Numerous molecular markers have been recently discovered as potential prognostic factors in acute myeloid leukemia (AML). It has become of critical importance to thoroughly evaluate their interrelationships and relative prognostic importance. Gene expression profiling was conducted in a well-characterized cohort of 439 AML patients (age < 60 years) to determine expression levels of EVI1, WT1, BCL2, ABCB1, BAALC, FLT3, CD34, INDO, ERG and MN1. A variety of AML-specific mutations were evaluated, that is, FLT3, NPM1, N-RAS, K-RAS, IDH1, IDH2, and CEBPADM/SM (double/single). Univariable survival analysis shows that (1) patients with FLT3ITD mutations have inferior overall survival (OS) and event-free survival (EFS), whereas CEBPADM and NPM1 mutations indicate favorable OS and EFS in intermediate-risk AML, and (2) high transcript levels of BAALC, CD34, MN1, EVl1, and ERG predict inferior OS and EFS. In multivariable survival analysis, CD34, ERG, and CEBPADM remain significant. Using survival tree and regression methodologies, we show that CEBPADM, CD34, and IDH2 mutations are capable of separating the intermediate group into 2 AML subgroups with highly distinctive survival characteristics (OS at 60 months: 51.9% vs 14.9%). The integrated statistical approach demonstrates that from the multitude of biomarkers a greatly condensed subset can be selected for improved stratification of intermediate-risk AML.

It is widely accepted that certain cytogenetic abnormalities consistently associate with particular subsets of acute myeloid leukemia (AML) that carry distinct responses to therapy.1,2  Approximately 40% of all AML patients are currently classified into distinct groups with variable prognosis based on the presence or absence of specific recurrent cytogenetic abnormalities. AML without favorable and particular unfavorable cytogenetic aberrations is classified as intermediate prognosis. The intermediate-risk cytogenetic subclass of AML includes cytogenetically normal (CN) and AML with other cytogenetic abnormalities and accounts for approximately 60% of all AML patients; and accordingly, recent gene-mutation and gene-expression studies represent a mixture of leukemias with favorable and unfavorable prognoses.

In recent years, a variety of novel molecular markers have refined the risk stratification of intermediate-risk AML. For instance, mutations in FLT3,3-5 NPM1,6-9  and CEBPA10-16  all carry variable prognostic value. Recently, IDH1 and IDH2 mutations were identified; but for the time being, the prognostic value of these mutations appears to be controversial.17-20 

Besides acquired mutations, a number of individual genes have been proposed as important prognostic expression markers (ie, specific gene expression levels were shown to be associated with treatment outcome in AML). For instance, expression of EVI1,21-23 BAALC,24,25 ERG,26,27  and MN128,29  was proposed as indicators for treatment outcome in AML. Some expression markers, such as WT1,30-33 BCL2,34 INDO,35 CD34,36 ABCB1,37  and FLT3,38  have been put forward as clinical markers, but their applicability has been less well established or has been controversial.

Previous studies have often assessed the prognostic value of various biomarkers on an individual basis or in a limited collective context. For the purpose of risk stratification and understanding of the relative prognostic importance, it has become crucial to integrate them in a joint analysis. In the present study, we investigate the role of gene expression markers EVI1, WT1, BAALC, ERG, BCL2, ABCB1, INDO, CD34, BCL2, and MN1 (evaluated using standardized microarray analysis) as well as somatic gene mutations in FLT3, N-RAS, CEBPASM,CEBPADM, NPM1, IDH1, and IDH2 in survival prognosis in cytogenetically defined intermediate-risk AML. In addition to univariate and multivariate analyses, we adopted a statistical approach that is capable of deriving a simplified prognostic index that can be used for the risk stratification of the intermediate-risk group.

Patients, cell samples, and molecular analyses

We investigated a cohort of 439 patients (age < 60 years) with a diagnosis of primary AML or refractory anemia with excess blasts(-t) (n = 17; Table 1). All patients were treated according to the HOVON (Dutch-Belgian Hematology-Oncology Cooperative group) protocols between 1987 and 2006 (www.hovon.nl).39-41  This study was approved by the Medical Ethical Committee of the Erasmus University Medical Center.

Table 1

Clinical and molecular characteristics of the 439 patients

VariablesRangeMean/median or %No. of patients
Clinical variables    
    White blood cell count, × 109/L 0.3-278 52.0/29.8 (mean/median)  
    Bone marrow blast count, % 0-98 62.1/66 (mean/median)  
    Platelet count, × 109/L 3-998 78.9/52  
Patient characteristics    
    Age, y 15-60 42.11/43 (mean/median)  
    Sex (female)   219 
FAB classification    
    M0  3.6 16 
    M1  19.1 84 
    M2  23.2 102 
    M3  22 
    M4  18.5 81 
    M4Eo  6.2 27 
    M5  23.7 104 
    M6  1.1 
    RAEB  0.9 
    Not determined  4.8 21 
Cytogenetics    
    t(8;21)  35 
    inv (16)  8.2 36 
    t(15;17)  5.7 25 
    CN  43.5 192 
    CA  28.7 126 
    MK*  5.7 25 
Mutations    
    NPM1+  29.6 130 
    FLT3ITD 26.9 118 
    FLT3TKD 10.7 47 
    N-RAS+  987 43 
    K-RAS+  0.9 
    CEBPASM 1.6 
    CEBPADM 5.2 23 
    IDH1+  7.2 32 
    IDH2+  8.2 36 
    NPM1+ FLT3ITD 15.3 67 
    NPM1+ FLT3ITD−  14.4 63 
    NPM1− FLT3ITD 11.6 51 
    NPM1− FLT3ITD−  58.8 258 
VariablesRangeMean/median or %No. of patients
Clinical variables    
    White blood cell count, × 109/L 0.3-278 52.0/29.8 (mean/median)  
    Bone marrow blast count, % 0-98 62.1/66 (mean/median)  
    Platelet count, × 109/L 3-998 78.9/52  
Patient characteristics    
    Age, y 15-60 42.11/43 (mean/median)  
    Sex (female)   219 
FAB classification    
    M0  3.6 16 
    M1  19.1 84 
    M2  23.2 102 
    M3  22 
    M4  18.5 81 
    M4Eo  6.2 27 
    M5  23.7 104 
    M6  1.1 
    RAEB  0.9 
    Not determined  4.8 21 
Cytogenetics    
    t(8;21)  35 
    inv (16)  8.2 36 
    t(15;17)  5.7 25 
    CN  43.5 192 
    CA  28.7 126 
    MK*  5.7 25 
Mutations    
    NPM1+  29.6 130 
    FLT3ITD 26.9 118 
    FLT3TKD 10.7 47 
    N-RAS+  987 43 
    K-RAS+  0.9 
    CEBPASM 1.6 
    CEBPADM 5.2 23 
    IDH1+  7.2 32 
    IDH2+  8.2 36 
    NPM1+ FLT3ITD 15.3 67 
    NPM1+ FLT3ITD−  14.4 63 
    NPM1− FLT3ITD 11.6 51 
    NPM1− FLT3ITD−  58.8 258 

Mutation present (or absent) groups denoted with (+) or (−).

