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Cell Rep. 2016 Jun 14;15(11):2348-56. doi: 10.1016/j.celrep.2016.05.037. Epub 2016 Jun 2.

A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors.

Author information

1
Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10021, USA; Institute for Precision Medicine, Weill Cornell Medical College, New York, NY 10021, USA; Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA.
2
Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, NY 10021, USA.
3
Department of Biomedical Informatics, Columbia University, New York, NY 10027, USA.
4
Department of Biomedical Informatics, Columbia University, New York, NY 10027, USA; Department of Physiology and Cellular Biophysics, Columbia University, New York, NY 10032, USA.
5
Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
6
Institute for Precision Medicine, Weill Cornell Medical College, New York, NY 10021, USA; Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, NY 10021, USA.
7
Institute for Precision Medicine, Weill Cornell Medical College, New York, NY 10021, USA; Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, NY 10021, USA. Electronic address: dsr2005@med.cornell.edu.
8
Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10021, USA; Institute for Precision Medicine, Weill Cornell Medical College, New York, NY 10021, USA. Electronic address: ole2001@med.cornell.edu.

Abstract

Mutations in transcription factor (TF) genes are frequently observed in tumors, often leading to aberrant transcriptional activity. Unfortunately, TFs are often considered undruggable due to the absence of targetable enzymatic activity. To address this problem, we developed CRAFTT, a computational drug-repositioning approach for targeting TF activity. CRAFTT combines ChIP-seq with drug-induced expression profiling to identify small molecules that can specifically perturb TF activity. Application to ENCODE ChIP-seq datasets revealed known drug-TF interactions, and a global drug-protein network analysis supported these predictions. Application of CRAFTT to ERG, a pro-invasive, frequently overexpressed oncogenic TF, predicted that dexamethasone would inhibit ERG activity. Dexamethasone significantly decreased cell invasion and migration in an ERG-dependent manner. Furthermore, analysis of electronic medical record data indicates a protective role for dexamethasone against prostate cancer. Altogether, our method provides a broadly applicable strategy for identifying drugs that specifically modulate TF activity.

PMID:
27264179
PMCID:
PMC4912004
DOI:
10.1016/j.celrep.2016.05.037
[Indexed for MEDLINE]
Free PMC Article

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