Abstract

Acute myeloid leukemia (AML) is a highly diverse disease characterized by various cytogenetic and molecular abnormalities. MicroRNAs are small noncoding RNAs that show variable expression during myeloid differentiation. MicroRNA expression in marrow blasts in 215 cases of newly diagnosed and (cyto)genetically defined AML was assessed using quantitative reverse-transcription–polymerase chain reaction (RT-PCR) for 260 human microRNAs. In the same series, mRNA gene expression profiles were established, allowing a direct comparison between microRNA and mRNA expression. We show that microRNA expression profiling following unsupervised analysis reveals distinctive microRNA signatures that correlate with cytogenetic and molecular subtypes of AML (ie, AMLs with t(8;21), t(15;17), inv(16), NPM1, and CEBPA mutations). Significantly differentially expressed microRNAs for genetic subtypes of AML were identified. Specific microRNAs with established oncogenic and tumor suppressor functions, such as microRNA-155, microRNA-21, and let-7, appear to be associated with particular subtypes. Combinations of selected sets of microRNAs could predict cytogenetically normal AML with mutations in the genes of NPM1 and CEBPA and FLT3-ITD with similar accuracy as mRNA probe set combinations defined by gene expression profiling. MicroRNA expression apparently bears specific relationships to the heterogeneous pathobiology of AML. Distinctive microRNA signatures appear of potential value in the clinical diagnosis of AML.

Introduction

The pathogenesis of acute myeloid leukemia (AML) is a heterogeneous multistep process affecting cell differentiation, proliferation, and apoptosis, which ultimately leads to malignant transformation of hematopoietic progenitors. Deregulated gene expression, disrupting cellular pathways, has been used for the classification of AML.1-5  The prognosis of AML depends on well-defined leukemia-specific prognostic factors, such as the cytogenetic abnormalities t(15;17), t(8;21), and inv(16) with a relatively favorable prognosis and the 3q26 abnormalities, −5/−5q and −7/−7q with an unfavorable prognosis.6-8  Furthermore, various molecular abnormalities in AML with normal karyotype have apparent prognostic significance, such as the somatic gene mutations in nucleophosmin-1 (NPM1), FMS-like tyrosine kinase 3 (FLT3, internal tandem duplications [ITDs]), and CCAAT/enhancer binding protein alpha (CEBPA).9-13 NPM1 mutations frequently occur in association with FLT3-ITD mutations. Several studies on the clinical impact in AML subgroups revealed that the subset of AML with NPM1 mutations lacking FLT3-ITD mutations has a significantly better overall survival.14 

MicroRNAs are a class of small noncoding RNAs that regulate translation of protein coding mRNAs and thereby protein expression, by translation inhibition or cleavage of the mRNA transcripts.15  There is an accumulating body of evidence indicating that microRNAs play important roles in cellular growth and differentiation.16  MicroRNA expression profiles of tumor samples have recently been shown to provide phenotypic signatures of particular cancer types.17-22  MicroRNAs can act as tumor suppressor. For instance, expression of some microRNAs, such as let-723  and the microRNA15a/16-1 cluster,24  has been reported to be reduced in lung cancer and chronic lymphocytic leukemia (CLL) respectively, suggesting tumor suppressor activities. In contrast, microRNA-17-92 cluster25  and microRNA-155/BIC26,27  have been shown to be overexpressed in B-cell lymphomas, indicative of their oncogenic potential.

A characteristic microRNA expression signature may aid in the diagnosis of certain types or subtypes of cancers. It has been shown that microRNA profiles of bone marrow samples from patients with acute lymphoblastic leukemia (ALL) discriminated subsets of ALL with different molecular aberrancies.17 

In AML, information about microRNA expression has been gathered only in a limited series of patients so far. Debernardi et al28  reported in 30 AML patients with normal cytogenetics that microRNA-181a correlates with cytologic subclass. They also reported that the expression of microRNA-10a, microRNA-10b, and microRNA-196a, which are located in intergenic regions in the HOX gene clusters, in AML correlates positively with HOXA and HOXB gene expression, suggesting a role of these microRNAs in aberrant regulation of proliferation and differentiation in leukemogenesis.28  It was recently reported that microRNA expression signatures discriminate ALL from AML29  and AML from normal bone marrow CD34+ cells.30 

Here we addressed the question whether microRNA expression signatures could be used to classify a heterogeneous disease such as AML. We used a microRNA quantification method based on reverse-transcriptase (RT) reaction using stem-loop primers followed by real-time quantitative polymerase chain reaction (PCR) analysis31  for 260 known human microRNAs to study the expression profile in 215 clinically and molecularly well-characterized de novo AML bone marrow samples. Furthermore, we sought to identify microRNAs associated with known AML cytogenetic and molecular abnormalities. Finally, we set out to directly compare microRNA-based class predictors of AML subgroups with predictors identified by mRNAs GEP in the same patient cohort.

Methods

Patients and cell samples

Patients used in this study had a newly diagnosed AML determined by cytologic examination of bone marrow (Tables 1, 2). All patients provided written informed consent in accordance with the Declaration of Helsinki and were treated according to the protocols of the Dutch-Belgian-Hematology-Oncology-Cooperative group (available at http://www.hovon.nl).32-34  The ethics committee of Erasmus University Medical Center, Rotterdam, The Netherlands, approved the study. A total of 215 patients provided bone marrow aspirates at the time of diagnosis. Blast and mononuclear cells were purified by Ficoll-Hypaque (Nygaard, Oslo, Norway) centrifugation and cryopreserved. After thawing, the AML samples contained 80% to 100% blast cells, regardless of the blast count at diagnosis. CD34+ cells from 4 healthy control subjects were sorted from bone marrow aspirates using a fluorescence-activated cell sorter.

