Prognostically Useful Gene-Expression Profiles in Acute Myeloid Leukemia
Peter J.M. Valk, Ph.D., Roel G.W. Verhaak, M.Sc., M. Antoinette Beijen, Claudia A.J. Erpelinck, Sahar Barjesteh van Waalwijk van Doorn-Khosrovani, M.Sc., Judith M. Boer, Ph.D., H. Berna Beverloo, Ph.D., Michael J. Moorhouse, Ph.D., Peter J. van der Spek, Ph.D., Bob Löwenberg, M.D., Ph.D., and Ruud Delwel, Ph.D.
Background In patients with acute myeloid leukemia (AML) a combinationof methods must be used to classify the disease, make therapeuticdecisions, and determine the prognosis. However, this combinedapproach provides correct therapeutic and prognostic informationin only 50 percent of cases.
Methods We determined the gene-expression profiles in samplesof peripheral blood or bone marrow from 285 patients with AMLusing Affymetrix U133A GeneChips containing approximately 13,000unique genes or expression-signature tags. Data analyses werecarried out with Omniviz, significance analysis of microarrays,and prediction analysis of microarrays software. Statisticalanalyses were performed to determine the prognostic significanceof cases of AML with specific molecular signatures.
Results Unsupervised cluster analyses identified 16 groups ofpatients with AML on the basis of molecular signatures. We identifiedthe genes that defined these clusters and determined the minimalnumbers of genes needed to identify prognostically importantclusters with a high degree of accuracy. The clustering wasdriven by the presence of chromosomal lesions (e.g., t(8;21),t(15;17), and inv(16)), particular genetic mutations (CEBPA),and abnormal oncogene expression (EVI1). We identified severalnovel clusters, some consisting of specimens with normal karyotypes.A unique cluster with a distinctive gene-expression signatureincluded cases of AML with a poor treatment outcome.
Conclusions Gene-expression profiling allows a comprehensiveclassification of AML that includes previously identified geneticallydefined subgroups and a novel cluster with an adverse prognosis.
Acute myeloid leukemia (AML) is not a single disease but a groupof neoplasms with diverse genetic abnormalities and variableresponses to treatment. Cytogenetics and molecular analysescan be used to identify subgroups of AML with different prognoses.For instance, the translocations inv(16), t(8;21), and t(15;17)herald a favorable prognosis, whereas other cytogenetic aberrationsindicate poor-risk leukemia.1,2,3,4,5 Abnormalities involving11q23, t(6;9), or 7(q) are defined as poor-risk markers by somegroups2,3 and as intermediate-risk markers by others.3,4,5 Theseinconsistencies and the absence of cytogenetic abnormalitiesin a considerable proportion of patients argue for refinementof the classification of AML.
Additional reasons for extending the molecular analyses of AMLare exemplified by findings regarding the gene for fms-liketyrosine kinase 3 (FLT3), the gene encoding ectotropic viralintegration 1 site (EVI1), and the gene for CCAAT/enhancer bindingprotein alpha (CEBPA). An internal tandem duplication in FLT3,a hematopoietic growth factor receptor, is the most common molecularabnormality in AML.6,7 The presence of such mutations in FLT3and elevated expression of the transcription factor EVI1 confera poor prognosis,6,7,8 whereas mutations in CEBPA are associatedwith a good outcome.9,10
Molecular classification based on DNA-expression profiling offersa powerful way of distinguishing myeloid from lymphoid cancerand subclasses within these two diseases.11,12,13,14 DNA-microarrayanalysis has the potential to identify distinct subgroups ofAML with the use of one comprehensive assay, to classify casesthat currently resist categorization by means of other methods,and to identify subgroups with favorable or unfavorable prognoseswithin genetically defined subclasses. The goals of this studyof 285 adults with AML were to use gene-expression profilesto identify established and novel subclasses of AML and otherwiseunrecognized cases of poor-risk AML.
