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Genome Biol. 2015 May 21;16:105. doi: 10.1186/s13059-015-0668-3.

Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.

Author information

1
Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, 1450 Biggy Street, NRT 6503, Los Angeles, CA, 90089-9601, USA. lijingya@usc.edu.
2
Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI, 49503, USA. Hui.Shen@vai.org.
3
Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI, 49503, USA. Peter.Laird@vai.org.
4
Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, 1450 Biggy Street, NRT 6503, Los Angeles, CA, 90089-9601, USA. peggy.farnham@med.usc.edu.
5
Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, 1450 Biggy Street, NRT 6503, Los Angeles, CA, 90089-9601, USA. benjamin.berman@cshs.org.
6
Bioinformatics and Computational Biology Research Center, Department of Biomedical Sciences, Cedars-Sinai Medical Center, AHSP Bldg., Suite A8111, Los Angeles, CA, 90048, USA. benjamin.berman@cshs.org.

Abstract

Recent studies indicate that DNA methylation can be used to identify transcriptional enhancers, but no systematic approach has been developed for genome-wide identification and analysis of enhancers based on DNA methylation. We describe ELMER (Enhancer Linking by Methylation/Expression Relationships), an R-based tool that uses DNA methylation to identify enhancers and correlates enhancer state with expression of nearby genes to identify transcriptional targets. Transcription factor motif analysis of enhancers is coupled with expression analysis of transcription factors to infer upstream regulators. Using ELMER, we investigated more than 2,000 tumor samples from The Cancer Genome Atlas. We identified networks regulated by known cancer drivers such as GATA3 and FOXA1 (breast cancer), SOX17 and FOXA2 (endometrial cancer), and NFE2L2, SOX2, and TP63 (squamous cell lung cancer). We also identified novel networks with prognostic associations, including RUNX1 in kidney cancer. We propose ELMER as a powerful new paradigm for understanding the cis-regulatory interface between cancer-associated transcription factors and their functional target genes.

PMID:
25994056
PMCID:
PMC4460959
DOI:
10.1186/s13059-015-0668-3
[Indexed for MEDLINE]
Free PMC Article

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