RAEB indicates refractory anemia with excess blasts; FAB, French-American-British; CN, normal cytogenetics or -X or -Y as the sole abnormality; CA, cytogenetically abnormal; MK, monosomal karyotype; and M4Eo, M4 category with inv(16).

*

MK category contains 12 AML patients classified as complex karyotype and 13 other cases with complex karyotypes are in the CA category.

All AML cases in this study were also included in other studies,16,17,22,23,42  and subsets of cases have also been investigated in other studies35,43-45  (supplemental Table 1, available on the Blood Web site; see the Supplemental Materials link at the top of the online article). The earlier studies had different study objectives (ie, dealing with individual markers or selected subsets of leukemia; for instance, CN AML).

AML was cytogenetically classified into the following prognostic categories: (I) favorable: t(8;21) and inv(16); (II) very unfavorable: monosomal karyotypes as defined earlier46 ; (III) intermediate-risk I: CN; and (IV) intermediate-risk II: the remaining AML cases (cytogenetically abnormal).

After informed consent was given in accordance with the Declaration of Helsinki, bone marrow aspirates or peripheral blood samples were taken at diagnosis. Blasts and mononuclear cells were purified by Ficoll-Hypaque (Nygaard) centrifugation and cryopreserved. The AML samples contained 80%-100% blast cells after thawing, regardless of the blast count at diagnosis. Mutational analyses were all performed as described previously.9,12,17,47,48 

Gene profiling and quality control for assessment of gene expression variations

A total of 439 AML samples were analyzed using Affymetrix U133Plus2.0 GeneChips (Affymetrix) that contains 54 675 probe sets, representing 20 650 unique genes. The methods have been reported in detail elsewhere.45,49  The differences between the scaling/normalization factors of the GeneChips in complete cohort was < 3-fold (0.62 ± 0.20). All additional measures of quality (percentage genes present, [39.8 ± 3.5]; GAPDH 3′ to 5′ ratio, [1.08[ ± 0.15]; and actin 3′to 5′ ratio, [1.30 ± 0.26]), indicated high overall sample and assay quality in the complete AML cohort.

Informative probe sets detecting expression of various genes were selected. Only those probe sets with accurate annotation and genomic localization using the ENSEMBL genome browser (www.ensembl.org) were included: ABCB1: 209993_at and 209994_s_at; WT1: 206067_s_at and 216953_s_at; BCL2: 203684_s_at and 203685_at; BAALC: 218899_s_at and 222780_s_at; ERG: 213541_s_at and 241926_s_at; EVI1: 221884_at and 226420_at; FLT3: 206674_at; CD34: 209543_s_at; and MN1: 205330_at and INDO: 210029_at.

Data preparation

Each of the mutation markers is coded as a binary variable (ie, mutation present [+] or absent). The gene expression of each selected gene was determined from either a single probe or a combination of multiple probes linked to that gene. Probe sets for each expression marker were selected from the Affymetrix U133Plus2.0 GeneChip, based on an accurate annotation and localization using the ENSEMBL genome browser. If one probe per gene was available (MN1, CD34, FLT3, and INDO1), the probe expression intensity was log2-transformed and scaled so that the minimal value equals 0 and the maximal value equals 1. In case multiple probe sets were annotated for a single gene (BAALC, BCL2, ABCB1, EVI1, WT1, and ERG), we reduced the number of variables by performing a factor analysis per gene using the log2-transformed expression data of all 439 AML patient samples. This resulted in a factor score, composed of the expression values from all the representative probe sets, for each individual expression marker. The factor scores were also rescaled so that the minimal value of the score for each gene is 0 and the maximal value is 1.

Statistical analysis

Statistical analyses were performed with R (Version 2.9.2). Both overall survival (OS; with failure defined as death because of any cause) and event-free survival (EFS; with failure defined as no complete remission [set at day 1], relapse, or death in first complete remission) were considered as endpoints for survival analyses.

To determine the prognostic value of the markers, the Cox proportional hazard regression model was used in univariable and multivariable analyses. To further inspect the prognostic importance and/or redundancy of the markers, we applied a variable selection in the Cox proportional hazards model, namely, the Akaike information criterion-based stepwise variable selection and the Least Absolute Shrinkage and Selection Operator (LASSO),50  where the optimal penalty parameter was chosen so that it maximizes the cross-validated partial log-likelihood (20-fold cross-validation).51  To further evaluate the hierarchy of the prognostic importance, we used tree-structured survival modeling (unbiased recursive partitioning approach of Hothorn et al52 ). Estimated probabilities of OS and EFS were calculated using the Kaplan-Meier method. Partial likelihood ratio test was used to evaluate differences between survival distributions.

The bimodal shape of the EVI1 expression distribution (supplemental Figure 3) suggests that there are 2 populations of patients with high and low EVI1 expression. A mixture model fit with normally distributed components52  supports the evidence for this observation (supplemental Figure 3). The intersection point of the 2 superimposed densities naturally suggests a threshold (c = 1.15) to decide whether or not the EVI1 was overexpressed. EVI1 expression based on quantitative reverse-transcribed polymerase chain reaction was treated as a categorical variable in previous reports.21,22  Here we also use a categorical EVI1 in survival analyses using the reference value of 1.15. A penalized spline fit in Cox proportional hazard regression suggests a nonlinear behavior of EVI1 (P value of a test for linearity .04, best degrees of freedom 2.1 determined by Akaike information criterion). Despite a piecewise constant, transformation might not be the best approximation of the true relationship it manages to separate the distinctive survival characteristic of the small group of patients (8.8%) with high EVI1, which would be masked if we treated EVI1 as linear. The remaining markers are treated on a continuous scale in accordance with their actual distribution pattern. Unless otherwise stated, with “high expression” we refer to high values extreme with respect to the distribution of each marker.

Pair-wise associations between binary markers were assessed by means of χ2 test (or Fisher [Halton-Freeman] exact tests when the expected count number in at least one of the cells dropped to < 5). The direction of the observed associations was measured by a φ coefficient. Spearman correlation coefficient was used to assess the pair-wise correlations between gene expression markers. Differential gene expression across patient subcategories was tested by means of Wilcoxon sum-rank test (2 categories only) and Kruskal-Wallis test (> 2 categories). The level .05 has been used as a threshold to declare the statistical significance.

Recurrent mutations and expression levels in cytogenetically defined AML subsets

Details on the molecular and clinical characteristics of the investigated cohort of 439 patients are summarized in Table 1.