Methods
Patients and Cell Samples
Eligible patients had received a diagnosis of primary AML, whichhad been confirmed by means of a cytologic examination of bloodand bone marrow (Table 1). All patients were treated accordingto the protocols of the Dutch–Belgian Hematology–OncologyCooperative group (available at www.hovon.nl).15,16,17 All subjectsprovided written informed consent. A total of 285 patients providedbone marrow aspirates or peripheral-blood samples at the timeof diagnosis and 8 healthy control subjects provided peripheral-bloodsamples or bone marrow aspirates. Blasts and mononuclear cellswere purified by Ficoll–Hypaque (Nygaard) centrifugationand cryopreserved. CD34+ cells from three control subjects weresorted by means of a fluorescence-activated cell sorter. TheAML samples contained 80 to 100 percent blast cells after thawing,regardless of the blast count at diagnosis.
Table 1. Clinical and Molecular Characteristics of the 285 Patients with Newly Diagnosed AML.
Isolation and Quality Control of RNA
After thawing, cells were washed once with Hanks' balanced-saltsolution. High-quality total RNA was extracted by lysis withguanidinium thiocyanate followed by cesium chloride–gradientpurification.18 RNA levels, quality, and purity were assessedwith the use of the RNA 6000 Nano assay on the Agilent 2100Bioanalyzer (Agilent). None of the samples showed RNA degradation(ratio of 28S ribosomal RNA to 18S ribosomal RNA of at least2) or contamination by DNA.
Gene Profiling and Quality Control
Samples were analyzed with the use of Affymetrix U133A GeneChips.Each gene on this chip is represented by 10 to 20 oligonucleotides,termed a "probe set." The intensity of hybridization of labeledmessenger RNA (mRNA) to these sets reflects the level of expressionof a particular gene. The U133A GeneChip contains 22,283 probesets, representing approximately 13,000 genes. We used 10 µgof total RNA to prepare antisense biotinylated RNA. Single-strandedcomplementary DNA (cDNA) and double-stranded cDNA were synthesizedaccording to the manufacturer's protocol (Invitrogen Life Technologies)with the use of the T7-(deoxythymidine)24-primer (Genset). Invitro transcription was performed with biotin-11-cytidine triphosphateand biotin-16-uridine triphosphate (Perkin–Elmer) andthe MEGAScript T7 labeling kit (Ambion). Double-stranded cDNAand complementary RNA (cRNA) were purified and fragmented withthe GeneChip Sample Cleanup Module (Affymetrix). BiotinylatedRNA was hybridized to the Affymetrix U133A GeneChip (45°Cfor 16 hours). Staining, washing, and scanning procedures werecarried out as described in the GeneChip Expression Analysistechnical manual (Affymetrix). All GeneChips were visually inspectedfor irregularities. The global method of scaling, or normalization,was applied, and the mean (±SD) difference between thescaling, or normalization, factors of all GeneChips (293 samples;285 from patients with AML, 5 from subjects with normal bonemarrow, and 3 from subjects with CD34+ cell samples) was 0.70±0.26.All additional measures of quality — the percentage ofgenes present (50.6±3.8), the ratio of actin 3' to 5'(1.24±0.19), and the ratio of GAPDH 3' to 5' (1.05±0.14)— indicated a high overall quality of the samples andassays. Detailed clinical, cytogenetic, and molecular cytogeneticinformation is available at the Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo,accession number GSE1159
[NCBI GEO]
).
Data Normalization, Analysis, and Visualization
All intensity values were scaled to an average value of 150per GeneChip according to the method of global scaling, or normalization,provided in the Affymetrix Microarray Suite software, version5.0 (MAS5.0). Since our methods reliably identify samples withan average intensity value of 30 or more but do not reliablydiscriminate values between 0 and 30, these values were setto 30. This procedure affected 31 percent of all intensity values,of which 64 percent were flagged as absent by the MAS5.0 software,3 percent were flagged as marginal, and 33 percent were flaggedas present according to the MAS5.0 software.