Table 1

Clinical characteristics of the cohort of 215 patients with newly diagnosed AML

Characteristic Median (range) No. of cases (%) 
Sex 
    Male  117 (54.4) 
    Female  98 (45.6) 
    Total  215 (100.0) 
Age 49.7 y (18-77.2 y)  
    Younger than 35 y  45 (20.9) 
    35 to 60 y  118 (54.9) 
    Older than 60 y  52 (24.2) 
    Total  215 (100.0) 
White cell count, ×109/L 33 (0.3-510)  
Marrow blasts, % 60 (0-93)  
Platelet count, ×109/L 64.5 (8-931)  
French-American-British classification 
    M0  6 (2.8) 
    M1  47 (21.9) 
    M2  55 (25.6) 
    M3  6 (2.8) 
    M4  37 (17.2) 
    M5  44 (20.5) 
    M6  2 (0.9) 
    Not determined  18 (8.4) 
    Total  215 (100.0) 
Characteristic Median (range) No. of cases (%) 
Sex 
    Male  117 (54.4) 
    Female  98 (45.6) 
    Total  215 (100.0) 
Age 49.7 y (18-77.2 y)  
    Younger than 35 y  45 (20.9) 
    35 to 60 y  118 (54.9) 
    Older than 60 y  52 (24.2) 
    Total  215 (100.0) 
White cell count, ×109/L 33 (0.3-510)  
Marrow blasts, % 60 (0-93)  
Platelet count, ×109/L 64.5 (8-931)  
French-American-British classification 
    M0  6 (2.8) 
    M1  47 (21.9) 
    M2  55 (25.6) 
    M3  6 (2.8) 
    M4  37 (17.2) 
    M5  44 (20.5) 
    M6  2 (0.9) 
    Not determined  18 (8.4) 
    Total  215 (100.0) 
Table 2

Cytogenetic and molecular characteristics of the cohort of 215 patients with newly diagnosed AML

Characteristic No. of cases 
Cytogenetic abnormalities 
    −9q 2.3 
     −8 11 5.1 
    11q23 2.8 
     −5/−5(q)/−7/−7(q) 16 7.4 
    Complex 2.3 
    Inv(16) 15 7.0 
    Normal karyotype (NK) 100 46.5 
    t(15;17) 2.8 
    t(6;9) 0.9 
    t(8;21) 12 5.6 
    t(9;22) 0.9 
     −X/Y 1.4 
    Other 30 14.0 
    Not determined 0.9 
    Total 215 100.0 
Molecular abnormalities 
    FLT3-TKD 20 9.3 
    FLT3-ITD 62 28.8 
    N-RAS 22 10.2 
    K-RAS 0.5 
    CEBPA 22 10.2 
    NPM1 68 31.6 
Total patients 215 100.0 
Normal karyotype 100 100.0 
    With FLT3-TKD 10 10.0 
    With FLT3-ITD 45 45.0 
    With N-RAS 11 11.0 
    With K-RAS 0.0 
    With CEBPA 19 19.0 
    With NPM1 55 55.0 
Characteristic No. of cases 
Cytogenetic abnormalities 
    −9q 2.3 
     −8 11 5.1 
    11q23 2.8 
     −5/−5(q)/−7/−7(q) 16 7.4 
    Complex 2.3 
    Inv(16) 15 7.0 
    Normal karyotype (NK) 100 46.5 
    t(15;17) 2.8 
    t(6;9) 0.9 
    t(8;21) 12 5.6 
    t(9;22) 0.9 
     −X/Y 1.4 
    Other 30 14.0 
    Not determined 0.9 
    Total 215 100.0 
Molecular abnormalities 
    FLT3-TKD 20 9.3 
    FLT3-ITD 62 28.8 
    N-RAS 22 10.2 
    K-RAS 0.5 
    CEBPA 22 10.2 
    NPM1 68 31.6 
Total patients 215 100.0 
Normal karyotype 100 100.0 
    With FLT3-TKD 10 10.0 
    With FLT3-ITD 45 45.0 
    With N-RAS 11 11.0 
    With K-RAS 0.0 
    With CEBPA 19 19.0 
    With NPM1 55 55.0 

RNA isolation and quality control of RNA

After thawing, total RNA was isolated from cells using Trizol reagent according to manufacturer's protocol (Invitrogen, Breda, The Netherlands). Quality control of RNA was done using RNA 6000 Nano assay on the Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA). All RNA samples showed high quality (without RNA degradation or DNA contamination).

Mutation analyses

Sequence analyses for FLT3-ITD, FTL3-TKD, NPM1, and CEBPA mutations were performed as described previously.13,35 

MicroRNA profiling using multiplex real-time quantitative RT-PCR

A multiplexing RT-PCR method was used for the detection of multiple microRNAs.36  MicroRNA-specific RT primers for 260 mature human microRNAs including several small nuclear RNAs (RNU19, RNU24, RNU38B, RNU43, RNU44, RNU48, RNU49, and RNU66) that were used for internal normalization were divided into 7 pools containing 32 to 47 primers per pool (Applied Biosystems, Nieuwerkerk a/d IJssel, The Netherlands) and used for multiplexing RT reaction. RT reactions of 10 μL contained: 1 μL 10× RT buffer (Applied Biosystems), 0.2 μL dNTPs (100 mM each), 1 μL MultiScribe Reverse Transcriptase (50 U/μL), 0.125 μL AB RNase Inhibitor (20 U/μL), 2 μL 5× multiplex RT primers, 2 μL of 50 ng/μL total RNA, and 3.675 μL H2O. Reactions were incubated in a Primus HT Thermocycler (MWY Biotech, High Point, NC) in a 96-well plate for 30 minutes at 16°C, 30 minutes at 42°C, 5 minutes at 85°C, and then held at 4°C.

Single real-time quantitative PCR reactions of 10 μL were set up in 384-well plates for each of the 260 microRNAs. Real-time quantitative PCR reactions contained 2.5 μL 4× Universal Master Mix (Applied Biosystems), 1.6 μL 20-fold diluted multiplex RT product, 2.5 μL 5-fold H2O diluted 20× Taqman MicroRNA primer/probe Assay (Applied Biosystems), and 3.4 μL H2O. Caliper Sciclone ALH3000 pipetting robot (Caliper LS, Mountain View, CA) was used to dispense 2.5 μL primer/probe assays into 384-well plates. This facility was kindly provided by A.G. Uitterlinden (Internal Medicine, Erasmus MC Rotterdam, The Netherlands). Real-time quantitative RT-PCR was performed on an ABI 7900 HT Sequence Detection System (SDS; Applied Biosystems) in a 384-well plate format, under following conditions: first a hot start of 95°C for 10 minutes, followed by 40 cycles of 95°C for 15 seconds, and 60°C for 1 minute. A Twister robot (Applied Biosystems) was used to load 384-well plates automatically into the 7900 HT SDS. Reproducibility was tested studying microRNA expression in 10 AML samples in duplicate. RNA was isolated in 2 independent experiments, and subsequently cDNA synthesis and real-time RT-PCR steps were also performed in 2 separate experiments. High correlation in microRNA expression between 2 independent experiments was found, with correlation coefficient R of 0.97 (Figure S1, available on the Blood website; see the Supplemental Materials link at the top of the online article).

