In their large study of 1185 patients with acute myeloid leukemia (AML), Shen and colleagues have dissected the overlapping incidences and prognostic significances of mutations of the 12 genes most frequently mutated in AML, including FLT3, NPM1, CEBPA, KIT, N-RAS, MLL, WT1, IDH1/2, TET2, DNMT3A, and ASXL1.1 

They preferentially studied 176 patients with core binding factor (CBF) AML (mostly AML carrying the 8;21 translocation), 390 patients with acute promyelocytic leukemia (APL), and 605 patients with cytogenetically normal (CN)–AML or AML carrying 11q2.3 abnormality. While they confirmed that FLT3 and KIT are the most frequently mutated genes in APL and CBF-AML, respectively,2,3  their analysis of the largest cohort of CN-AML reported to date is of particular interest. In this subgroup of patients, the highest mutation incidences were observed for the NPM1 (20.9%), MLL (14%), TET2 (12.7%), and DNMT3A (12.3%) genes. Surprisingly, FLT3 mutation incidence was only 10.8%, while the incidence of CEBPA mutations was as high as 22%. In this subgroup of 605 patients, because of the poor prognosis associated with DNMT3A mutations4,5  and the significant association observed between NPM1 and DNMT3A mutations, 2 distinct prognostic groups now emerge among patients with AML carrying the so-called “favorable” NPM1 mutation, according to DNMT3A mutation status. In multivariate analysis, DNMT3A mutations and MLL rearrangements were identified as bad prognostic factors, while bi-allelic CEBPA mutations and only the NPM1m+/DNMT3Am− pattern were associated with a better outcome.

Interestingly, DNMT3A mutations and MLL abnormalities seem to share more than a poor prognostic value. Both deregulate gene promoter methylation. Both are responsible for an up-regulation of HOXA7, HOXA9, and HOXA10 gene expression. Both are associated with the myelomonocytic or monocytic AML subsets of the French-American-British classification.6  Actually, a third class of genes encoding epigenetic modifiers, including DNMT3A, IDH1, IDH2, TET2, ASXL1, and EZH2, appears to play a major role in AML pathogenesis.7  This new class should be distinguished from the already proposed class I and class II genetic abnormalities affecting genes involved in signal transduction and differentiation pathways, respectively (see table). Very interestingly, most of the abnormalities belonging to this new class III seem to be associated with a worse patient outcome and more frequently observed in older patients with the disease. They may thus provide a genetic explanation for the poorer treatment effects observed in older as opposed to younger patients, even in patients with favorable cytogenetic or genetic features, as defined by the European LeukemiaNet classification.8  Large studies, taking into account the incidence and prognostic impact of these newly described genetic events in older patients specifically, are thus needed.

Table 1

Gene mutations in AML

Class IClass IIClass III?Other
Pathways: Signal Transduction Differentiation Epigenetic regulation Tumor suppression 
Genes FLT3 RUNX1 (AML1) TET2 WT1 
 KIT CBFβ IDH1, IDH2 TP53 
 NRAS, KRAS CEBPA DNMT3A  
 JAK2 NPM1 ASXL1  
 PTPN11 PU1 EZH2  
  MLL   
  RARA   
Class IClass IIClass III?Other
Pathways: Signal Transduction Differentiation Epigenetic regulation Tumor suppression 
Genes FLT3 RUNX1 (AML1) TET2 WT1 
 KIT CBFβ IDH1, IDH2 TP53 
 NRAS, KRAS CEBPA DNMT3A  
 JAK2 NPM1 ASXL1  
 PTPN11 PU1 EZH2  
  MLL   
  RARA   

Awaiting efficient targeted therapies, the open issue is how gene mutation patterns may help physicians to guide the management of patients with AML in the daily practice. Given the large number of mutations already described and the fact that they often partially overlap, the definition of a standardized and well-accepted prognostic algorithm based on mutation patterns will not be an easy task. Other tools, such as gene expression profiling (GEP) or minimal residual disease (MRD) monitoring, might also be useful, for integrating multidimensional information in a single signature or measure. Monitoring MRD may offer the additional advantage to also integrate personalized information on anti-leukemic drug metabolism. However, all these approaches, including mutation patterns, GEPs, or MRD monitoring, remain based on AML bulk disease examination. Thus, they do not take into account the level of genetic instability and potential for clonal evolution of leukemic stem cells or AML cell subpopulations with stem cell features that could give birth to AML relapse, as recently brilliantly demonstrated in acute lymphoblastic leukemia.9,10 

Conflict-of-interest disclosure: The author declares no competing financial interests. ■

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