The distribution of the recurrent mutations among the cytogenetically defined AML subsets is summarized in supplemental Table 2. We note increased frequencies of FLT3ITD and NPM1 mutations in CN AML as well as the common occurrence of FLT3ITD and FLT3TKD in AML with t(15;17). The prevalence of FLT3TKD and N-RAS mutations is higher in AML with inv(16). IDH1 and both CEBPASM and CEBPADM were observed exclusively in intermediate-risk cytogenetic categories (CN and cytogenetically abnormal). K-RAS mutations are relatively rare in AML and were not considered in further analyses.

The majority of expression markers genes show a differential expression in the cytogenetically defined AML subsets (supplemental Figure 1). Expression marker genes INDO1 and FLT3 do not have distinctive expression patterns in relationship to the cytogenetically defined subgroups (P values of the overall Kruskal-Wallis test: .301 and .204, respectively). Compared with the normal karyotype group, significantly higher expression of WT1 is associated with t(15;17) (P < .001), relatively low BCL2 expression is observed in AML with t(8;21) (P < .001), and high expression of both BAALC and CD34 was detected in t(8;21) and inv(16) groups (P < .001 for both comparisons). We further noticed elevated MN1 expression in AML with inv(16) compared with the CN group (P < .001).

Associations between recurrent mutation and expression markers

The summary of pair-wise associations between the binary mutation markers is given in supplemental Table 3. FLT3ITD, FLT3TKD, and IDH1 mutations appear significantly overrepresented in NPM1 mutant group (φ coefficients 0.36, 0.15, and 0.28, respectively). On the other hand, FLT3ITD are more prevalent in AML without FLT3TKD (φ = −0.11), N-RAS (φ = −0.2), or IDH2 mutations (φ = −0.11).

Spearman correlation analysis between the gene expression markers in Figure 1 revealed the following associations: (1) the expression of the marker genes BAALC, CD34, MN1, ERG, and ABCB1 is relatively strongly associated; (2) BAALC exhibits the strongest positive correlation with CD34 expression (correlation coefficient [R] = 0.78) and MN1 (R = 0.76); and (3) moderate associations are also observed between ERG and WT1 (R = 0.43), ERG and BCL2 (R = 0.4), and BCL2 and WT1 (R = 0.36); (4) INDO1 appears to be inversely associated with EVI1 (R = −0.25), WT1 (R = −0.28), and ERG (R = −0.14).

Figure 1

Associations between the gene expression markers. Bottom triangle represents a scatter-plot matrix of the markers, where the red lines are the lowess smoothing curves. Top triangle represents pair-wise Spearman correlation coefficients. On the diagonal, there are histograms of each of the markers.

Figure 1

Associations between the gene expression markers. Bottom triangle represents a scatter-plot matrix of the markers, where the red lines are the lowess smoothing curves. Top triangle represents pair-wise Spearman correlation coefficients. On the diagonal, there are histograms of each of the markers.

The summary of the association analysis between the mutation and gene expression markers is given in supplemental Table 4. The NPM1 mutant patient group is significantly associated with higher WT1 expression. In contrast, the expression of BCL2, BAALC, ERG, ABCB1, CD34, and MN1 is elevated in NPM1 wild-type AML. Other associations that we observe are, for example, decreased BAALC, CD34, and MN1 expression as well as increased FLT3 and WT1 expression in FLT3ITD AML. Increased ABCB1 expression associates with CEBPADM AML. Likewise, BAALC and CD34 expression is higher in IDH1 wild-type AML, and BCL2 expression is higher in IDH2 mutant AML.

Survival analyses in intermediate- risk AML

Univariable survival analysis (Table 2) indicated inferior OS in intermediate-risk AML patients with FLT3ITD mutations (hazard ratio [HR] = 1.41; P = .017), whereas CEBPADM (OS: HR = 0.38, P = .004; EFS: HR = 0.45, P = .007) and NPM1 mutations (OS: HR = 0.73, P = .03; EFS: HR = 0.69; P = .006) were found indicative of favorable OS and EFS. The positive prognostic impact of NPM1 mutations becomes even more pronounced in FLT3ITD-negative AML (OS: HR = 0.63, P = .022; EFS: HR = 0.64, P = .018). Univariable analysis of the gene expression markers demonstrates that increased expressions of BAALC, CD34, MN1, EVI1, and ERG are significant negative indicators for OS and EFS (all HR > 1.5, P < .01). Univariable survival analysis for CN AML is given in supplemental Table 5. The negative predictive effect of FLT3ITD, BAALC, CD34, EVI1, and ERG is retained in CN AML.

Table 2

Univariable survival analysis in the intermediate-risk group

VariableOS
EFS
Hazard ratioLowerUpperPHazard ratioLowerUpperP
NPM1 0.73 0.55 0.97 .03 0.69 0.53 0.9 .006 
FLT3ITD 1.41 1.06 1.86 .017 1.3 0.99 1.7 .059 
FLT3TKD 0.82 0.51 1.32 .418 0.74 0.47 1.16 .192 
N-RAS 0.94 0.57 1.54 .798 1.23 0.77 1.94 .386 
CEBPASM 1.01 0.38 2.72 .984 0.8 0.3 2.16 .662 
CEBPADM 0.38 0.19 0.74 .004 0.45 0.25 0.81 .007 
IDH1 0.83 0.52 1.31 .414 0.97 0.64 1.47 .877 
IDH2 0.74 0.47 1.17 .199 0.79 0.51 1.21 .273 
FLT3ITD × NPM1 +− 1.67 1.13 2.46 .01 1.76 1.21 2.58 .003 
 −+ 0.63 0.42 0.94 .022 0.64 0.45 0.93 .018 
 ++ 1.03 0.72 1.47 .875 0.9 0.64 1.27 .549 
EVI1* 1.78 1.17 2.7 .007 2.01 1.34 3.02 < .001 
BAALC  3.16 1.74 5.72 < .001 2.9 1.63 5.16 < .001 
CD34  3.81 2.17 6.67 < .001 3.57 2.11 6.05 < .001 
MN1  2.41 1.37 4.23 .002 2.51 1.46 4.32 < .001 
ERG  3.69 1.65 8.26 .001 3.48 1.63 7.43 .001 
ABCB1  0.99 0.51 1.93 .983 0.92 0.49 1.73 .798 
BCL2  1.07 0.5 2.3 .861 1.19 0.57 2.46 .644 
INDO1  0.65 0.32 1.36 .254 0.69 0.35 1.38 0.3 
VariableOS
EFS
Hazard ratioLowerUpperPHazard ratioLowerUpperP
NPM1 0.73 0.55 0.97 .03 0.69 0.53 0.9 .006 
FLT3ITD 1.41 1.06 1.86 .017 1.3 0.99 1.7 .059 
FLT3TKD 0.82 0.51 1.32 .418 0.74 0.47 1.16 .192 
N-RAS 0.94 0.57 1.54 .798 1.23 0.77 1.94 .386 
CEBPASM 1.01 0.38 2.72 .984 0.8 0.3 2.16 .662 
CEBPADM 0.38 0.19 0.74 .004 0.45 0.25 0.81 .007 
IDH1 0.83 0.52 1.31 .414 0.97 0.64 1.47 .877 
IDH2 0.74 0.47 1.17 .199 0.79 0.51 1.21 .273 
FLT3ITD × NPM1 +− 1.67 1.13 2.46 .01 1.76 1.21 2.58 .003 
 −+ 0.63 0.42 0.94 .022 0.64 0.45 0.93 .018 
 ++ 1.03 0.72 1.47 .875 0.9 0.64 1.27 .549 
EVI1* 1.78 1.17 2.7 .007 2.01 1.34 3.02 < .001 
BAALC  3.16 1.74 5.72 < .001 2.9 1.63 5.16 < .001 
CD34  3.81 2.17 6.67 < .001 3.57 2.11 6.05 < .001 
MN1  2.41 1.37 4.23 .002 2.51 1.46 4.32 < .001 
ERG  3.69 1.65 8.26 .001 3.48 1.63 7.43 .001 
ABCB1  0.99 0.51 1.93 .983 0.92 0.49 1.73 .798 
BCL2  1.07 0.5 2.3 .861 1.19 0.57 2.46 .644 
INDO1  0.65 0.32 1.36 .254 0.69 0.35 1.38 0.3 