Normalization and filtering

SDS 2.3 software (Applied Biosystems) was used to analyze real-time RT-PCR data.

The geometric mean of RNU24, RNU43, RNU48, and RNU66 was used for internal normalization37  since these RNUs showed the most consistent expression in our cohort (Figure S2). The relative quantification method, 2−dCt was used to calculate the expression relative to the mean of the RNUs.38  The relative expression (2−dCt) of the microRNAs was normalized with the geometric mean of each microRNA for all patients. To obtain a normal linear distribution, a log2 transformation was performed. MicroRNAs that were unreliably quantifiable or not expressed (Ct value of 35 or higher) or not differentially expressed as defined by interquartile range less than 0.1 were excluded from further analysis, leaving a set of 178 microRNAs.

mRNA gene expression profiling

A total of 215 AML cases were analyzed for mRNA gene expression profiling (GEP) using Affymetrix Human Genome U133 Plus 2.0 GeneChips (Affymetrix, Santa Clara, CA) as described previously.2  Gene expression data are available at http://www.ncbi.nlm.nih.gov/geo/ as accession GSE6891.39 

Unsupervised cluster analyses

We used the unsupervised cluster analyses as described previously.3  A total of 215 AML patient bone marrow specimens were grouped based on Pearson correlation of the microRNA expression using the Correlation View visualization tool of the OmniViz package version 4.0.0 (Omniviz, Maynard, MA). The groups of patients were ordered based on absolute correlation. Clinical and molecular data were projected next to the correlation plot using the software program Heatmapper (http://www1.erasmusmc.nl/hematologie//heatmapper/, Erasmus University Medical Center).40 

Significance analysis of microarrays

All supervised analyses were done using significance analysis of microarray (SAM) with the statistical program R version 2.4.1 with the Bioconductor package version 2.0 implemented (http://www.bioconductor.org/, Bioconductor).41 

The differentially expressed microRNAs were identified by comparing specimens with specific cytogenetic (eg, t(15;17), t(8;21), inv(16)) or molecular abnormalities (NPM1, CEBPA and FLT3-ITD mutations) with all remaining AML patients in the cohort using SAM with a threshold of minimal 2-fold change in expression and a false discovery rate (FDR) of 0%.

Prediction analysis of microarrays

Supervised class prediction of AML with cytogenetic abnormalities, such as t(15;17), t(8;21), and inv(16) and molecular aberrations such as NPM1, CEBPA, and FLT3-ITD mutations, using microRNAs and mRNA expression data were performed using prediction analysis of microarrays (PAM).42  The total cohort of 215 different AML patients as described in Table 1 was randomly divided into a training set of 143 patients to build the class predictor and a validation set of 72 patients to test the predictor independent from the training set. The 10-fold method of cross-validation was applied to assess the class predictor in the training set. The minimum numbers of microRNAs and probe sets of genes (mRNA) that were identified were subsequently tested on the validation set. The accuracy of the specific class predictor was described by the number of truly predicted and false positively predicted cases in the validation set.

Results

Unsupervised analysis of microRNA expression reveals signatures that correlate with particular cytogenetic and molecular subgroups of AML

Using an approach of unsupervised ordering (ie, a priori not taking account of morphologic subtype, cytogenetics, molecular abnormalities, or other external information), 215 cases of AML were classified into the subgroups with similar expression patterns of microRNAs. The correlation view of Figure 1 (right section) shows the clusters with distinctive microRNA signatures. Figure 1 (left section) visualizes the distinct signatures of up- and down-regulated microRNAs. Certain microRNA expression patterns correlated with specific cytogenetic or molecular abnormalities (also shown in Figure 1). Thus microRNA clustering appears representative of particular (cyto)genetic subtypes of leukemia. The microRNA clustering that was evident from unsupervised clustering was frequently driven by a selected number of microRNAs. Detailed information about cluster distribution and clinical, cytogenetic, and molecular data is presented in Table S1.

Figure 1

Correlation view based on unsupervised clustering of 215 AML specimens involving 178 differentially expressed mature human microRNAs (right section) and the relative expression levels of microRNAs that characterize each of the individual clusters (left section). In the Pearson correlation view, the red squares indicate a positive correlation and the blue squares a negative pairwise correlation between the microRNA expression signatures. Using the program Heatmapper,40  cytologic, cytogenetic, and molecular data were plotted along the diagonal. Cytogenetic abnormalities and FAB classifications are indicated with different colors. The presence of molecular abnormalities, such as NPM1, CEBPA, FLT3-ITD, and FLT3-TKD mutations, is indicated in red and absence in green. On the left part of the figure, each individual microRNA expression cluster with a particular (cyto)genetic AML subtype is indicated (subtypes with [cyto]genetic abnormality). The total number of cases in each cluster and percentage of (cyto)genetic subtype of AML in cluster are presented in the columns. In addition, the percentages of these (cyto)genetic subtypes present within each microRNA expression cluster (thus also indicating relative proportion of cases with the abnormality that are located outside the cluster) is given (% of total [cyto]genetic subtype). The levels of expression of 178 microRNAs of each of the 22 clusters including normal bone marrow CD34+ cells are presented in the heatmap on the left side of the correlation view. The scale bar indicates an increase (red) or decrease (green) in the level of expression by a factor of 7 relative to the geometric mean of all samples.

Figure 1

Correlation view based on unsupervised clustering of 215 AML specimens involving 178 differentially expressed mature human microRNAs (right section) and the relative expression levels of microRNAs that characterize each of the individual clusters (left section). In the Pearson correlation view, the red squares indicate a positive correlation and the blue squares a negative pairwise correlation between the microRNA expression signatures. Using the program Heatmapper,40  cytologic, cytogenetic, and molecular data were plotted along the diagonal. Cytogenetic abnormalities and FAB classifications are indicated with different colors. The presence of molecular abnormalities, such as NPM1, CEBPA, FLT3-ITD, and FLT3-TKD mutations, is indicated in red and absence in green. On the left part of the figure, each individual microRNA expression cluster with a particular (cyto)genetic AML subtype is indicated (subtypes with [cyto]genetic abnormality). The total number of cases in each cluster and percentage of (cyto)genetic subtype of AML in cluster are presented in the columns. In addition, the percentages of these (cyto)genetic subtypes present within each microRNA expression cluster (thus also indicating relative proportion of cases with the abnormality that are located outside the cluster) is given (% of total [cyto]genetic subtype). The levels of expression of 178 microRNAs of each of the 22 clusters including normal bone marrow CD34+ cells are presented in the heatmap on the left side of the correlation view. The scale bar indicates an increase (red) or decrease (green) in the level of expression by a factor of 7 relative to the geometric mean of all samples.

