BACKGROUND: The genetic alterations responsible for an adverse outcome in most patients with acute myeloid leukemia (AML) are unknown. METHODS: Using massively parallel DNA sequencing, we identified a somatic mutation in DNMT3A, encoding a DNA methyltransferase, in the genome of cells from a patient with AML with a normal karyotype. We sequenced the exons of DNMT3A in 280 additional patients with de novo AML to define recurring mutations. RESULTS: A total of 62 of 281 patients (22.1%) had mutations in DNMT3A that were predicted to affect translation. We identified 18 different missense mutations, the most common of which was predicted to affect amino acid R882 (in 37 patients). We also identified six frameshift, six nonsense, and three splice-site mutations and a 1.5-Mbp deletion encompassing DNMT3A. These mutations were highly enriched in the group of patients with an intermediate-risk cytogenetic profile (56 of 166 patients, or 33.7%) but were absent in all 79 patients with a favorable-risk cytogenetic profile (P<0.001 for both comparisons). The median overall survival among patients with DNMT3A mutations was significantly shorter than that among patients without such mutations (12.3 months vs. 41.1 months, P<0.001). DNMT3A mutations were associated with adverse outcomes among patients with an intermediate-risk cytogenetic profile or FLT3 mutations, regardless of age, and were independently associated with a poor outcome in Cox proportional-hazards analysis. CONCLUSIONS: DNMT3A mutations are highly recurrent in patients with de novo AML with an intermediate-risk cytogenetic profile and are independently associated with a poor outcome. (Funded by the National Institutes of Health and others.).
Pubmed ID: 21067377 RIS Download
Mesh terms: Adult | DNA (Cytosine-5-)-Methyltransferase | DNA Methylation | DNA Mutational Analysis | Female | Frameshift Mutation | Gene Expression | Humans | Karyotyping | Leukemia, Myeloid, Acute | Male | Middle Aged | Mutation | Nucleic Acid Amplification Techniques | Prognosis | Proportional Hazards Models | Survival Analysis
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