DNA methylation analysis improves the prognostication of acute myeloid leukemia

Integration of orthogonal data could provide new opportunities to pinpoint the underlying molecular mechanisms of hematologic disorders. Using a novel gene network approach, we integrated DNA methylation data from The Cancer Genome Atlas (n = 194 cases) with the corresponding gene expression profile. Our integrated gene network analysis classified AML patients into low‐, intermediate‐, and high‐risk groups. The identified high‐risk group had significantly shorter overall survival compared to the low‐risk group (p‐value ≤ [Formula: see text]). Specifically, our approach identified a particular subgroup of nine high‐risk AML cases that died within 2 years after diagnosis. These high‐risk cases otherwise would be incorrectly classified as intermediate‐risk solely based on cytogenetics, mutation profiles, and common molecular characteristics of AML. We confirmed the prognostic value of our integrative gene network approach using two independent datasets, as well as through comparison with European LeukemiaNet and LSC17 criteria. Our approach could be useful in the prognostication of a subset of borderline AML cases. These cases would not be classified into appropriate risk groups by other approaches that use gene expression, but not DNA methylation data. Our findings highlight the significance of epigenomic data, and they indicate integrating DNA methylation data with gene coexpression networks can have a synergistic effect.