The 6 cases of AML with t(15;17) aggregated in cluster no. 16. One of the AML cases, which clustered together with specimens with t(15;17) in cluster no. 16, had not been reported to carry the t(15;17) However, a subsequent PCR analysis showed the presence of the PML-RARA fusion gene suggesting that the t(15;17) may have been missed by cytogenetic analysis due to cytogenetically cryptic fusion. MicroRNA signature of cluster no. 16 signature was characterized by a set of strongly up-regulated microRNAs (microRNA-382, -134, -376a, -127, -299–5p, and -323). Most AMLs with inv(16) were found in microRNA expression cluster no. 10 (60%; 9/15), and the other 6 cases showed variable signatures scattered over various clusters. The majority (83%; 10/12 cases) of AML with t(8;21) shared the microRNA signature of cluster no. 7 that was characterized by mainly down-regulated microRNAs. Interestingly, some AML cases carrying a inv(16) clustered into the cluster characterized by t(8;21) and vice versa. A similar global down-regulation of microRNAs was evident in microRNA expression cluster no. 8 that contained AMLs with CEBPA mutations. Three of 6 AMLs with 11q23 chromosomal abnormalities shared the microRNA expression profile of cluster no. 18. The microRNA expression profiles of all AMLs with NPM1 mutations (n = 68 cases) were largely restricted to the 6 clusters (no. 2, no. 4, no. 6, no. 9, no. 11, and no. 13) that appeared following unsupervised analysis. Clustering in these instances was determined mainly by a limited set of overexpressed microRNAs (microRNA-10a, -10b, -196a, and -196b). No characteristic clusters of either AMLs with FLT3-ITD or FLT3-TKD mutations appeared following unsupervised analysis nor were they apparent for the subsets of AMLs with dual FLT3-ITD and NPM1 mutations. Several AMLs aggregated together in clusters no. 1, no. 14, and no. 15 and thus shared 3 characteristic microRNA expression signatures. However no currently known cytogenetic or known molecular abnormalities were associated with these clusters. Normal bone marrow CD34+ specimens revealed a specific microRNA signature (cluster no. 20).

Significantly differentially expressed microRNAs in relation to cytogenetic translocations and molecular abnormalities

To identify the most significantly differentially expressed microRNAs that are associated with specific subtypes of AML, we performed a supervised analysis using SAM. MicroRNA expression for each of the cytogenetic (t(15;17), t(8;21), inv(16)) and molecular (NPM1, CEBPA, and FLT3-ITD mutations) subtypes was compared with the microRNA expression patterns of the remaining AMLs of the cohort. Several discriminating microRNAs were identified for each subtype. None of these microRNAs was localized in the chromosomal region specific for the corresponding cytogenetic translocation such as t(15;17), t(8;21), and inv(16). The detailed data on SAM are presented in the Table S2.

Class prediction of subgroups of AML with cytogenetic or molecular abnormalities by microRNA expression profiling, GEP (mRNA), or the combination of both methods

Using PAM we determined the minimal set of microRNAs and transcripts (mRNA) that could predict a particular (cyto)genetic subtype of AML in the cohort. Furthermore, we performed PAM by combining microRNA and mRNA expression data, to investigate whether microRNAs would improve the accuracy of the prediction. The number of correctly predicted (true positive) and number of false-positive cases in the 10-fold cross validation of the training set for individual subtypes as well as the numbers of microRNAs and transcript probe sets (mRNA) in the class predictors are presented in Table 3 and Table S3.

Table 3

microRNA and mRNA class predictors for subsets of AML with (cyto)genetic abnormalities identified using prediction analysis of microarrays (PAM)

(Cyto)genetic subset Training set, N = 143
 
Validation set, N = 72
 
Class predictor No. microRNAs 
No. of cases True prediction False positive No. of cases True prediction False positive 
microRNA class predictor 
    t(15;17) 
    t(8;21) 10 
    Inv(16) 10 72 
    NPM1 47 46 21 21 20 10 
    CEBPA 17 28 
    FLT3-ITD 46 33 22 16 14 48 
 No. of cases True prediction False positive No. of cases True prediction False positive No. probe sets 
mRNA class predictor 
    t(15;17) 
    t(8;21) 
    Inv(16) 
    NPM1 47 23 11 21 20 
    CEBPA 17 13 20 
    FLT3-ITD 46 31 16 13 36 
(Cyto)genetic subset Training set, N = 143
 
Validation set, N = 72
 
Class predictor No. microRNAs 
No. of cases True prediction False positive No. of cases True prediction False positive 
microRNA class predictor 
    t(15;17) 
    t(8;21) 10 
    Inv(16) 10 72 
    NPM1 47 46 21 21 20 10 
    CEBPA 17 28 
    FLT3-ITD 46 33 22 16 14 48 
 No. of cases True prediction False positive No. of cases True prediction False positive No. probe sets 
mRNA class predictor 
    t(15;17) 
    t(8;21) 
    Inv(16) 
    NPM1 47 23 11 21 20 
    CEBPA 17 13 20 
    FLT3-ITD 46 31 16 13 36 

PAM was performed to define microRNA and mRNA class predictors containing a minimal number of microRNAs and probe sets (mRNAs), respectively, that could predict whether a case belonged to a particular (cyto)genetic subset (first column). The cohort was randomly segregated into a training set and a validation set. The number of cases in the training set (column 2) and the validation set (column 5) are given. The 10-fold method of cross-validation, applied on the training set was used to compute truly predicted cases and errors. The cases that were truly predicted (column 3) or were false positive (column 4) in the training set are indicated. The minimal number of microRNAs or probe sets (mRNAs) that were identified in the training set was subsequently tested on the validation set. The number of cases correctly predicted (ie, true prediction) in the validation set (column 6) or were false positively predicted (column 7) are shown. The characteristics of the microRNAs and probe sets (mRNAs) are presented in Table S3.

A class predictor of 10 microRNAs could predict AML with t(8;21) and a set of 7 microRNAs predicted AML with t(15;17). We constructed a predictor of 72 microRNAs for AML with inv(16). Class prediction of AML subgroups with cytogenetic abnormalities using mRNA-GEP was more accurate since there were no errors in the validation set and it required only 2 mRNA probe sets for t(8;21) and for inv(16) and 8 probe sets for t(15;17).

