ***p < 0.001, ns - not significant).
D Cell invasion and migration in cell lines expressing ERG (DU145-ERG) and controls (DU145-GFP). The data are shown as mean ± SEM and at n=4 representation 10x field of view. Asterisks indicate statistically significant differences by paired t test and n = 3 for each condition (*p < 0.05, **p < 0.01, ***p < 0.001, ns - not significant).
E The binding of ERG and a control (IgG) by ChIP-PCR at the promoter of its target gene PLAU and at a negative control (ARHGEF) in cell lines expressing ERG (DU145-ERG, VCaP). Data are shown as mean ± SEM. Asterisks indicate statistically significant differences by paired t test and n = 3 for each condition (*p < 0.05, **p < 0.01, ***p < 0.001, ns - not significant).

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A Kaplan-Meier survival analysis for time from first-prescription of drug to prostate cancer diagnosis (censor point) using an age-adjusted cohort of male patients was performed for patients treated with dexamethasone, prednisone, simvastatin and top-100 prescribed drugs. Statistical significance was assessed using cox proportional hazards test for the comparison of dexamethasone to each other drug.
B The drug concentration required to inhibit 50% growth (GI₅₀) in mutant p53 and wild-type p53 cell lines in the NCI DTP. Statistical significance was assessed using the Mann-Whitney test.