Epigenetic markers expand genetic risk estimation in acute myeloid leukemia

Gene mutations and chromosome abnormalities are important components of prognostication in acute myeloid leukemia (AML). Here we assessed whether DNA methylation patterns in AML patients can augment risk assessments provided by genetic and other markers to better predict outcomes. Unsupervised DNA methylation patterns separating patients into 13 DNA methylation subtypes (epitypes) were used to classify 1,262 patients with de novo AML. Epitypes were predominantly comprised of a predominant genetic alteration; however, some patients within epitypes lacked cardinal alterations and were termed genetic alteration-like. Interestingly, patients displaying alteration-like DNA methylation patterns of CEBPAbZIP, FLT3-ITD, core-binding factor, KMT2A-rearrangements and other abnormalities displayed outcomes similar to patients with actual cardinal alterations. We further derived a DNA methylation signature enriched in patients with FLT3-ITD mutations that involved hypomethylation of STAT binding sites, termed the STAT hypomethylation signature (SHS). SHS positivity identified patients with inferior outcomes further adding to the prognostic significance of FLT3-ITD. Machine learning modeling revealed these DNA methylation signatures together significantly added to genetic, demographic and clinical markers to predict remission, relapse and overall survival. In summary, DNA methylation signatures capture patients who mimic cardinal genetic mutations providing additional prognostic information that may be used to in concert with standard genetic markers.