As regards AML with molecular aberrations, sets of microRNAs were derived that were characteristic for AML with NPM1, CEBPA, and FLT3-ITD mutations, respectively. Defined sets of 28 microRNAs could predict AML with CEBPA mutations, and 10 microRNAs were predictive of AML with NPM1 mutations. A selection of as many as 48 microRNAs was required to predict AML with FLT3-ITD mutations. MicroRNA- and mRNA-GEP–based class predictors of AML molecular subtypes showed comparable accuracies. MicroRNA-10a and microRNA-10b were included in the mixed microRNA-mRNA expression predictor of AML with NPM1 mutations (Table S3). In addition, 2 microRNAs (microRNA-155, microRNA-10b) were included in the combined microRNA-mRNA class predictor of AML with FLT3-ITD (Table S3). However, the combination did not improve the overall accuracy of prediction of AML with mutant NPM1 or mutant FLT3-ITD compared with the mRNA predictor alone.

Discussion

Our study demonstrates that the heterogeneity of AML can be resolved according to their variable microRNA expression signatures. We used quantitative real-time RT-PCR to study the expression of 260 known human microRNAs.

The cohort of 215 AML cases represents a cross section of a mixed diversity of clinical AML. Others43,44  and we3  have previously established that gene expression profiling can comprehensively classify the disorder. We now demonstrate that the expression profiling of a limited set of known human small noncoding microRNAs can also be used to identify subgroups with distinctive microRNA expression patterns, which notably correlate with particular cytogenetic and molecular AML entities.

The unsupervised clustering method revealed an association between the majority of the AMLs with t(8;21), t(15;17), inv(16), and characteristic microRNAs expression profiles (clusters no. 7, no. 16, no. 10) (Figure 1; Table S1). However, not all AMLs with the latter cytogenetic abnormalities clustered together. For example, several cases of AML with inv(16) and t(8;21) expressed microRNA patterns that were discrepant from the more common clusters no. 10 and no. 7. Interestingly, 2 cases with t(8;21) clustered together with AMLs with inv(16) and 1 case with inv(16) was found in the cluster no. 7 containing mainly cases with t(8;21). Another study44  had reported that some cases with inv(16) clustered together with cases with t(8;21) and vice versa using mRNA expression profiling. Whether these variations of microRNA and mRNA profiles among the latter cytogenetic subtypes are to be explained by the presence of other coexisting underlying genetic abnormalities in these not yet elucidated leukemias remains to be seen.

Using supervised analysis, we identified the microRNA signatures characteristic of AMLs with cytogenetic abnormalities: t(15;17), t(8;21), and inv(16). A prominent signature (microRNA-382, -134, -376a, -127, -299–5p, and -323), predominantly composed of up-regulated microRNAs, was identified for AML with t(15;17). Most of these microRNAs were found to be located on chromosome 14 and therefore appear not directly implicated in chromosomal translocation of AML with t(15;17) itself. The functions of the latter microRNAs in AML with t(15;17) are currently not known and the predicted targets are yet to be experimentally validated.

AML with t(8;21) was characterized mainly by a set of down-regulated microRNAs. We found 2 members of a known tumor suppressor microRNA family, let-7b and let-7c, to be down-regulated in AML with t(8;21) and also in AML with inv(16). Previous reports have linked let-7 microRNAs to human cancer23,45-48  showing that they regulate the expression of oncogenes RAS49  and HMGA2.45,47  Reduced expression of the let-7 in lung cancer is associated with poor prognosis.50  It is tempting to speculate that, similar to the gain of function of RAS due to gene mutations,51-53 RAS overexpression due to down-regulation of let-7 may be a cooperating event in the pathogenesis of AML with inv(16). Combining our microRNA and mRNA expression data sets, we did not find a significant negative correlation between the expression levels of let-7 and its targets RAS and HMGA. However, microRNAs regulate gene expression mainly by translational inhibition and to a lesser extent by degradation or cleavage of mRNA targets. Therefore, despite a lack of a negative correlation of microRNA and mRNA expression levels, we cannot exclude the possibility of down-regulation of let-7 in AML with inv(16) and t(8;21) resulting in increased protein levels of RAS and thereby contributing to malignant transformation in CBF leukemias. SAM also showed microRNA-127 to be significantly down-regulated in AML with inv(16). MicroRNA-127 has been reported to function as a tumor suppressor and may be silenced by hypermethylation in cancer models. Epigenetic activation of microRNA-127 results in a down-regulation of the BCL6 proto-oncogene.54  Thus altogether it is conceivable that let-7 and microRNA-127 would contribute to the pathobiology of core-binding factor leukemias with inv(16) and AML with t(8;21).

SAM also revealed differentially expressed microRNAs in certain molecular subgroups of AML. One prominent signature, predominantly containing up-regulated microRNA-10a, microRNA-10b, microRNA-196a, and microRNA-196b, was identified in AML carrying NPM1 mutations. NPM1 mutations are present in approximately 35% of patients with AML and are the most common molecular abnormality in patients with AML. They are especially prevalent in AML with a normal karyotype.14  It has been demonstrated that NPM1 mutations are associated with specific gene expression signatures.35,55-57  These transcript signatures are characterized by overexpression of homeobox genes (HOXA, HOXB families and TALE genes). Interestingly, the microRNAs (-10a, -10b, -196a, -196b) that are up-regulated in AML with mutant NPM1, are all located within the genomic cluster of HOX genes. Yekta et al have reported that microRNA-196a directs translational inhibition (cleavage) of HOXB8 mRNA.58  These observations are consistent with an aberrant regulatory circuit including NPM1, HOX genes, and microRNAs, which might be engaged in the arrest of cellular differentiation of hematopoietic progenitors and development of AML with mutant NPM1. Obviously, functional studies are required to provide formal evidence about the pathogenetic role of these candidate microRNAs.

In AMLs with FLT3-ITD, we found oncogenic microRNA-155 to be significantly up-regulated. Apart from the oncogenic potential of microRNA-155 in lymphomas27,59  and its role in immune function60,61  this particular microRNA has been postulated to have a role in myeloid differentiation.62 

AML with CEBPA mutations was associated with a distinct set of mainly down-regulated microRNAs. The possible cause of the phenomenon of down-regulation remains entirely elusive. One possibility to be considered is that differential regulation of microRNA processing (ie, expression levels of enzymes Drosha and Dicer) would result in a global down-regulation of microRNA expression in one subtype of AML and up-regulation in another. However, no differential expression of Drosha and Dicer was observed in AML patients with CEBPA mutation (data not shown), therefore other players may be implicated in this regulation.