For each probe set, the geometric mean of the hybridizationintensities of all samples from the patients was calculated.The level of expression of each probe set in every sample wasdetermined relative to this geometric mean and logarithmicallytransformed (on a base 2 scale) to ascribe equal weight to gene-expressionlevels with similar relative distances to the geometric mean.Deviation from the geometric mean reflects differential geneexpression. The transformed expression data were subsequentlyimported into Omniviz software, version 3.6 (Omniviz), significanceanalysis of microarrays (SAM) software, version 1.21, and predictionanalysis of microarrays (PAM) software, version 1.12.
Use of Pearson's Correlation and Visualization Tool
The Omniviz package was used to perform and visualize the resultsof unsupervised cluster analysis (an analysis that does nottake into account external information such as the morphologicsubtype or karyotype). Genes (probe sets) whose level of expressiondiffered from the geometric mean (reflecting up- or down-regulation)in at least one patient were selected for further analysis.The clustering of molecularly recognizable specific groups ofpatients was investigated with each of the selected probe setswith the use of the Pearson's Correlation and Visualizationtool of Omniviz (provided in Fig. B, C, D, E, F, G, and H inSupplementary Appendix 1, available with the full text of thisarticle at www.nejm.org).
The SAM Method
All supervised analyses were performed with the use of SAM software.19A supervised analysis correlates gene expression with an externalvariable such as the karyotype or the duration of survival.SAM calculates a score for each gene on the basis of the changein expression relative to the SD of all 285 measurements. Thecriteria for identifying the top 40 genes for an assigned clusterwere a minimal difference in gene expression between the assignedcluster and the other AML samples by a factor of 2 and a q valueof less than 2 percent. The q value for each gene representsthe probability that it is falsely called significantly deregulated.
The PAM Method
All supervised class-prediction analyses were performed by applyingPAM software in R (version 1.7.1).20 The method of the nearestshrunken centroids identifies a subgroup of genes that bestcharacterizes a predefined class. The prediction error was calculatedby means of 10-fold cross validation (see the Glossary) withinthe training set (two thirds of the patients) followed by theuse of a second validation set (one third of the patients).All genes identified by the SAM and PAM methods are listed inSupplementary Appendix 1 (Tables A1 to P1 and R).
Reverse-Transcriptase Polymerase Chain Reactions and Sequence Analyses
Reverse-transcriptase–polymerase-chain-reaction (RT-PCR)assays and sequence analyses for internal tandem duplicationand tyrosine kinase domain mutations in FLT3 and mutations inN-RAS, K-RAS, and CEBPA, as well as real-time PCR for EVI1 wereperformed as described previously.8,9,21,22 AML samples of theclusters characterized by favorable cytogenetic characteristics(t(8;21), t(15;17), and inv(16)) were analyzed for the expressionof fusion genes by real-time PCR (Supplementary Appendix 1).
Statistical Analysis
Statistical analyses were performed with Stata Statistical Software,release 7.0. Actuarial probabilities of overall survival (withfailure defined as death from any cause) and event-free survival(with failure defined as incomplete remission [set at day 1],relapse, or death during a first complete remission) were estimatedaccording to the Kaplan–Meier method.
Results
Visual Correlation of Gene Expression
All specimens of AML were classified into subgroups with theuse of unsupervised ordering (i.e., without taking into accounthematologic, cytogenetic, or other external information). Optimalclustering of these specimens was reached with the use of 2856probe sets (a probe set consists of 10 to 20 oligonucleotides);2856 sets represent 2008 annotated genes and 146 expressed-sequencetags, which are short sequences of unknown genes (Figure 1Aand Table 2, and Fig. B, C, D, E, F, G, and H in Supplementary Appendix 1).
Figure 1. Correlation View of Specimens from 285 Patients with AML Involving 2856 Probe Sets (Panel A) and an Adapted Correlation View (2856 Probe Sets) (Right-Hand Side of Panel B), and the Levels of Expression of the Top 40 Genes That Characterized Each of the 16 Individual Clusters (Left-Hand Side of Panel B).
