title: An integrative pan-cancer-wide analysis of epigenetic enzymes reveals universal patterns of epigenomic deregulation in cancer.
abstract: BACKGROUND
  One of the most important recent findings in cancer genomics is the identification of novel driver mutations which often target genes that regulate genome-wide chromatin and DNA methylation marks. Little is known, however, as to whether these genes exhibit patterns of epigenomic deregulation that transcend cancer types.
  
  
  RESULTS
  Here we conduct an integrative pan-cancer-wide analysis of matched RNA-Seq and DNA methylation data across ten different cancer types. We identify seven tumor suppressor and eleven oncogenic epigenetic enzymes which display patterns of deregulation and association with genome-wide cancer DNA methylation patterns, which are largely independent of cancer type. In doing so, we provide evidence that genome-wide cancer hyper- and hypo- DNA methylation patterns are independent processes, controlled by distinct sets of epigenetic enzyme genes. Using causal network modeling, we predict a number of candidate drivers of cancer DNA hypermethylation and hypomethylation. Finally, we show that the genomic loci whose DNA methylation levels associate most strongly with expression of these putative drivers are highly consistent across cancer types.
  
  
  CONCLUSIONS
  This study demonstrates that there exist universal patterns of epigenomic deregulation that transcend cancer types, and that intra-tumor levels of genome-wide DNA hypomethylation and hypermethylation are controlled by distinct processes.
authors.affiliation: Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, 320 Yue Yang Road, Shanghai, 200031, China.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Yang
authors.affiliation: Department of Women's Cancer, University College London, 74 Huntley Street, London, WC1E 6AU, UK.
authors.forename: Allison
authors.initials: A
authors.lastname: Jones
authors.affiliation: Department of Women's Cancer, University College London, 74 Huntley Street, London, WC1E 6AU, UK.
authors.forename: Martin
authors.initials: M
authors.lastname: Widschwendter
authors.affiliation: Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, 320 Yue Yang Road, Shanghai, 200031, China. a.teschendorff@ucl.ac.uk.|Statistical Cancer Genomics, Paul O'Gorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6BT, UK. a.teschendorff@ucl.ac.uk.
authors.forename: Andrew E
authors.initials: AE
authors.lastname: Teschendorff
mesh_terms: D019175:DNA Methylation; D044127:Epigenesis, Genetic; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D015894:Genome, Human; D006801:Humans; D009369:Neoplasms / Q000201:enzymology / Q000235:genetics*