Since microRNAs may have independent roles in the pathobiology of AML, not related to the underlying cytogenetic and molecular abnormalities, we performed a supervised analysis to identify differentially expressed microRNA between all AML samples and normal CD34+ bone marrow samples. We found microRNA-21 to be consistently up-regulated in AML. In earlier studies, this microRNA has been demonstrated to be associated with various cancers. Several tumor suppressors have experimentally been validated as targets of microRNA-21.63,64  Furthermore, microRNA-21 was found to contribute to cancer growth by modulating PTEN expression.65  Conditional deletion of PTEN in adult hematopoietic cells produced myeloproliferative disease and transplantable leukemias.66,67  Potential function of microRNA-21 in relation to PTEN and AML deserves further investigation.

The microRNA expression profiles were used for the class prediction of various subtypes of AML. We were able to predict AMLs with t(8;21), t(15;17), and inv(16) by limited sets of selected microRNAs. We also used PAM to compare the ability of class prediction by microRNA expression with that by transcript expression profiles in the same series of patients. Prediction of AML with t(8;21), t(15;17), and inv(16) using microRNAs required a greater number of microRNAs than mRNA probe sets. That the mRNA predictor required a smaller set of probes than the classifier constructed by microRNAs is not at all surprising. MicroRNA analysis made use of a restricted number of only 260 microRNAs, which is in sharp contrast to 54 675 probes that were included on the microarray used for mRNA profiling. As a matter of fact, the observed predictability of AML subtypes on the basis of a limited number of microRNAs demonstrates the power and potential of the approach of microRNA expression profiling for AML subtypes. The number of known human microRNAs continuously increases, and more than 1000 of them are predicted in silico. Therefore, in the future, new microRNAs with specific expression in the subsets of leukemia will likely enhance the possibilities for classification and class prediction of AML by microRNA expression.

To this end, the study presented here demonstrates that AMLs can be classified according to their variable and distinctive microRNA expression profiles. Application of SAM and PAM methods revealed significant differentially expressed microRNAs that characterize and categorize the subgroups of AML with different cytogenetic and molecular aberrations, in particular AML with t(8;21), inv(16), and t(15;17) and AML with NPM1 and CEBPA mutations. In comparison with mRNAs, the microRNA class predictor was less accurate in predicting AML with t(15;17), t(8;21), and inv(16). Combinations of selected sets of microRNAs could predict AMLs with mutations in the genes of NPM1, CEBPA, and FLT3-ITD with similar accuracy as mRNA probe set combinations. Furthermore, several established oncogenic and tumor suppressor microRNAs, such as microRNA-155, microRNA-21, and let-7, appear to be associated with specific subtypes of AML. These data suggest that microRNAs are implicated in the pathogenesis of AML and may have a useful role in future diagnostics.

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.

Acknowledgments

We thank Andre Uitterlinden (Department of Internal Medicine) for providing robot facility and Pascal Arp (Department of Internal Medicine) for his technical support in high-throughput experiments using a robot. Furthermore, we thank Berna Beverloo (Department of Clinical Genetics) for reviewing cytogenetic abnormalities and Mars van ‘t-Veer and HRC (Hematology Review Committee) for reviewing AML bone marrow clinical diagnostics. We also thank Bas Wouters for his support in gene expression data analysis and Ruud Delwel for helpful discussions.

This work was supported by a grant from Erasmus University Medical Center (M.J.-L.).

Authorship

Contribution: M.J.-L. designed the study, designed and supervised the experiments and data analysis, and wrote the paper; S.M.S. and M.K.D. performed experiments and data analysis; P.J.M.V. and B.L. codesigned the study, cowrote the paper, and established bio-bank and database of AML specimens containing all clinical, cytogenetic, and molecular data.

Conflict-of-interest disclosure: The authors declare no competing financial interests.

Correspondence: Mojca Jongen-Lavrencic, Erasmus University Medical Center, Department of Hematology, Dr Molewaterplein 50, 3015 GE Rotterdam, Postbus 2040, 3000 CA Rotterdam, The Netherlands; e-mail: m.lavrencic@erasmusmc.nl.