In Panel A, the Correlation Visualization tool displays pairwise correlations between the samples. The colors of the cells relate to Pearson's correlation coefficient values, with deeper colors indicating higher positive (red) or negative (blue) correlations. One hundred percent negative correlation would indicate that genes with a high level of expression in one sample would always have a low level of expression in the other sample and vice versa. Box 1 indicates a positive correlation between clusters 5 and 9 and box 2 a negative correlation between clusters 5 and 12. The red diagonal line displays the intraindividual comparison of results for a patient with AML (i.e., 100 percent correlation). To reveal the patterns of correlation, we applied a matrix-ordering method to rearrange the samples. The ordering algorithm starts with the most highly correlated pair of samples and, through an iterative process, sorts all the samples into correlated blocks. Each sample is joined to a block in an ordered manner so that a correlation trend is formed within a block, with the most correlated samples at the center. The blocks are then positioned along the diagonal of the plot in a similar ordered manner. Panel B shows all 16 clusters identified on the basis of the Correlation View. The French–American–British (FAB) classification and karyotype based on cytogenetic analyses are depicted in the columns along the original diagonal of the Correlation View; FAB subtype M0 is indicated in black, subtype M1 in green, subtype M2 in purple, subtype M3 in orange, subtype M4 in yellow, subtype M5 in blue, and subtype M6 in gray; normal karyotypes are indicated in green, inv(16) abnormalities in yellow, t(8;21) abnormalities in purple, t(15;17) abnormalities in orange, 11q23 abnormalities in blue, 7(q) abnormalities in red, +8 aberrations in pink, complex karyotypes (those involving more than three chromosomal abnormalities) in black, and other abnormalities in gray. FLT3 internal tandem duplication (ITD) mutations, FLT3 mutations in the tyrosine kinase domain (TKD), N-RAS, K-RAS, and CEBPA mutations, and the overexpression of EVI1 are depicted in the same set of columns: red indicates the presence of a given abnormality, and green its absence. The levels of expression of the top 40 genes identified by the significance analysis of microarrays of each of the 16 clusters as well as in normal bone marrow (NBM) and CD34+ cells are shown on the left side. The scale bar indicates an increase (red) or decrease (green) in the level of expression by a factor of at least 4 relative to the geometric mean of all samples. The percentages of the most common abnormalities (those present in more than 40 percent of specimens) and the percentages of specimens in each cluster with a normal karyotype are indicated.
Table 2. Evaluation of the Omniviz Correlation View Results on the Basis of the Clustering of AML Specimens with Similar Molecular Abnormalities.
Sixteen distinct groups of patients with AML were identifiedon the basis of strong similarities in gene-expression profiles.Figure 1A, a Pearson's correlation view, shows these clustersas red squares along the diagonal. A red rectangle indicatespositive pairwise correlations (equality in gene expressionbetween clusters) and a blue rectangle indicates negative pairwisecorrelations (inequality in gene expression between clusters)(Figure 1A, and Fig. A in Supplementary Appendix 1). The finalOmniviz Correlation View was adapted so that cytologic, cytogenetic,and molecular features were plotted directly adjacent to theoriginal diagonal. This arrangement allowed the visualizationof groups of patients with similar patterns of gene expressionalong with relevant clinical and genetic findings (Figure 1B).
Distinct clusters of t(8;21), inv(16), and t(15;17) were readilyidentified with 1692 probe sets (Table 2). Identification ofclusters with mutations in FLT3, monosomy 7, or overexpressionof EVI1 required 2856 probe sets (Table 2, and Fig. B, C, D,E, F, G, and H in Supplementary Appendix 1). When more geneswere used, the compact pattern of clustering vanished (Table 2).When included in the Omniviz Correlation View analyses (2856probe sets), all five samples of bone marrow and three CD34+samples from control subjects gathered within clusters 8 and10, respectively.
