Inferring regulatory element landscapes and transcription factor networks from cancer methylomes

Lijing Yao; Hui Shen; Peter W. Laird; Peggy J. Farnham; Benjamin P. Berman

doi:10.1186/s13059-015-0668-3

Inferring regulatory element landscapes and transcription factor networks from cancer methylomes

Lijing Yao, Hui Shen, Peter W. Laird, Peggy J. Farnham^*, Benjamin P. Berman

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

129 Scopus citations

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.

Original language	American English
Article number	105
Journal	Genome Biology
Volume	16
Issue number	1
DOIs	https://doi.org/10.1186/s13059-015-0668-3
State	Published - 21 May 2015
Externally published	Yes

Bibliographical note

Funding Information:
We thank the ENCODE Project, the Roadmap Epigenome Mapping Consortium, and the FANTOM Consortium for the use of the genomic locations of enhancers [2, 9, 31, 32]. The results published here are largely based upon data generated by the TCGA Research Network (http://cancergenome.nih.gov/), and our use of these data is in accordance with the guidelines at: http://cancergenome.nih.gov/ publications/publicationguidelines. We thank the TCGA Consoritum members for use of these datasets. We thank Simon Coetzee and Toshinori Hinoue for suggestions and help with the ELMER BioConductor package. BPB and LY were supported in part by NCI grant 1U01CA184826, and BPB in part by 5R01HG006705. PJF and LY were supported in part by National Cancer Institute (NCI) grants 1U01ES017154, U54HG006996, and P30CA014089. HS and PWL were supported in part by the TCGA Consortium through NCI grant 1U24CA143882. High-performance computing support was provided by the USC High Performance Computing Center (HPCC).

Publisher Copyright:
© 2015 Yao et al.

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10.1186/s13059-015-0668-3

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title = "Inferring regulatory element landscapes and transcription factor networks from cancer methylomes",

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.",

author = "Lijing Yao and Hui Shen and Laird, {Peter W.} and Farnham, {Peggy J.} and Berman, {Benjamin P.}",

note = "Funding Information: We thank the ENCODE Project, the Roadmap Epigenome Mapping Consortium, and the FANTOM Consortium for the use of the genomic locations of enhancers [2, 9, 31, 32]. The results published here are largely based upon data generated by the TCGA Research Network (http://cancergenome.nih.gov/), and our use of these data is in accordance with the guidelines at: http://cancergenome.nih.gov/ publications/publicationguidelines. We thank the TCGA Consoritum members for use of these datasets. We thank Simon Coetzee and Toshinori Hinoue for suggestions and help with the ELMER BioConductor package. BPB and LY were supported in part by NCI grant 1U01CA184826, and BPB in part by 5R01HG006705. PJF and LY were supported in part by National Cancer Institute (NCI) grants 1U01ES017154, U54HG006996, and P30CA014089. HS and PWL were supported in part by the TCGA Consortium through NCI grant 1U24CA143882. High-performance computing support was provided by the USC High Performance Computing Center (HPCC). Publisher Copyright: {\textcopyright} 2015 Yao et al.",

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AU - Berman, Benjamin P.

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N2 - 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.

AB - 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.

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Inferring regulatory element landscapes and transcription factor networks from cancer methylomes

Abstract

Bibliographical note

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