References

References
1
Ebert
BL
Golub
TR
Genomic approaches to hematologic malignancies.
Blood
2004
, vol. 
104
 (pg. 
923
-
932
)
2
Valk
PJ
Delwel
R
Lowenberg
B
Gene expression profiling in acute myeloid leukemia.
Curr Opin Hematol
2005
, vol. 
12
 (pg. 
76
-
81
)
3
Valk
PJ
Verhaak
RG
Beijen
MA
, et al. 
Prognostically useful gene-expression profiles in acute myeloid leukemia.
N Engl J Med
2004
, vol. 
350
 (pg. 
1617
-
1628
)
4
Bullinger
L
Valk
PJ
Gene expression profiling in acute myeloid leukemia.
J Clin Oncol
2005
, vol. 
23
 (pg. 
6296
-
6305
)
5
Bullinger
L
Dohner
K
Bair
E
, et al. 
Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia.
N Engl J Med
2004
, vol. 
350
 (pg. 
1605
-
1616
)
6
Grimwade
D
Walker
H
Harrison
G
, et al. 
The predictive value of hierarchical cytogenetic classification in older adults with acute myeloid leukemia (AML): analysis of 1065 patients entered into the United Kingdom Medical Research Council AML11 trial.
Blood
2001
, vol. 
98
 (pg. 
1312
-
1320
)
7
Slovak
ML
Kopecky
KJ
Cassileth
PA
, et al. 
Karyotypic analysis predicts outcome of preremission and postremission therapy in adult acute myeloid leukemia: a Southwest Oncology Group/Eastern Cooperative Oncology Group Study.
Blood
2000
, vol. 
96
 (pg. 
4075
-
4083
)
8
Byrd
JC
Mrozek
K
Dodge
RK
, et al. 
Pretreatment cytogenetic abnormalities are predictive of induction success, cumulative incidence of relapse, and overall survival in adult patients with de novo acute myeloid leukemia: results from Cancer and Leukemia Group B (CALGB 8461).
Blood
2002
, vol. 
100
 (pg. 
4325
-
4336
)
9
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
 (pg. 
3740
-
3746
)
10
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
 (pg. 
4011
-
4020
)
11
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
 (pg. 
4372
-
4380
)
12
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
 (pg. 
2717
-
2723
)
13
Barjesteh van Waalwijk van Doorn-Khosrovani
S
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
 (pg. 
31
-
40
)
14
Falini
B
Nicoletti
I
Martelli
MF
Mecucci
C
Acute myeloid leukemia carrying cytoplasmic/mutated nucleophosmin (NPMc+ AML): biologic and clinical features.
Blood
2007
, vol. 
109
 (pg. 
874
-
885
)
15
Bartel
DP
MicroRNAs: genomics, biogenesis, mechanism, and function.
Cell
2004
, vol. 
116
 (pg. 
281
-
297
)
16
Plasterk
RH
Micro RNAs in animal development.
Cell
2006
, vol. 
124
 (pg. 
877
-
881
)
17
Lu
J
Getz
G
Miska
EA
, et al. 
MicroRNA expression profiles classify human cancers.
Nature
2005
, vol. 
435
 (pg. 
834
-
838
)
18
Kent
OA
Mendell
JT
A small piece in the cancer puzzle: microRNAs as tumor suppressors and oncogenes.
Oncogene
2006
, vol. 
25
 (pg. 
6188
-
6196
)
19
Xi
Y
Shalgi
R
Fodstad
O
Pilpel
Y
Ju
J
Differentially regulated micro-RNAs and actively translated messenger RNA transcripts by tumor suppressor p53 in colon cancer.
Clin Cancer Res
2006
, vol. 
12
 (pg. 
2014
-
2024
)
20
Cummins
JM
Velculescu
VE
Implications of micro-RNA profiling for cancer diagnosis.
Oncogene
2006
, vol. 
25
 (pg. 
6220
-
6227
)
21
Cummins
JM
He
Y
Leary
RJ
, et al. 
The colorectal microRNAome.
Proc Natl Acad Sci U S A
2006
, vol. 
103
 (pg. 
3687
-
3692
)
22
Iorio
MV
Ferracin
M
Liu
CG
, et al. 
MicroRNA gene expression deregulation in human breast cancer.
Cancer Res
2005
, vol. 
65
 (pg. 
7065
-
7070
)
23
Takamizawa
J
Konishi
H
Yanagisawa
K
, et al. 
Reduced expression of the let-7 microRNAs in human lung cancers in association with shortened postoperative survival.
Cancer Res
2004
, vol. 
64
 (pg. 
3753
-
3756
)
24
Calin
GA
Ferracin
M
Cimmino
A
, et al. 
A MicroRNA signature associated with prognosis and progression in chronic lymphocytic leukemia.
N Engl J Med
2005
, vol. 
353
 (pg. 
1793
-
1801
)
25
Hayashita
Y
Osada
H
Tatematsu
Y
, et al. 
A polycistronic microRNA cluster, miR-17–92, is overexpressed in human lung cancers and enhances cell proliferation.
Cancer Res
2005
, vol. 
65
 (pg. 
9628
-
9632
)
26
Kluiver
J
van den Berg
A
de Jong
D
, et al. 
Regulation of pri-microRNA BIC transcription and processing in Burkitt lymphoma.
Oncogene
2007
, vol. 
26
 (pg. 
3769
-
3776
)
27
Eis
PS
Tam
W
Sun
L
, et al. 
Accumulation of miR-155 and BIC RNA in human B cell lymphomas.
Proc Natl Acad Sci U S A
2005
, vol. 
102
 (pg. 
3627
-
3632
)
28
Debernardi
S
Skoulakis
S
Molloy
G
Chaplin
T
Dixon-McIver
A
Young
BD
MicroRNA miR-181a correlates with morphological sub-class of acute myeloid leukaemia and the expression of its target genes in global genome-wide analysis.
Leukemia
2007
, vol. 
21
 (pg. 
912
-
916
)
29
Mi
S
Lu
J
Sun
M
, et al. 
MicroRNA expression signatures accurately discriminate acute lymphoblastic leukemia from acute myeloid leukemia.
Proc Natl Acad Sci U S A
2007
, vol. 
104
 (pg. 
19971
-
19976
)
30
Isken
F
Steffen
B
Merk
S
, et al. 
Identification of acute myeloid leukaemia associated microRNA expression patterns.
Br J Haematol
2008
, vol. 
140
 (pg. 
153
-
161
)
31
Chen
C
Ridzon
DA
Broomer
AJ
, et al. 
Real-time quantification of microRNAs by stem-loop RT-PCR.
Nucleic Acids Res
2005
, vol. 
33
 pg. 
e179
  
32
Lowenberg
B
Suciu
S
Archimbaud
E
, et al. 
Use of recombinant GM-CSF during and after remission induction chemotherapy in patients aged 61 years and older with acute myeloid leukemia: final report of AML-11, a phase III randomized study of the Leukemia Cooperative Group of European Organisation for the Research and Treatment of Cancer and the Dutch Belgian Hemato-Oncology Cooperative Group.
Blood
1997
, vol. 
90
 (pg. 
2952
-
2961
)
33
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
 (pg. 
743
-
752
)
34
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
 (pg. 
2908
-
2913
)
35
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
 (pg. 
3747
-
3754
)
36
Lao
K
Xu
NL
Sun
YA
Livak
KJ
Straus
NA
Real time PCR profiling of 330 human micro-RNAs.
Biotechnol J
2007
, vol. 
2
 (pg. 
33
-
35
)
37
Vandesompele
J
De Preter
K
Pattyn
F
, et al. 
Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes.
Genome Biol
2002
, vol. 
3
  