Genes characteristic of each of the 16 clusters were obtainedby means of supervised analysis (distinctions on the basis ofpredefined classes), with the use of the SAM method. The expressionprofiles of the top 40 genes of each cluster are plotted inFigure 1B beside the correlation view. The SAM analyses identified599 discriminating genes (Tables A1 to P1 in Supplementary Appendix 1);we were unable to identify a distinct gene profile for cluster14.
Recurrent Translocations
CBF-MYH11
All AML samples with inv(16), which causes the CBF-MYH11 fusiongene, gathered within cluster 9 (Figure 1B, and Table I in Supplementary Appendix 1).Four specimens within this cluster were not knownto harbor an inv(16), but molecular analysis and Southern blottingrevealed that their leukemic cells had the CBF-MYH11 fusiongene (Table I and Fig. I in Supplementary Appendix 1). SAM analysisrevealed that MYH11 was the most discriminative gene for thiscluster (Table I1 and Fig. J in Supplementary Appendix 1). Interestingly,a low level of expression of CBF was correlated with this cluster,perhaps because of the decreased expression or deletion of theMYH11-CBF alternate fusion gene or down-regulation of the normalCBF allele by the CBF-MYH11 fusion protein.
PML-RAR
Cluster 12 contained all cases of acute promyelocytic leukemia(APL) with t(15;17) (Figure 1B, and Table L in Supplementary Appendix 1),including one patient (Patient 322) who had previouslyreceived a diagnosis of APL with PML-RAR on the basis of RT-PCRalone. SAM analyses revealed that genes for hepatocyte growthfactor (HGF), macrophage-stimulating 1 growth factor (MST1),and fibroblast growth factor 13 (FGF13) were specific for thiscluster. In addition, cluster 12 could be separated into twosubgroups: one with a high and the other with a low white-cellcount (Fig. K in Supplementary Appendix 1). This subdivisioncorresponds to the presence of FLT3 internal tandem duplicationmutations (Figure 1B).
AML1-ETO
All specimens from patients with the t(8;21) that generatesthe AML1-ETO fusion gene grouped within cluster 13 (Figure 1B,and Table M in Supplementary Appendix 1). SAM identified ETOas the most discriminative gene for this cluster (Table M1 andFig. L in Supplementary Appendix 1).
11q23 Abnormalities
Cases with 11q23 abnormalities were scattered among the 285samples, although two subgroups were apparent: cluster 1 andcluster 16 (Figure 1B, and Tables A and P in Supplementary Appendix 1).Cluster 16, with 11 total cases, contained 4 cases of t(9;11)and 1 case of t(11;19). SAM analyses identified a strong signatureof up-regulated genes in most cases in this cluster (Figure 1B,and Table P1 in Supplementary Appendix 1). Although 6 of14 cases within cluster 1 also had 11q23 abnormalities, thissubgroup was more heterogeneous than cluster 16 (Figure 1B).
CEBPA Mutations
Mutations in CEBPA occur in approximately 7 percent of patientswith AML, most with a normal karyotype, and predict a favorableoutcome.9,10 Two clusters (4 and 15) had a high frequency ofCEBPA mutations (Figure 1B). The sets of up-regulated or down-regulatedgenes in cluster 4 discriminated the specimens it containedfrom those in cluster 15 (Table D1 in Supplementary Appendix 1).The up-regulated genes included the T-cell genes CD7 andthe T-cell receptor delta locus, which may be expressed by immatureAML cells.23,24 All but one of the top 40 genes of cluster 15were down-regulated (Table O1 in Supplementary Appendix 1).These genes were also down-regulated in cluster 4 (Figure 1B).The genes encoding alpha1-catenin (CTNNA1), tubulin beta-5 (TUBB5),and Nedd4 family interacting protein 1 (NDFIP1) were the onlydown-regulated genes among the top 40 in both cluster 4 andcluster 15.