Mutation present (or absent) groups are denoted with (+) or (−). The reference category for all binary mutation markers is mutation absent (−). The reference category for the combined aberration in FLT3ITD and NPM1 is both mutations absent.

*

EVI1 expression categorized with the reference category EVI1 < 1.15.

The multivariable Cox regression analysis (Table 3) shows that CD34, ERG, and CEBPADM remain significant predictors for OS and EFS after the correction for the remaining markers (respective P values for OS, P = .004, P = .036, and P < .001; and EFS, P = .005, P = .032, and P < .001), whereas neither increased BAALC, increased MN1, nor EVI1 expression or the presence of FLT3ITD is no longer indicative of adverse OS and EFS in intermediate-risk AML. The multivariable survival analysis for CN AML is summarized in supplemental Table 6. When we control for the remaining prognostic markers, only CEBPADM and CD34 remain significant in CN AML.

Table 3

Multivariable survival analysis of the intermediate-risk group

VariableOS
EFS
Hazard ratioLowerUpperPHazard ratioLowerUpperP
FLT3ITD × NPM1 + − 1.23 0.79 1.92 .37 1.54 2.37 .05 
 −+ 0.73 0.43 1.25 .25 0.7 0.42 1.17 .17 
 ++ 1.09 0.66 1.79 .74 0.94 0.58 1.52 .81 
FLT3TKD 1.13 0.67 1.93 .64 0.99 0.6 1.63 .95 
N-RAS 0.99 0.59 1.68 .99 1.28 0.79 2.08 .32 
CEBPASM 0.99 0.35 2.81 .99 0.84 0.3 2.36 .75 
CEBPADM 0.21 0.1 0.44 < .001 0.26 0.14 0.51 < .001 
IDH1 1.09 0.67 1.79 .73 1.31 0.83 2.06 .25 
IDH2 0.66 0.4 1.1 .11 0.79 0.49 1.27 .32 
EVI1* 1.15 0.72 1.83 .56 1.3 0.83 2.04 .26 
BAALC  1.43 0.43 4.72 .56 1.07 0.35 3.28 .91 
CD34  5.09 1.73 15.01 < .001 4.47 1.62 12.32 < .001 
MN1  0.59 0.21 1.67 .32 0.7 0.25 1.95 .49 
ERG  4.13 1.01 16.86 .05 3.93 1.05 14.62 .04 
ABCB1  0.84 0.32 2.22 .72 0.67 0.26 1.68 .39 
BCL2  0.34 0.12 0.96 .04 0.42 0.15 1.15 .09 
WT1  0.68 0.28 1.64 .39 0.66 0.29 1.53 .34 
FLT3  0.75 0.28 .56 0.66 0.25 1.74 .41 
INDO1  0.7 0.3 1.61 .4 0.69 0.32 1.51 .36 
VariableOS
EFS
Hazard ratioLowerUpperPHazard ratioLowerUpperP
FLT3ITD × NPM1 + − 1.23 0.79 1.92 .37 1.54 2.37 .05 
 −+ 0.73 0.43 1.25 .25 0.7 0.42 1.17 .17 
 ++ 1.09 0.66 1.79 .74 0.94 0.58 1.52 .81 
FLT3TKD 1.13 0.67 1.93 .64 0.99 0.6 1.63 .95 
N-RAS 0.99 0.59 1.68 .99 1.28 0.79 2.08 .32 
CEBPASM 0.99 0.35 2.81 .99 0.84 0.3 2.36 .75 
CEBPADM 0.21 0.1 0.44 < .001 0.26 0.14 0.51 < .001 
IDH1 1.09 0.67 1.79 .73 1.31 0.83 2.06 .25 
IDH2 0.66 0.4 1.1 .11 0.79 0.49 1.27 .32 
EVI1* 1.15 0.72 1.83 .56 1.3 0.83 2.04 .26 
BAALC  1.43 0.43 4.72 .56 1.07 0.35 3.28 .91 
CD34  5.09 1.73 15.01 < .001 4.47 1.62 12.32 < .001 
MN1  0.59 0.21 1.67 .32 0.7 0.25 1.95 .49 
ERG  4.13 1.01 16.86 .05 3.93 1.05 14.62 .04 
ABCB1  0.84 0.32 2.22 .72 0.67 0.26 1.68 .39 
BCL2  0.34 0.12 0.96 .04 0.42 0.15 1.15 .09 
WT1  0.68 0.28 1.64 .39 0.66 0.29 1.53 .34 
FLT3  0.75 0.28 .56 0.66 0.25 1.74 .41 
INDO1  0.7 0.3 1.61 .4 0.69 0.32 1.51 .36 

Mutation present (or absent) groups denoted with (+) or (−). The reference category for binary mutation markers is mutation absent (−). The reference category for the combined aberration in FLT3ITD and NPM1 is both mutations absent.

*

EVI expression categorized with the reference category EVI < 1.15.

To investigate which minimal subset/combination of markers is sufficient for assessing prognosis, variable selection in Cox proportional hazards models was performed. The LASSO variable selection with an optimal value of 8.7 of the penalization parameter identified the following markers: CD34, CEBPADM, IDH2, BCL2, ERG, NPM1, EVI1, FLT3ITD, and INDO1. Estimated regression coefficients of the penalized Cox proportional hazards model for different values of the penalization parameter for OS are shown in supplemental Figure 2. The plot indicates that, among the considered series of markers, CD34 and CEBPADM play a predominant role in survival prognosis. The Akaike information criterion-based stepwise selection identified a similar set of markers (ie, CD34, CEBPADM, IDH2, BCL2, and ERG). The variables recognized as important by the recursive binary partitioning in the survival tree methodology were CD34, CEBPADM, and IDH2 (Figure 2). Similar results were obtained for EFS. The tree model is in accordance with the penalized Cox regression approach in that CD34 and CEBPADM were again identified as the most important predictors.

