DNA methylation identifies genetically and prognostically distinct subtypes of myelodysplastic syndromes

Autor: Dinh Diep, Brian M. Reilly, Tiffany N. Tanaka, Pablo Tamayo, Rafael Bejar, Kun Zhang, Huwate Yeerna
Rok vydání: 2019
Předmět:
Zdroj: Blood advances, vol 3, iss 19
Popis: Recurrent mutations implicate several epigenetic regulators in the early molecular pathobiology of myelodysplastic syndromes (MDS). We hypothesized that MDS subtypes defined by DNA methylation (DNAm) patterns could enhance our understanding of MDS disease biology and identify patients with convergent epigenetic profiles. Bisulfite padlock probe sequencing was used to measure DNAm of ∼500 000 unique cytosine guanine dinucleotides covering 140 749 nonoverlapping regulatory regions across the genome in bone marrow DNA samples from 141 patients with MDS. Application of a nonnegative matrix factorization (NMF)–based decomposition of DNAm profiles identified 5 consensus clusters described by 5 NMF components as the most stable grouping solution. Each of the 5 NMF components identified by this approach correlated with specific genetic abnormalities and categorized patients into 5 distinct methylation clusters, each largely defined by a single NMF component. Methylation clusters displayed unique differentially methylated regulatory loci enriched for active and bivalent promoters and enhancers. Two clusters were enriched for samples with complex karyotypes, although only one had an increased number of TP53 mutations. Each of the 3 most frequently mutated splicing factors, SF3B1, U2AF1, and SRSF2, was enriched in different clusters. Mutations of ASXL1, EZH2, and RUNX1 were coenriched in the SRSF2-containing cluster. In multivariate analysis, methylation cluster membership remained independently associated with overall survival. Targeted DNAm profiles identify clinically relevant subtypes of MDS not otherwise distinguished by mutations or clinical features. Patients with diverse genetic lesions can converge on common DNAm states with shared pathogenic mechanisms and clinical outcomes.
Databáze: OpenAIRE