» Articles » PMID: 29229604

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. 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. .

Citing Articles

Comparative evaluation of feature reduction methods for drug response prediction.

Firoozbakht F, Yousefi B, Tsoy O, Baumbach J, Schwikowski B Sci Rep. 2024; 14(1):30885.

PMID: 39730699 PMC: 11680677. DOI: 10.1038/s41598-024-81866-1.


Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning.

Cai Z, Apolinario S, Baiao A, Pacini C, Sousa M, Vinga S Nat Commun. 2024; 15(1):10390.

PMID: 39614072 PMC: 11607321. DOI: 10.1038/s41467-024-54771-4.


Human VCP mutant ALS/FTD microglia display immune and lysosomal phenotypes independently of GPNMB.

Clarke B, Ziff O, Tyzack G, Petric Howe M, Wang Y, Klein P Mol Neurodegener. 2024; 19(1):90.

PMID: 39593143 PMC: 11590569. DOI: 10.1186/s13024-024-00773-1.


On the identification of differentially-active transcription factors from ATAC-seq data.

Gerbaldo F, Sonder E, Fischer V, Frei S, Wang J, Gapp K PLoS Comput Biol. 2024; 20(10):e1011971.

PMID: 39441876 PMC: 11534267. DOI: 10.1371/journal.pcbi.1011971.


ResisenseNet hybrid neural network model for predicting drug sensitivity and repurposing in breast Cancer.

Karampuri A, Jakkula B, Perugu S Sci Rep. 2024; 14(1):23949.

PMID: 39397003 PMC: 11471817. DOI: 10.1038/s41598-024-71076-0.


References
1.
Green W, Slovak M, Chen I, Pallavicini M, Hecht J, Willman C . Lack of IRF-1 expression in acute promyelocytic leukemia and in a subset of acute myeloid leukemias with del(5)(q31). Leukemia. 1999; 13(12):1960-71. DOI: 10.1038/sj.leu.2401596. View

2.
Ohh M, Park C, Ivan M, Hoffman M, Kim T, Huang L . Ubiquitination of hypoxia-inducible factor requires direct binding to the beta-domain of the von Hippel-Lindau protein. Nat Cell Biol. 2000; 2(7):423-7. DOI: 10.1038/35017054. View

3.
Scott G, Chang C, Erny K, Xu F, Fredericks W, Rauscher 3rd F . Ets regulation of the erbB2 promoter. Oncogene. 2001; 19(55):6490-502. DOI: 10.1038/sj.onc.1204041. View

4.
Semenza G . Hypoxia-inducible factor 1: oxygen homeostasis and disease pathophysiology. Trends Mol Med. 2001; 7(8):345-50. DOI: 10.1016/s1471-4914(01)02090-1. View

5.
Tegner J, Yeung M, Hasty J, Collins J . Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc Natl Acad Sci U S A. 2003; 100(10):5944-9. PMC: 156306. DOI: 10.1073/pnas.0933416100. View