Figure 2

The survival tree model (OS). The tree shows the partitioning of the 318 intermediate-risk AML into 4 groups with more similar survival characteristics. Kaplan-Meier estimates of the survival curves for each of the groups attached at the bottom of the tree. Group I (n = 132) consists of patients with CD34 expression ≤ 0.398. Group II (n = 143) are patients with CD34 expression > 0.398, IDH2 wild type CEBPADM absent. Group III is characterized by CD34 expression > 0.398, IDH2 mutation present, and no CEBPADM. Group IV includes patients with CD34 expression > 0.398 and CEBPADM.

Figure 2

The survival tree model (OS). The tree shows the partitioning of the 318 intermediate-risk AML into 4 groups with more similar survival characteristics. Kaplan-Meier estimates of the survival curves for each of the groups attached at the bottom of the tree. Group I (n = 132) consists of patients with CD34 expression ≤ 0.398. Group II (n = 143) are patients with CD34 expression > 0.398, IDH2 wild type CEBPADM absent. Group III is characterized by CD34 expression > 0.398, IDH2 mutation present, and no CEBPADM. Group IV includes patients with CD34 expression > 0.398 and CEBPADM.

The survival tree model in Figure 2 naturally suggests stratification of the intermediate-risk AML into subgroups with more homogeneous survival characteristics. According to the model, the intermediate-risk group could be divided into 4 categories: (I) “low CD34” (defined as CD34 < 0.398); (II) “high CD34” (defined as CD34 > 0.398), IDH2 wild-type and CEBPADM absent; (III) “high CD34” (> 0.398), IDH2 mutated, and no CEBPADM; and (IV) “high CD34” (> 0.398) and CEBPADM. Of the 4 categories, the groups (I, III, and IV) have statistically indistinguishable survival characteristics (P value of a 2 df partial likelihood ratio test: 0.298 [OS] and 0.333 [EFS]). The 3 groups (I, III, and IV) together could be aggregated as the favorable intermediate-risk group (estimated OS and EFS at 60 months, 51.9% and 41.5%, respectively). In contrast, the estimated OS and EFS at 60 months in the group (II) is 14.9% and 8.3%, respectively, which indicates unfavorable prognosis. The latter group has been designated: poor intermediate-risk group. The survival characteristics in the proposed strata compared with the survival profile of the established cytogenetic prognostic stratification (as described in “Patients, cell samples, and molecular analyses”) is given in Figure 3. The difference in survival between favorable and intermediate favorable prognostic groups is not statistically significant (P value of 1 df likelihood ratio test: 0.153 [OS], 0.44 [EFS]). The survival characteristics between poor and poor intermediate group are significantly different (P value of 1 df likelihood ratio test: .012 for both OS and EFS).

Figure 3

Risk stratification of intermediate-risk AML. The left and right panels presents Kaplan-Meier survival curve estimates for the OS and EFS in 5 AML subsets. Black lines indicate survival curves for favorable (solid line), intermediate (dashed line), and unfavorable (dotted line) cytogenetic risk subgroups of AML as defined in “Patients, cell samples, and molecular analyses.” The red curve represents the poor intermediate group defined as CD34 expression > 0.398, no IDH2 mutation, and no CEBPADM. The green line represents the favorable intermediate group defined as (1) CD34 expression < 0.398, (2) CD34 expression > 0.398 and CEBPADM, or (3) CD34 expression > 0.398, no CEBPADM, and IDH2 mutant.

Figure 3

Risk stratification of intermediate-risk AML. The left and right panels presents Kaplan-Meier survival curve estimates for the OS and EFS in 5 AML subsets. Black lines indicate survival curves for favorable (solid line), intermediate (dashed line), and unfavorable (dotted line) cytogenetic risk subgroups of AML as defined in “Patients, cell samples, and molecular analyses.” The red curve represents the poor intermediate group defined as CD34 expression > 0.398, no IDH2 mutation, and no CEBPADM. The green line represents the favorable intermediate group defined as (1) CD34 expression < 0.398, (2) CD34 expression > 0.398 and CEBPADM, or (3) CD34 expression > 0.398, no CEBPADM, and IDH2 mutant.

AML is a group of neoplasms characterized by a variety of genetic and epigenetic aberrations and variable responses to therapy.1,2  The pretreatment karyotype of leukemic blasts is currently a key determinant for therapy decision-making in AML. Usually, the largest cytogenetic subclass of AML (ie, those patients with a normal karyotype and patients with prognostically noninformative cytogenetic aberrations) is categorized as intermediate risk. In recent years, a number of novel markers have been identified as putative classifiers for these AML patients. These markers include a wide variety of acquired mutations as well as expression changes in specific genes.

In previous studies, prognostic risk assessments were put forward based on various expression markers BAALC,24,25,53 ERG,26,27 MN1,28,29  and EVI1.21-23  These studies have postulated risk algorithms mainly for CN AML and included only few of the wide variety of mutations and expression markers. Studies addressing the relative importance of the various postulated mutations and expression markers are limited.54 

In this study, we investigated the role of a wide series of genomic biomarkers that included mutations in FLT3, CEBPA, NPM1, NRAS, IDH1, IDH2, and WT1 genes as well as high expression of EVI1, WT1, BCL2, ABCB1, BAALC, FLT3, CD34, INDO, ERG, and MN1 in the risk stratification of intermediate-risk AML. The results reveal particular associations between some of these markers that may strongly affect the collective use of these markers in risk assessment. For instance, we demonstrate an inverse association between NPM1 mutations and Affymetrix HGU133 Plus2.0-derived CD34 expression, as was shown by others.9,55,56  Importantly, relatively strong associations exist between expression levels of CD34, BAALC, MN1, ERG, and ABCB1. Consequently, expression values of all these markers inversely correlate with the presence of mutant NPM1. These interactions indicate that these markers will have similar value in risk stratification of AML and should therefore be taken into account when prognostic scores based on selected markers are constructed.

By univariable analyses, we confirmed the prognostic ability of previously established markers in intermediate-risk AML: CEBPADM and NPM1 mutations as indicators for favorable OS and EFS6-12  and FLT3ITD mutations as markers for poor response to therapy.3-5,57  High expression of BAALC, CD34, MN1, and ERG all express unfavorable prognostic value with regard to OS and EFS, which is in line with earlier publications.24-29,53  Importantly, expression of CD34 mRNA strongly associates with poor OS and EFS.

