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.

Aberrant DNA methylation contributes to the malignant phenotype in virtually all types of cancer, including myeloid leukemia. We hypothesized that CpG island hypermethylation also occurs in juvenile myelomonocytic leukemia (JMML) and investigated whether it is associated with clinical, hematologic, or prognostic features. Based on quantitative measurements of DNA methylation in 127 JMML cases using mass spectrometry (MassARRAY), we identified 4 gene CpG islands with frequent hypermethylation: BMP4 (36% of patients), CALCA (54%), CDKN2B (22%), and RARB (13%). Hypermethylation was significantly associated with poor prognosis: when the methylation data were transformed into prognostic scores using a LASSO Cox regression model, the 5-year overall survival was 0.41 for patients in the top tertile of scores versus 0.72 in the lowest score tertile (P = .002). Among patients given allogeneic hematopoietic stem cell transplantation, the 5-year cumulative incidence of relapse was 0.52 in the highest versus 0.10 in the lowest score tertile (P = .007). In multivariate models, DNA methylation retained prognostic value independently of other clinical risk factors. Longitudinal analyses indicated that some cases acquired a more extensively methylated phenotype at relapse. In conclusion, our data suggest that a high-methylation phenotype characterizes an aggressive biologic variant of JMML and is an important molecular predictor of outcome.

Juvenile myelomonocytic leukemia (JMML) is an aggressive myeloproliferative disorder of early childhood characterized by mutations activating RAS signaling. Established clinical and genetic markers fail to fully recapitulate the clinical and biological heterogeneity of this disease. Here we report DNA methylome analysis and mutation profiling of 167 JMML samples. We identify three JMML subgroups with unique molecular and clinical characteristics. The high methylation group (HM) is characterized by somatic PTPN11 mutations and poor clinical outcome. The low methylation group is enriched for somatic NRAS and CBL mutations, as well as for Noonan patients, and has a good prognosis. The intermediate methylation group (IM) shows enrichment for monosomy 7 and somatic KRAS mutations. Hypermethylation is associated with repressed chromatin, genes regulated by RAS signaling, frequent co-occurrence of RAS pathway mutations and upregulation of DNMT1 and DNMT3B, suggesting a link between activation of the DNA methylation machinery and mutational patterns in JMML.