Quantitative DNA methylation predicts survival in adult acute myeloid leukemia

• Published in: Blood Vol. 115; no. 3; pp. 636 - 642
• Main Authors: Bullinger, Lars, Ehrich, Mathias, Döhner, Konstanze, Schlenk, Richard F., Döhner, Hartmut, Nelson, Matthew R., van den Boom, Dirk
• Format: Journal Article
• Language: English
• Published: Washington, DC Elsevier Inc 21-01-2010; Americain Society of Hematology
• Subjects: Adult; Biological and medical sciences; Chromosomes, Human, Pair 17; CpG Islands - genetics; Cytogenetic Analysis - methods; DNA Methylation - physiology; DNA, Neoplasm - analysis; Gene Expression Profiling - methods; Hematologic and hematopoietic diseases; Humans; Leukemia, Myeloid, Acute - diagnosis; Leukemia, Myeloid, Acute - genetics; Leukemia, Myeloid, Acute - mortality; Leukemias. Malignant lymphomas. Malignant reticulosis. Myelofibrosis; Medical sciences; Multivariate Analysis; Oligonucleotide Array Sequence Analysis - methods; Prognosis; Promoter Regions, Genetic - genetics; Sequence Analysis, DNA - methods; Survival Analysis; Human; Acute myelogenous leukemia; Hematology; DNA; Adult; Malignant hemopathy; Predictive factor; Methylation; Survival; Cancer; Quantitative analysis
• ISSN: 0006-4971; 1528-0020
• DOI: 10.1182/blood-2009-03-211003

Acute myeloid leukemia (AML) is characterized by molecular heterogeneity that is not fully reflected in the current classification system. Recent insights point toward a significant role of aberrant DNA methylation in leukemogenesis. Therefore, we investigated the prognostic impact of DNA methylation in AML. To screen for promoter methylation in AML we applied a combination of base-specific cleavage biochemistry and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), a powerful methodology allowing for quantitatively investigating DNA methylation status in a large series of both promoter regions and leukemia samples. We analyzed 92 genomic regions in 182 patient samples, correlated findings with clinical and molecular data, and validated the results in an independent cohort of 74 AML samples. Using this approach, we were able to identify novel leukemia subgroups based on distinct DNA methylation patterns. Furthermore, we defined a methylation-based outcome predictor for patient survival (P < .01) that in multivariable analysis provided independent prognostic information (hazard ratio, 1.52; 95% CI, 1.06-2.16). Here, we report the first large-scale methylation-based outcome predictor in AML, and thereby our findings support the use of genomic methylation markers for improved molecular classification and prognostication in adult AML.