Pan-cancer patterns of DNA methylationWitte, Tania; Plass, Christoph; Gerhauser, Clarissa
doi: 10.1186/s13073-014-0066-6pmid: 25473433
The comparison of DNA methylation patterns across cancer types (pan-cancer methylome analyses) has revealed distinct subgroups of tumors that share similar methylation patterns. Integration of these data with the wealth of information derived from cancer genome profiling studies performed by large international consortia has provided novel insights into the cellular aberrations that contribute to cancer development. There is evidence that genetic mutations in epigenetic regulators (such as DNMT3, IDH1/2 or H3.3) mediate or contribute to these patterns, although a unifying molecular mechanism underlying the global alterations of DNA methylation has largely been elusive. Knowledge gained from pan-cancer methylome analyses will aid the development of diagnostic and prognostic biomarkers, improve patient stratification and the discovery of novel druggable targets for therapy, and will generate hypotheses for innovative clinical trial designs based on methylation subgroups rather than on cancer subtypes. In this review, we discuss recent advances in the global profiling of tumor genomes for aberrant DNA methylation and the integration of these data with cancer genome profiling data, highlight potential mechanisms leading to different methylation subgroups, and show how this information can be used in basic research and for translational applications. A remaining challenge is to experimentally prove the functional link between observed pan-cancer methylation patterns, the associated genetic aberrations, and their relevance for the development of cancer.
The `dnet’ approach promotes emerging research on cancer patient survivalFang, Hai; Gough, Julian
doi: 10.1186/s13073-014-0064-8pmid: 25246945
We present the `dnet’ package and apply it to the `TCGA’ mutation and clinical data of >3,000 patients. We uncover the existence of an underlying gene network that at least partially controls cancer `survivalness’, with mutations that are significantly correlated with patient survival, yet independent of tumour origin and type. The survivalness network has natural community structure corresponding to tumour hallmarks, and contains genes that are potentially druggable in the clinic. This network has evolutionary roots in Deuterostomia identifying PTK2 and VAV1 as under-valued relative to more studied genes from that era. The `dnet’ R package is available at
http://cran.r-project.org/package=dnet
.