In multivariable analyses, CEBPADM independently predicts favorable outcome, whereas CD34 and ERG are independent predictors for inferior OS and EFS. ERG expression has emerged as a strong negative predictor in multivariate analyses previously54 ; however, in this model, CD34 expression is the strongest expression marker for poor outcome. By conducting a model selection in both Cox proportional hazards regression models and survival trees, it becomes evident that CEBPADM and CD34 expression stands out as the most prominent predictors for treatment outcome. Although the value of CD34 protein expression has been controversial,36 CD34 mRNA appears to be notably valuable in AML risk stratification.

Although stratification based on expression levels is challenging, the usage of standardized protocols and Affymetrix GeneChips may facilitate the implementation of gene expression level analyses. Indeed, because many laboratories currently use Affymetrix GeneChips, the results of these types of analyses may be relatively easily implemented.

We developed a simplified stratification rule of intermediate-risk AML, which identifies 2 distinctive groups of patients with survival characteristics being similar to the generally established favorable and poor risk cytogenetic subgroups, respectively. We acknowledge that the proposed stratification needs further validation in future studies and will probably be improved with new emerging knowledge. Nevertheless, the model presented here discloses several particularly interesting associations with respect to the hierarchy of the prognostic importance of a scale of molecular biomarkers and adds to the understanding of the heterogeneity of intermediate-risk AML.

The online version of this article contains a data supplement.

The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

This work was supported by the Dutch Cancer Society (Koningin Wilhelmina Fonds) and Center for Translational Molecular Medicine. This study was performed within the framework of the Center for Translational Molecular Medicine (Leukemia BioCHIP project, grant 03O-102).

Contribution: V.R. designed research, analyzed data, and wrote the paper; S.A., B.J.W., C.A.J.E., H.B.B., and R.D. performed research; W.L.J.v.P. analyzed data and wrote manuscript; B.L. designed research and wrote the paper; and P.J.M.V. designed and performed research, analyzed data, and wrote the manuscript.

Conflict-of-interest disclosure: R.D., B.L., and P.J.M.V. have declared ownership interests in Skyline Diagnostic, a start-up and spin-off company of Erasmus University Medical Center. The remaining authors declare no competing financial interests.

Correspondence: Peter J. M. Valk, Erasmus University Medical Center Rotterdam, Department of Hematology, Ee1391a, Dr Molewaterplein 50, 3015 GE Rotterdam Z-H, The Netherlands; e-mail: p.valk@erasmusmc.nl.

1
Marcucci
 
G
Haferlach
 
T
Dohner
 
H
Molecular genetics of adult acute myeloid leukemia: prognostic and therapeutic implications.
J Clin Oncol
2011
, vol. 
29
 
5
(pg. 
475
-
486
)
2
Burnett
 
A
Wetzler
 
M
Lowenberg
 
B
Therapeutic advances in acute myeloid leukemia.
J Clin Oncol
2011
, vol. 
29
 
5
(pg. 
487
-
494
)
3
Nakao
 
M
Yokota
 
S
Iwai
 
T
, et al. 
Internal tandem duplication of the flt3 gene found in acute myeloid leukemia.
Leukemia
1996
, vol. 
10
 
12
(pg. 
1911
-
1918
)
4
Gilliland
 
DG
Griffin
 
JD
The roles of FLT3 in hematopoiesis and leukemia.
Blood
2002
, vol. 
100
 
5
(pg. 
1532
-
1542
)
5
Levis
 
M
Small
 
D
FLT3: ITDoes matter in leukemia.
Leukemia
2003
, vol. 
17
 
9
(pg. 
1738
-
1752
)
6
Schnittger
 
S
Schoch
 
C
Kern
 
W
, et al. 
Nucleophosmin gene mutations are predictors of favorable prognosis in acute myelogenous leukemia with a normal karyotype.
Blood
2005
, vol. 
106
 
12
(pg. 
3733
-
3739
)
7
Dohner
 
K
Schlenk
 
RF
Habdank
 
M
, et al. 
Mutant nucleophosmin (NPM1) predicts favorable prognosis in younger adults with acute myeloid leukemia and normal cytogenetics: interaction with other gene mutations.
Blood
2005
, vol. 
106
 
12
(pg. 
3740
-
3746
)
8
Thiede
 
C
Koch
 
S
Creutzig
 
E
, et al. 
Prevalence and prognostic impact of NPM1 mutations in 1485 adult patients with acute myeloid leukemia (AML).
Blood
2006
, vol. 
107
 
10
(pg. 
4011
-
4020
)
9
Verhaak
 
RG
Goudswaard
 
CS
van Putten
 
W
, et al. 
Mutations in nucleophosmin NPM1 in acute myeloid leukemia (AML): association with other gene abnormalities and previously established gene expression signatures and their favorable prognostic significance.
Blood
2005
, vol. 
106
 
12
(pg. 
3747
-
3754
)
10
Preudhomme
 
C
Sagot
 
C
Boissel
 
N
, et al. 
Favorable prognostic significance of CEBPA mutations in patients with de novo acute myeloid leukemia: a study from the Acute Leukemia French Association (ALFA).
Blood
2002
, vol. 
100
 
8
(pg. 
2717
-
2723
)
11
Frohling
 
S
Schlenk
 
RF
Stolze
 
I
, et al. 
CEBPA mutations in younger adults with acute myeloid leukemia and normal cytogenetics: prognostic relevance and analysis of cooperating mutations.
J Clin Oncol
2004
, vol. 
22
 
4
(pg. 
624
-
633
)
12
van Waalwijk van Doorn-Khosrovani
 
SB
Erpelinck
 
C
Meijer
 
J
, et al. 
Biallelic mutations in the CEBPA gene and low CEBPA expression levels as prognostic markers in intermediate-risk AML.
Hematol J
2003
, vol. 
4
 
1
(pg. 
31
-
40
)
13
Dufour
 
A
Schneider
 
F
Metzeler
 
KH
, et al. 
Acute myeloid leukemia with biallelic CEBPA gene mutations and normal karyotype represents a distinct genetic entity associated with a favorable clinical outcome.
J Clin Oncol
2010
, vol. 
28
 
4
(pg. 
570
-
577
)
14
Green
 
CL
Koo
 
KK
Hills
 
RK
Burnett
 
AK
Linch
 
DC
Gale
 
RE
Prognostic significance of CEBPA mutations in a large cohort of younger adult patients with acute myeloid leukemia: impact of double CEBPA mutations and the interaction with FLT3 and NPM1 mutations.
J Clin Oncol
2010
, vol. 
28
 
16
(pg. 
2739
-
2747
)
15
Pabst
 
T
Eyholzer
 
M
Fos
 
J
Mueller
 
BU
Heterogeneity within AML with CEBPA mutations: only CEBPA double mutations, but not single CEBPA mutations, are associated with favourable prognosis.
Br J Cancer
2009
, vol. 
100
 
