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Clinical Trial
. 2016 Jun 9;374(23):2209-2221.
doi: 10.1056/NEJMoa1516192.

Genomic Classification and Prognosis in Acute Myeloid Leukemia

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Free PMC article
Clinical Trial

Genomic Classification and Prognosis in Acute Myeloid Leukemia

Elli Papaemmanuil et al. N Engl J Med. .
Free PMC article

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.).

Figures

Figure 1
Figure 1. Landscape of Driver Mutations in Acute Myeloid Leukemia (AML).
Panel A shows driver events in 1540 patients with AML. Each bar represents a distinct driver lesion; the lesions include gene mutations, chromosomal aneuploidies, fusion genes, and complex karyotypes. The colors in each bar indicate the molecular risk according to the European LeukemiaNet (ELN) classification. Panel B shows the distribution of samples and overlap (cross-sections) across molecular subgroups (vertical bars). Patients who had no driver mutations and those who had driver mutations but did not meet the criteria for any specific class are also included. The number at the top of each column is the number of patients assigned solely to the designated class; the numbers of patients meeting criteria for two or more classes are shown at the intersection of classes.
Figure 2
Figure 2. Identification of Molecular Subgroups in AML.
The rows in the graph represent individual genomic lesions, and the columns represent patients in the study. Vertical purple lines (some of which appear as blocks because of clustering) indicate the presence of a specified driver mutation in a patient. The patients have been ordered by group membership; orange lines demarcate boundaries between classes. OH-meCpG denotes hydroxymethyl CpG.
Figure 3
Figure 3. Molecular Subclassification and Overall Survival.
Panel A shows Kaplan–Meier curves for overall survival among patients in the 11 genomically defined subgroups and patients who did not have a straightforward classification. Panel B shows Kaplan–Meier curves for overall survival with the additive and independent prognostic effects of TP53 mutation and complex karyotype (TP53 mutation, 17 patients; complex karyotype, 89; and TP53 mutation with complex karyotype, 70). Panel C shows Kaplan–Meier curves for overall survival with the additive and independent prognostic effects of ASXL1 and SRSF2 mutations (ASXL1 mutation, 55 patients; SRSF2 mutation, 74; and ASXL1 and SRSF2 mutations, 15). In Panels B and C, wt denotes wild type, and mut mutation. The bar chart in Panel D shows concordance estimates for overall survival with the use of variables selected in our model (concordance, approximately 71%) as compared with a model using variables considered in the ELN guidelines (concordance, approximately 64%). The doughnut chart shows the relative proportion of explained variance in overall survival in the full model that is accounted for by different categories of predictor variables. Clinical variables are performance status, splenomegaly, bone marrow blasts, and blood counts. Demographic variables are age and sex. Nuisance variables are other variables (e.g., which trial a patient was enrolled in, what year a patient entered the clinical trial, and whether cytogenetic data were missing). The volcano plot in Panel E shows the incremental contribution to the effect size (expressed as the logarithmic hazard on the x axis; positive values indicate a worsening effect) versus P values (expressed on an inverted logarithmic scale on the y axis) for each of the 228 variables included in the random-effects model. The circles above the dotted line represent 18 variables with a q value of less than 0.1; the size of each circle corresponds to the frequency of the variable, as indicated in the box. The incremental contribution of age is shown for every 10 years of age, and the incremental contribution of the white-cell count (WBC) is shown for each increase of 1×109 cells per liter. The colors of the circles correspond to the colors shown in the doughnut chart in Panel D.
Figure 4
Figure 4. Influence of Gene–Gene Interactions on Overall Survival.
Panel A shows Kaplan–Meier curves for overall survival according to the presence or absence of FLT3ITD. The deleterious prognostic effect of FLT3ITD was significantly greater when both DNMT3A and NPM1 were mutated, as shown in the graph at the right (P = 0.009 for three-way interaction in the univariate analysis; q= 0.004 in the multivariate analysis with correction for multiple hypothesis testing). A total of 28 patients had both DNMT3A and FLT3ITD, 77 had both NPM1 and FLT3ITD, and 93 had all three mutations. Panel B shows Kaplan–Meier curves for overall survival according to the presence or absence of NRAS codon 12/13 mutation. The prognostic effect of NRAS codon 12/13 mutation was significantly greater when both DNMT3A and NPM1 carried the driver mutation (in 45 patients), as shown in the graph at the right (P = 0.0007 for three-way interaction in the univariate analysis). Panel C shows Kaplan–Meier curves for overall survival according to the presence or absence of MLLPTD and FLT3TKD (P = 0.0004 for gene–gene interaction in the univariate analysis; q = 0.008 in the multivariate analysis with correction for multiple hypothesis testing). A total of 69 patients had MLLPTD, 112 had FLT3TKD, and 10 had both. Panel D shows Kaplan–Meier curves for overall survival according to the presence or absence of driver mutations in DNMT3A, IDH2R140, or both (P = 0.05 for gene–gene interaction in the univariate analysis; q= 0.05 in the multivariate analysis with correction for multiple hypothesis testing). A total of 338 patients had DNMT3A, 20 had IDH2R140, and 19 had both.

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