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Genomic Classification and Prognosis in Acute Myeloid Leukemia
Elli Papaemmanuil, Moritz Gerstung, Lars Bullinger, Verena I. Gaidzik, Peter Paschka, Nicola D. Roberts, Nicola Potter, Michael Heuser, Felicitas Thol, Niccolò Bolli, Gunes Gundem, Peter Van Loo, Iñigo Martincorena, Peter Ganly, Laura Mudie
New England Journal of Medicine · 2016 · ▲ 4,352 citations
Abstract
BACKGROUND: Recent studies have provided a detailed census of genes that are mutated in acute myeloid leukemia (AML). Our next challenge is to understand how this genetic diversity defines the pathophysiology of AML and informs clinical practice. METHODS: We enrolled a total of 1540 patients in three prospective trials of intensive therapy. Combining driver mutations in 111 cancer genes with cytogenetic and clinical data, we defined AML genomic subgroups and their relevance to clinical outcomes. RESULTS: We identified 5234 driver mutations across 76 genes or genomic regions, with 2 or more drivers identified in 86% of the patients. Patterns of co-mutation compartmentalized the cohort into 11 classes, each with distinct diagnostic features and clinical outcomes. In addition to currently defined AML subgroups, three heterogeneous genomic categories emerged: AML with mutations in genes encoding chromatin, RNA-splicing regulators, or both (in 18% of patients); AML with TP53 mutations, chromosomal aneuploidies, or both (in 13%); and, provisionally, AML with IDH2(R172) mutations (in 1%). Patients with chromatin-spliceosome and TP53-aneuploidy AML had poor outcomes, with the various class-defining mutations contributing independently and additively to the outcome. In addition to class-defining lesions, other co-occurring driver mutations also had a substantial effect on overall survival. The prognostic effects of individual mutations were often significantly altered by the presence or absence of other driver mutations. Such gene-gene interactions were especially pronounced for NPM1-mutated AML, in which patterns of co-mutation identified groups with a favorable or adverse prognosis. These predictions require validation in prospective clinical trials. CONCLUSIONS: The driver landscape in AML reveals distinct molecular subgroups that reflect discrete paths in the evolution of AML, informing disease classification and prognostic stratification. (Funded by the Wellcome Trust and others; ClinicalTrials.gov number, NCT00146120.).
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- 10.1056/nejmoa1516192
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- 2026-06-06 MST
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APA
Papaemmanuil, E., Gerstung, M., Bullinger, L., Gaidzik, V.I., Paschka, P., Roberts, N.D., Potter, N., Heuser, M., Thol, F., Bolli, N., Gundem, G., Loo, P.V., Martincorena, I., Ganly, P., Mudie, L., McLaren, S., O’Meara, S., Raine, K., Jones, D., & Teague, J.W. (2016). Genomic Classification and Prognosis in Acute Myeloid Leukemia. <em>New England Journal of Medicine</em>. https://doi.org/10.1056/nejmoa1516192
Vancouver
Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND, et al. Genomic Classification and Prognosis in Acute Myeloid Leukemia. New England Journal of Medicine. 2016. doi:10.1056/nejmoa1516192.
BibTeX
@article{elli2016Genomi,
title = {Genomic Classification and Prognosis in Acute Myeloid Leukemia},
author = {Elli Papaemmanuil and Moritz Gerstung and Lars Bullinger and Verena I. Gaidzik and Peter Paschka and Nicola D. Roberts and Nicola Potter and Michael Heuser and Felicitas Thol and Niccolò Bolli and Gunes Gundem and Peter Van Loo and Iñigo Martincorena and Peter Ganly and Laura Mudie and Stuart McLaren and Sarah O’Meara and Keiran Raine and David Jones and Jon W. Teague and Adam P. Butler and Mel Greaves and Arnold Ganser and Konstanze Döhner and Richard F. Schlenk},
journal = {New England Journal of Medicine},
year = {2016},
doi = {10.1056/nejmoa1516192},
}
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