Genomic Classification and Outcome in Acute Myeloid Leukemia


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As reported in The New England Journal of Medicine, Elli Papaemmanuil, PhD, of the Cancer Genome Project, Wellcome Trust Sanger Institute, and colleagues have identified 11 genomic subgroups in acute myeloid leukemia (AML) associated with distinct diagnostic features and clinical outcomes.

AML Subgroups

The study included analysis of driver mutations in 111 cancer genes and cytogenetic and clinical data from 1,540 patients in 3 clinical trials of intensive therapy. A total of 5,234 driver mutations were identified across 76 genes/genomic regions, with at least 2 drivers identified in 86% of patients.

Concurrent mutation patterns divided the cohort into 11 distinct classes. In addition to currently defined AML subgroups, three notable heterogeneous genomic classes were identified, consisting of AML with mutations in genes encoding chromatin, RNA-splicing regulators, or both (18% of patients); AML with TP53 mutations, chromosomal aneuploidies, or both (13%); and (provisionally) AML with IDH2 R172 mutations (1%).

Patients with chromatin-spliceosome and TP53-aneuploidy AML had a poor outcome, with the various class-defining mutations being associated with outcomes both alone and in combination. Other concurrent driver mutations in addition to class-defining alterations also had an effect on survival.

Divergent Effects of Concurrent Mutations

Prognostic effects of individual mutations were found to be significantly altered by the presence or absence of other driver mutations. Such interactions were especially notable for NPM1-mutated AML, with outcomes among patients with NPM1 mutation varying according to the presence or absence of NRAS, IDH, PTPN11, FLT3, and chromatin-spliceosome mutations. An adverse prognostic effect of FLT3-ITD was most evident in patients with concomitant NPM1 and DNMT3A mutations. The concomitant presence of NPM1, DNMT3A, and NRAS G12/13 mutations was associated with a better prognosis.

The investigators concluded: “The driver landscape in AML reveals distinct molecular subgroups that reflect discrete paths in the evolution of AML, informing disease classification and prognostic stratification…. Prospective clinical studies are needed for further validation of this schema.”

Dr. Papaemmanuil; Moritz Gerstung, PhD; and Peter J. Campbell, MB, ChB, PhD, of the Cancer Genome Project, Wellcome Trust Sanger Institute; and Hartmut Döhner, MD, of Ulm University contributed equally to The New England Journal article.

Papaemmanuil E, et al: N Engl J Med 374:2209-2221, 2016.



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