Transcription Factor Activities Enhance Markers of Drug Sensitivity in Cancer

Abstract

Transcriptional dysregulation induced by aberrant transcription factors (TF) is a key feature of cancer, but its global influence on drug sensitivity has not been examined. Here, we infer the transcriptional activity of 127 TFs through analysis of RNA-seq gene expression data newly generated for 448 cancer cell lines, combined with publicly available datasets to survey a total of 1,056 cancer cell lines and 9,250 primary tumors. Predicted TF activities are supported by their agreement with independent shRNA essentiality profiles and homozygous gene deletions, and recapitulate mutant-specific mechanisms of transcriptional dysregulation in cancer. By analyzing cell line responses to 265 compounds, we uncovered numerous TFs whose activity interacts with anticancer drugs. Importantly, combining existing pharmacogenomic markers with TF activities often improves the stratification of cell lines in response to drug treatment. Our results, which can be queried freely at dorothea.opentargets.io, offer a broad foundation for discovering opportunities to refine personalized cancer therapies.Significance: Systematic analysis of transcriptional dysregulation in cancer cell lines and patient tumor specimens offers a publicly searchable foundation to discover new opportunities to refine personalized cancer therapies. Cancer Res; 78(3); 769-80.Ó2017 AACR.

Patient‐specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

“…We then investigated if these differences in dynamic behavior could be derived from their genetic makeup (Garcia‐Alonso et al , ). From the proteins in our model, only KRAS is functionally mutated in AsPC1 and TP53 in BxPC3.…”

“…These differences in the model parameters cause changes in the dynamic behavior of the system ( Fig 2B) and are behind the differential activation of Cas3 in response to drugs. We then investigated if these differences in dynamic behavior could be derived from their genetic makeup (Garcia-Alonso et al, 2018). From the proteins in our model, only KRAS is functionally mutated in AsPC1 and TP53 in BxPC3.…”

A functional landscape of chronic kidney disease entities from public transcriptomic data

“…First, we assessed the activity of transcription factors (TFs; Figure 3), based the levels of expression of their known putative targets (see Methods). Changes in putative target genes provided superior estimates of the TF activity than the expression level of the transcription factor itself 18,19 (Figure 3). We found 10 TFs differentially regulated in at least one CKD entity ( Figure 3).…”

“…We estimated transcription factor activities in the glomerular CKD entities using DoRothEA 18 which is a pipeline that tries to estimate transcription factor activity via the expression level of its target genes utilizing a curated database of transcription factor -target gene interactions (TF Regulon). For more information see Supplementary Methods.…”

Applications of ENCODE data to systematic analyses via data integration

“…In one study, ENCODE ChIP-seq data were integrated with RNA-seq data to identify drugs that might alter TF activity in specific diseases [**68]. In another study, Chen et al showed that they could use ENCODE data to identify genome-wide signatures of TF activity and proposed that these signatures could be utilized to identify new candidate drugs [69].…”