Abstract
BackgroundAlthough technological advances now allow increased tumor profiling, a detailed understanding of the mechanisms leading to the development of different cancers remains elusive. Our approach toward understanding the molecular events that lead to cancer is to characterize changes in transcriptional regulatory networks between normal and tumor tissue. Because enhancer activity is thought to be critical in regulating cell fate decisions, we have focused our studies on distal regulatory elements and transcription factors that bind to these elements.ResultsUsing DNA methylation data, we identified more than 25,000 enhancers that are differentially activated in breast, prostate, and kidney tumor tissues, as compared to normal tissues. We then developed an analytical approach called Tracing Enhancer Networks using Epigenetic Traits that correlates DNA methylation levels at enhancers with gene expression to identify more than 800,000 genome-wide links from enhancers to genes and from genes to enhancers. We found more than 1200 transcription factors to be involved in these tumor-specific enhancer networks. We further characterized several transcription factors linked to a large number of enhancers in each tumor type, including GATA3 in non-basal breast tumors, HOXC6 and DLX1 in prostate tumors, and ZNF395 in kidney tumors. We showed that HOXC6 and DLX1 are associated with different clusters of prostate tumor-specific enhancers and confer distinct transcriptomic changes upon knockdown in C42B prostate cancer cells. We also discovered de novo motifs enriched in enhancers linked to ZNF395 in kidney tumors.ConclusionsOur studies characterized tumor-specific enhancers and revealed key transcription factors involved in enhancer networks for specific tumor types and subgroups. Our findings, which include a large set of identified enhancers and transcription factors linked to those enhancers in breast, prostate, and kidney cancers, will facilitate understanding of enhancer networks and mechanisms leading to the development of these cancers.Electronic supplementary materialThe online version of this article (doi:10.1186/s13072-016-0102-4) contains supplementary material, which is available to authorized users.
Highlights
Technological advances allow increased tumor profiling, a detailed understanding of the mechanisms leading to the development of different cancers remains elusive
To identify activated and inactivated enhancers specific to breast, prostate, and kidney tumor tissue samples, we assembled a large set of genomic coordinates that includes regions previously identified as distal regulatory elements by Encyclopedia of DNA Elements (ENCODE) and Roadmap Epigenome Mapping Consortium (REMC) [6, 7] as well as enhancer locations derived from H3K27Ac Chromatin Immunoprecipitation (ChIP)-seq data generated in our laboratory for this study (e.g., H3K27Ac ChIP-seq for MCF7, MDAMB231, and MCF10A breast cells and for C42B and RWPE1 prostate cells)
We found that GATA3 ChIP-seq peaks from the MCF7 ER+ breast cancer cell line were statistically significantly enriched in the set of tumor-specific enhancers linked to GATA3 in breast invasive carcinoma (BRCA) by Tracing Enhancer Networks using Epigenetic Traits (TENET), as compared to all tumor-specific enhancers linked to genes or to all tumor-specific enhancers (Fisher exact test, adj. p value
Summary
Technological advances allow increased tumor profiling, a detailed understanding of the mechanisms leading to the development of different cancers remains elusive. Our approach toward understanding the molecular events that lead to cancer is to characterize changes in transcriptional regulatory networks between normal and tumor tissue. Recent studies from the Encyclopedia of DNA Elements (ENCODE) and the Roadmap Epigenome Mapping Consortium (REMC) have shown that more than ten thousand enhancers can be identified using epigenomic marks in a given cell line or tissue [6, 7]. It is not clear whether all of these enhancers are functional [8] or which gene is regulated by each enhancer. It is difficult to a priori develop a detailed transcriptional regulatory network for a given cell type [1, 9]
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