INFERRING REGULATORY ELEMENT LANDSCAPES AND TRANSCRIPTION FACTOR NETWORKS FROM CANCER METHYLOMES
Previous studies show that differences in methylation levels are specifically associated with cancer development. However, it remains an enigma for intergenic regions what the consequence of such methylation changes are in terms of gene expression. To address this we invented a method to associate methylation and gene expression across tumor data. We applied this to the cancer genome atlas (TCGA) data and show how the method can be used to identify 1) the specific gene under control of the methylated DNA and 2) its upstream regulators. We built a software package called ELMER and made it available through R/Bioconductor.
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.
RELEVANCE TO OC
The paper describes associations for ovarian cancer in addition to all other cancers and will be used for future and better datasets.