8
(pg. 
1343
-
1346
)
16
Wouters
 
BJ
Lowenberg
 
B
Erpelinck-Verschueren
 
CA
van Putten
 
WL
Valk
 
PJ
Delwel
 
R
Double CEBPA mutations, but not single CEBPA mutations, define a subgroup of acute myeloid leukemia with a distinctive gene expression profile that is uniquely associated with a favorable outcome.
Blood
2009
, vol. 
113
 
13
(pg. 
3088
-
3091
)
17
Abbas
 
S
Lugthart
 
S
Kavelaars
 
FG
, et al. 
Acquired mutations in the genes encoding IDH1 and IDH2 both are recurrent aberrations in acute myeloid leukemia: prevalence and prognostic value.
Blood
2010
, vol. 
116
 
12
(pg. 
2122
-
2126
)
18
Marcucci
 
G
Maharry
 
K
Wu
 
YZ
, et al. 
IDH1 and IDH2 gene mutations identify novel molecular subsets within de novo cytogenetically normal acute myeloid leukemia: a Cancer and Leukemia Group B study.
J Clin Oncol
2010
, vol. 
28
 
14
(pg. 
2348
-
2355
)
19
Mardis
 
ER
Ding
 
L
Dooling
 
DJ
, et al. 
Recurring mutations found by sequencing an acute myeloid leukemia genome.
N Engl J Med
2009
, vol. 
361
 
11
(pg. 
1058
-
1066
)
20
Ward
 
PS
Patel
 
J
Wise
 
DR
, et al. 
The common feature of leukemia-associated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting alpha-ketoglutarate to 2-hydroxyglutarate.
Cancer Cell
2010
, vol. 
17
 
3
(pg. 
225
-
234
)
21
Barjesteh van Waalwijk van Doorn-Khosrovani
 
S
Erpelinck
 
C
van Putten
 
WL
, et al. 
High EVI1 expression predicts poor survival in acute myeloid leukemia: a study of 319 de novo AML patients.
Blood
2003
, vol. 
101
 
3
(pg. 
837
-
845
)
22
Lugthart
 
S
Drunen
 
EV
Norden
 
YV
, et al. 
High EVI1 levels predict adverse outcome in acute myeloid leukemia: prevalence of EVI1 overexpression and chromosome 3q26 abnormalities underestimated.
Blood
2008
, vol. 
111
 
8
(pg. 
4329
-
4337
)
23
Groschel
 
S
Lugthart
 
S
Schlenk
 
RF
, et al. 
High EVI1 expression predicts outcome in younger adult patients with acute myeloid leukemia and is associated with distinct cytogenetic abnormalities.
J Clin Oncol
2010
, vol. 
28
 
12
(pg. 
2101
-
2107
)
24
Baldus
 
CD
Tanner
 
SM
Ruppert
 
AS
, et al. 
BAALC expression predicts clinical outcome of de novo acute myeloid leukemia patients with normal cytogenetics: a Cancer and Leukemia Group B Study.
Blood
2003
, vol. 
102
 
5
(pg. 
1613
-
1618
)
25
Baldus
 
CD
Thiede
 
C
Soucek
 
S
Bloomfield
 
CD
Thiel
 
E
Ehninger
 
G
BAALC expression and FLT3 internal tandem duplication mutations in acute myeloid leukemia patients with normal cytogenetics: prognostic implications.
J Clin Oncol
2006
, vol. 
24
 
5
(pg. 
790
-
797
)
26
Marcucci
 
G
Baldus
 
CD
Ruppert
 
AS
, et al. 
Overexpression of the ETS-related gene, ERG, predicts a worse outcome in acute myeloid leukemia with normal karyotype: a Cancer and Leukemia Group B study.
J Clin Oncol
2005
, vol. 
23
 
36
(pg. 
9234
-
9242
)
27
Marcucci
 
G
Maharry
 
K
Whitman
 
SP
, et al. 
High expression levels of the ETS-related gene, ERG, predict adverse outcome and improve molecular risk-based classification of cytogenetically normal acute myeloid leukemia: a Cancer and Leukemia Group B Study.
J Clin Oncol
2007
, vol. 
25
 
22
(pg. 
3337
-
3343
)
28
Heuser
 
M
Beutel
 
G
Krauter
 
J
, et al. 
High meningioma 1 (MN1) expression as a predictor for poor outcome in acute myeloid leukemia with normal cytogenetics.
Blood
2006
, vol. 
108
 
12
(pg. 
3898
-
3905
)
29
Langer
 
C
Marcucci
 
G
Holland
 
KB
, et al. 
Prognostic importance of MN1 transcript levels, and biologic insights from MN1-associated gene and microRNA expression signatures in cytogenetically normal acute myeloid leukemia: a Cancer and Leukemia Group B study.
J Clin Oncol
2009
, vol. 
27
 
19
(pg. 
3198
-
3204
)
30
Bergmann
 
L
Miething
 
C
Maurer
 
U
, et al. 
High levels of Wilms' tumor gene (wt1) mRNA in acute myeloid leukemias are associated with a worse long-term outcome.
Blood
1997
, vol. 
90
 
3
(pg. 
1217
-
1225
)
31
Garg
 
M
Moore
 
H
Tobal
 
K
Liu Yin
 
JA
Prognostic significance of quantitative analysis of WT1 gene transcripts by competitive reverse transcription polymerase chain reaction in acute leukaemia.
Br J Haematol
2003
, vol. 
123
 
1
(pg. 
49
-
59
)
32
Barragan
 
E
Cervera
 
J
Bolufer
 
P
, et al. 
Prognostic implications of Wilms' tumor gene (WT1) expression in patients with de novo acute myeloid leukemia.
Haematologica
2004
, vol. 
89
 
8
(pg. 
926
-
933
)
33
Yanada
 
M
Terakura
 
S
Yokozawa
 
T
, et al. 
Multiplex real-time RT-PCR for prospective evaluation of WT1 and fusion gene transcripts in newly diagnosed de novo acute myeloid leukemia.
Leuk Lymphoma
2004
, vol. 
45
 
9
(pg. 
1803
-
1808
)
34
Karakas
 
T
Maurer
 
U
Weidmann
 
E
Miething
 
CC
Hoelzer
 
D
Bergmann
 
L
High expression of bcl-2 mRNA as a determinant of poor prognosis in acute myeloid leukemia.
Ann Oncol
1998
, vol. 
9
 
2
(pg. 
159
-
165
)
35
Chamuleau
 
ME
van de Loosdrecht
 
AA
Hess
 
CJ
, et al. 
High INDO (indoleamine 2,3-dioxygenase) mRNA level in blasts of acute myeloid leukemic patients predicts poor clinical outcome.
Haematologica
2008
, vol. 
93
 