RESEARCH0034
38
Livak
KJ
Schmittgen
TD
Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method.
Methods
2001
, vol. 
25
 (pg. 
402
-
408
)
39
National Center for Biotechnology Information
Gene Expression Omnibus.
Accessed March 12, 2008 
40
Verhaak
RG
Sanders
MA
Bijl
MA
, et al. 
HeatMapper: powerful combined visualization of gene expression profile correlations, genotypes, phenotypes and sample characteristics.
BMC Bioinformatics
2006
, vol. 
7
 (pg. 
337
-
340
)
41
Carey
VJ
Gentry
J
Whalen
E
Gentleman
R
Network structures and algorithms in Bioconductor.
Bioinformatics
2005
, vol. 
21
 (pg. 
135
-
136
)
42
Tibshirani
R
Hastie
T
Narasimhan
B
Chu
G
Diagnosis of multiple cancer types by shrunken centroids of gene expression.
Proc Natl Acad Sci U S A
2002
, vol. 
99
 (pg. 
6567
-
6572
)
43
Golub
TR
Slonim
DK
Tamayo
P
, et al. 
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Science
1999
, vol. 
286
 (pg. 
531
-
537
)
44
Bullinger
L
Rucker
FG
Kurz
S
, et al. 
Gene-expression profiling identifies distinct subclasses of core binding factor acute myeloid leukemia.
Blood
2007
, vol. 
110
 (pg. 
1291
-
1300
)
45
Shell
S
Park
SM
Radjabi
AR
, et al. 
Let-7 expression defines two differentiation stages of cancer.
Proc Natl Acad Sci U S A
2007
, vol. 
104
 (pg. 
11400
-
11405
)
46
Zhang
HH
Wang
XJ
Li
GX
Yang
E
Yang
NM
Detection of let-7a microRNA by real-time PCR in gastric carcinoma.
World J Gastroenterol
2007
, vol. 
13
 (pg. 
2883
-
2888
)
47
Lee
YS
Dutta
A
The tumor suppressor microRNA let-7 represses the HMGA2 oncogene.
Genes Dev
2007
, vol. 
21
 (pg. 
1025
-
1030
)
48
Brueckner
B
Stresemann
C
Kuner
R
, et al. 
The human let-7a-3 locus contains an epigenetically regulated microRNA gene with oncogenic function.
Cancer Res
2007
, vol. 
67
 (pg. 
1419
-
1423
)
49
Johnson
SM
Grosshans
H
Shingara
J
, et al. 
RAS is regulated by the let-7 microRNA family.
Cell
2005
, vol. 
120
 (pg. 
635
-
647
)
50
Yanaihara
N
Caplen
N
Bowman
E
, et al. 
Unique microRNA molecular profiles in lung cancer diagnosis and prognosis.
Cancer Cell
2006
, vol. 
9
 (pg. 
189
-
198
)
51
Schubbert
S
Shannon
K
Bollag
G
Hyperactive Ras in developmental disorders and cancer.
Nat Rev Cancer
2007
, vol. 
7
 (pg. 
295
-
308
)
52
Bacher
U
Haferlach
T
Schoch
C
Kern
W
Schnittger
S
Implications of NRAS mutations in AML: a study of 2502 patients.
Blood
2006
, vol. 
107
 (pg. 
3847
-
3853
)
53
Valk
PJ
Bowen
DT
Frew
ME
Goodeve
AC
Lowenberg
B
Reilly
JT
Second hit mutations in the RTK/RAS signaling pathway in acute myeloid leukemia with inv(16) [letter].
Haematologica
2004
, vol. 
89
 pg. 
106
 
54
Saito
Y
Liang
G
Egger
G
, et al. 
Specific activation of microRNA-127 with downregulation of the proto-oncogene BCL6 by chromatin-modifying drugs in human cancer cells.
Cancer Cell
2006
, vol. 
9
 (pg. 
435
-
443
)
55
Alcalay
M
Tiacci
E
Bergomas
R
, et al. 
Acute myeloid leukemia bearing cytoplasmic nucleophosmin (NPMc+ AML) shows a distinct gene expression profile characterized by up-regulation of genes involved in stem-cell maintenance.
Blood
2005
, vol. 
106
 (pg. 
899
-
902
)
56
Baldus
CD
Mrozek
K
Marcucci
G
Bloomfield
CD
Clinical outcome of de novo acute myeloid leukaemia patients with normal cytogenetics is affected by molecular genetic alterations: a concise review.
Br J Haematol
2007
, vol. 
137
 (pg. 
387
-
400
)
57
Wilson
CS
Davidson
GS
Martin
SB
, et al. 
Gene expression profiling of adult acute myeloid leukemia identifies novel biologic clusters for risk classification and outcome prediction.
Blood
2006
, vol. 
108
 (pg. 
685
-
696
)
58
Yekta
S
Shih
IH
Bartel
DP
MicroRNA-directed cleavage of HOXB8 mRNA.
Science
2004
, vol. 
304
 (pg. 
594
-
596
)
59
Kluiver
J
Poppema
S
de Jong
D
, et al. 
BIC and miR-155 are highly expressed in Hodgkin, primary mediastinal and diffuse large B cell lymphomas.
J Pathol
2005
, vol. 
207
 (pg. 
243
-
249
)
60
Thai
TH
Calado
DP
Casola
S
, et al. 
Regulation of the germinal center response by microRNA-155.
Science
2007
, vol. 
316
 (pg. 
604
-
608
)
61
Rodriguez
A
Vigorito
E
Clare
S
, et al. 
Requirement of bic/microRNA-155 for normal immune function.
Science
2007
, vol. 
316
 (pg. 
608
-
611
)
62
Georgantas
RW
Hildreth
R
Morisot
S
Alder
J
Civin
CI
MicroRNA hsa-mir-155 blocks myeloid and erythroid differentiation of human CD34+ cells [abstract].
ASH Annual Meeting Abstracts
2006
, vol. 
108
 pg. 
1337
 
63
Zhu
S
Si
ML
Wu
H
Mo
YY
MicroRNA-21 targets the tumor suppressor gene tropomyosin 1 (TPM1).
J Biol Chem
2007
, vol. 
282
 (pg. 
14328
-
14336
)
64
Asangani
IA
Rasheed
SA
Nikolova
DA
, et al. 
MicroRNA-21 (miR-21) post-transcriptionally downregulates tumor suppressor Pdcd4 and stimulates invasion, intravasation and metastasis in colorectal cancer.
Oncogene
 
Prepublished on October 29, 2007, as DOI 10.1038/sj.onc.1210856
65
Meng
F
Henson
R
Wehbe-Janek
H
Ghoshal
K
Jacob
ST
Patel
T
MicroRNA-21 regulates expression of the PTEN tumor suppressor gene in human hepatocellular cancer.
Gastroenterology
2007
, vol. 
133
 (pg. 
647
-
658
)
66
Yilmaz
OH
Valdez
R
Theisen
BK
, et al. 
Pten dependence distinguishes haematopoietic stem cells from leukaemia-initiating cells.
Nature
2006
, vol. 
441
 (pg. 
475
-
482
)
67
Zhang
J
Grindley
JC
Yin
T
, et al. 
PTEN maintains haematopoietic stem cells and acts in lineage choice and leukaemia prevention.
Nature
2006
, vol. 
441
 (pg. 
518
-
522
)