JNCI Journal of the National Cancer Institute · 2015 · 18 citations · 40 references
MedicineCancer BiologyMolecular GeneticsEpigeneticsEpigenomicsMolecular OncologyCancer EpigeneticsDna MethylationDna Methylation ProfilingEpigenetic Biomarker AnalysisEpigenetic Biomarker DiscoveryEpigenetic BiomarkersAcute Leukemia ChemotherapyAcute Myeloid Leukemia BiologyCancer Epigenetic BiomarkersCox RegressionCytarabine-based RegimensKaplan-meier Confidence IntervalsKaplan-meier Survival EstimatesLeukemia Classification
BACKGROUND: Acute myeloid leukemia (AML) is curable in a subset of cases. The DNA methylation regulator TET2 is frequently mutated in AML, and we hypothesized that studying TET2-specific differentially methylated CpGs (tet2-DMCs) improves AML classification. METHODS: We used bisulfite pyrosequencing to analyze the methylation status of four tet2-DMCs (SP140, MCCC1, EHMT1, and MTSS1) in a test group of 94 consecutive patients and a validation group of 92 consecutive patients treated with cytarabine-based chemotherapy. Data were analyzed with hierarchical clustering, Cox proportional hazards regression, and Kaplan-Meier analyses. All statistical tests were two-sided. RESULTS: In the test cohort, hierarchical clustering analysis identified low levels of tet2-DMC methylation in 31 of 94 (33%) cases, and these had markedly longer overall survival (median survival 72+ vs 14 months, P = .002). Similar results were seen in the validation cohort. tet2-DMC-low status was shown to be an independent predictor of overall survival (hazard ratio = 0.29, P = .0002). In The Cancer Genome Atlas (TCGA) dataset where DNA methylation was analyzed by a different platform, tet2-DMC-low methylation was also associated with improved outcome (median survival = 55 vs 15 months, P = .0003) and was a better predictor of survival than mutations in TET2, IDH1, or IDH2, individually or combined. CONCLUSIONS: Low levels of tet2-DMC methylation define a subgroup of AML that is highly curable and cannot be identified solely by genetic and cytogenetic analyses.
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Timothy J. Ley, Li Ding, Matthew J. Walter et al. · New England Journal of Medicine · 2010 · 2K citations · Full text