12
(pg. 
1894
-
1898
)
36
Kanda
 
Y
Hamaki
 
T
Yamamoto
 
R
, et al. 
The clinical significance of CD34 expression in response to therapy of patients with acute myeloid leukemia: an overview of 2483 patients from 22 studies.
Cancer
2000
, vol. 
88
 
11
(pg. 
2529
-
2533
)
37
van den Heuvel-Eibrink
 
MM
van der Holt
 
B
te Boekhorst
 
PA
, et al. 
MDR 1 expression is an independent prognostic factor for response and survival in de novo acute myeloid leukaemia.
Br J Haematol
1997
, vol. 
99
 
1
(pg. 
76
-
83
)
38
Ozeki
 
K
Kiyoi
 
H
Hirose
 
Y
, et al. 
Biologic and clinical significance of the FLT3 transcript level in acute myeloid leukemia.
Blood
2004
, vol. 
103
 
5
(pg. 
1901
-
1908
)
39
Lowenberg
 
B
Boogaerts
 
MA
Daenen
 
SM
, et al. 
Value of different modalities of granulocyte-macrophage colony-stimulating factor applied during or after induction therapy of acute myeloid leukemia.
J Clin Oncol
1997
, vol. 
15
 
12
(pg. 
3496
-
3506
)
40
Lowenberg
 
B
van Putten
 
W
Theobald
 
M
, et al. 
Effect of priming with granulocyte colony-stimulating factor on the outcome of chemotherapy for acute myeloid leukemia.
N Engl J Med
2003
, vol. 
349
 
8
(pg. 
743
-
752
)
41
Ossenkoppele
 
GJ
Graveland
 
WJ
Sonneveld
 
P
, et al. 
The value of fludarabine in addition to ARA-C and G-CSF in the treatment of patients with high-risk myelodysplastic syndromes and AML in elderly patients.
Blood
2004
, vol. 
103
 
8
(pg. 
2908
-
2913
)
42
Lugthart
 
S
Groschel
 
S
Beverloo
 
HB
, et al. 
Clinical, molecular, and prognostic significance of WHO type inv(3)(q21q26.2)/t(3;3)(q21; q26.2) and various other 3q abnormalities in acute myeloid leukemia.
J Clin Oncol
2010
, vol. 
28
 
24
(pg. 
3890
-
3898
)
43
Damm
 
F
Heuser
 
M
Morgan
 
M
, et al. 
Integrative prognostic risk score in acute myeloid leukemia with normal karyotype.
Blood
2011
, vol. 
117
 
17
(pg. 
4561
-
4568
)
44
Taskesen
 
E
Bullinger
 
L
Corbacioglu
 
A
, et al. 
Prognostic impact, concurrent genetic mutations, and gene expression features of AML with CEBPA mutations in a cohort of 1182 cytogenetically normal AML patients: further evidence for CEBPA double mutant AML as a distinctive disease entity.
Blood
2011
, vol. 
117
 
8
(pg. 
2469
-
2475
)
45
Valk
 
PJ
Verhaak
 
RG
Beijen
 
MA
, et al. 
Prognostically useful gene-expression profiles in acute myeloid leukemia.
N Engl J Med
2004
, vol. 
350
 
16
(pg. 
1617
-
1628
)
46
Breems
 
DA
Van Putten
 
WL
De Greef
 
GE
, et al. 
Monosomal karyotype in acute myeloid leukemia: a better indicator of poor prognosis than a complex karyotype.
J Clin Oncol
2008
, vol. 
26
 
29
(pg. 
4791
-
4797
)
47
Valk
 
PJM
Bowen
 
DT
Frew
 
ME
Goodeve
 
AC
Löwenberg
 
B
Reilly
 
JT
Second hit mutations in the RTK/RAS signalling pathway in acute myeloid leukaemia and inv(16).
Haematologica
2004
, vol. 
89
 
1
pg. 
106
 
48
Care
 
RS
Valk
 
PJ
Goodeve
 
AC
, et al. 
Incidence and prognosis of c-KIT and FLT3 mutations in core binding factor (CBF) acute myeloid leukaemias.
Br J Haematol
2003
, vol. 
121
 
5
(pg. 
775
-
777
)
49
Verhaak
 
RG
Wouters
 
BJ
Erpelinck
 
CA
, et al. 
Prediction of molecular subtypes in acute myeloid leukemia based on gene expression profiling.
Haematologica
2009
, vol. 
94
 
1
(pg. 
131
-
134
)
50
Tibshirani
 
R
The lasso method for variable selection in the Cox model.
Stat Med
1997
, vol. 
16
 
4
(pg. 
385
-
395
)
51
Goeman
 
JJ
L1 penalized estimation in the Cox proportional hazards model.
Biom J
2010
, vol. 
52
 
1
(pg. 
70
-
84
)
52
Hothorn
 
T
Hornik
 
K
Zeileis
 
A
Unbiased recursive partitioning: a conditional inference framework.
J Comput Graph Statist
2006
, vol. 
15
 (pg. 
651
-
674
)
53
Langer
 
C
Radmacher
 
MD
Ruppert
 
AS
, et al. 
High BAALC expression associates with other molecular prognostic markers, poor outcome, and a distinct gene-expression signature in cytogenetically normal patients younger than 60 years with acute myeloid leukemia: a Cancer and Leukemia Group B (CALGB) study.
Blood
2008
, vol. 
111
 
11
(pg. 
5371
-
5379
)
54
Metzeler
 
KH
Dufour
 
A
Benthaus
 
T
, et al. 
ERG expression is an independent prognostic factor and allows refined risk stratification in cytogenetically normal acute myeloid leukemia: a comprehensive analysis of ERG, MN1, and BAALC transcript levels using oligonucleotide microarrays.
J Clin Oncol
2009
, vol. 
27
 
30
(pg. 
5031
-
5038
)
55
Falini
 
B
Mecucci
 
C
Tiacci
 
E
, et al. 
Cytoplasmic nucleophosmin in acute myelogenous leukemia with a normal karyotype.
N Engl J Med
2005
, vol. 
352
 
3
(pg. 
254
-
266
)
56
Cazzaniga
 
G
Dell'oro
 
MG
Mecucci
 
C
, et al. 
Nucleophosmin mutations in childhood acute myelogenous leukemia with normal karyotype.
Blood
2005
, vol. 
106
 
4
(pg. 
1419
-
1422
)
57
Frohling
 
S
Schlenk
 
RF
Breitruck
 
J
, et al. 
Prognostic significance of activating FLT3 mutations in younger adults (16 to 60 years) with acute myeloid leukemia and normal cytogenetics: a study of the AML Study Group Ulm.
Blood
2002
, vol. 
100
 
13
(pg. 
4372
-
4380
)

Author notes

*

B.L. and P.J.M.V. contributed equally to this study as co-senior authors.