Overexpression of EVI1
High levels of expression of EVI1, which occur in approximately10 percent of cases of AML, predict a poor outcome.8 In cluster10, 10 of 22 specimens (Table J in Supplementary Appendix 1)showed increased expression of EVI1, and 6 of these 10 specimenshad chromosome 7 abnormalities. In cluster 8, 4 of 13 specimensalso had chromosome 7 aberrations (Table H in Supplementary Appendix 1),but since its molecular signature differed fromthat of cluster 10 (Figure 1B), the high level of expressionof EVI1 or EVI1-related proteins may have determined the molecularprofile of cluster 10. In the heterogeneous cluster 1, 5 of14 specimens also had increased EVI1 expression. These specimensmay have appeared outside cluster 10 because their molecularsignatures were most likely the result of the overexpressionof EVI1 and an 11q23 abnormality.
FLT3 and RAS Mutations
Samples from most patients in clusters 2, 3, and 6 harboreda FLT3 internal tandem duplication (Figure 1B). Almost all thesepatients had a normal karyotype. The presence of FLT3 internaltandem duplication seemed to divide clusters 3, 5, and 12 intotwo groups. Other individual specimens with a FLT3 internaltandem duplication were dispersed over the entire series; mutationsin the tyrosine kinase domain of FLT3 were not clustered. Likewise,mutations in codon 12, 13, or 61 of the small GTPase RAS (N-RASand K-RAS) had no apparent signatures and did not aggregatein the Correlation View (Figure 1B).
Other Clusters
Specimens from patients with AML with a normal karyotype clusteredinto several subgroups within the assigned clusters (Figure 1B).Most patients in cluster 11 had normal karyotypes and noconsistent additional abnormality. Cluster 5 contained mainlyspecimens from patients with AML of subtype M4 or M5, accordingto the French–American–British (FAB) classification(Figure 1B). Clusters 7, 8, 11, and 14 were not associated witha FAB subtype but had distinct gene-expression profiles.
Class Prediction of Distinct Clusters
We used the PAM method to validate the cluster-specific genesidentified by the SAM method and to determine the minimal numberof genes that can be used to predict karyotypic or other geneticabnormalities with biologic significance in AML (Table 3). The285 specimens were randomly divided into a training set (189specimens) and a validation set (96 specimens). All patientsin the validation set who had favorable cytogenetic findingswere identified with 100 percent accuracy with the use of onlya few genes (Table 3). As expected from the SAM analyses, ETOfor t(8;21), MYH11 for inv(16), and HGF for t(15;17) were amongthe best predictors of the cytogenetic abnormalities (TableR in Supplementary Appendix 1). Cluster 10 (which involved EVI1overexpression) was predicted with a high degree of accuracy,although with a higher 10-fold cross-validation error than thatin the groups with favorable cytogenetic findings. In cluster16 (involving 11q23 abnormalities), samples from 3 of 96 patientswere wrongfully identified in the validation set. Since cluster15 (involving CEBPA mutations) contained few samples, we combinedboth CEBPA-containing clusters. These combined clusters predictedthe presence of CEBPA mutations within the validation set with98 percent accuracy. We were unable to identify a signaturethat reliably identified FLT3 internal tandem duplications.
Table 3. Results of Class Prediction Analysis with the Use of Prediction Analysis of Microarrays.
Survival Analyses
Overall survival, event-free survival, and relapse rates weredetermined among patients whose specimens were within clusterscontaining more than 20 specimens in the Correlation View (clusters5, 9, 10, 12, and 13) (Figure 2). The mean (±SE) actuarialprobabilities of overall survival and event-free survival at60 months were 59±10 percent and 55±11 percent,respectively, among patients with samples in cluster 13; 57±12percent and 47±11 percent, respectively, among thosewith samples in cluster 12; and 72±10 percent and 52±10percent, respectively, among those with samples in cluster 9.Patients with samples in cluster 5 had an intermediate rateof overall survival (32±8 percent) and event-free survival(27±8 percent), whereas survival among patients withsamples in cluster 10 was poorer (the overall survival ratewas 18±9 percent, and the event-free survival rate was6±6 percent), mainly as a result of an increased incidenceof relapse (Figure 2C).
Figure 2. Kaplan–Meier Estimates of Overall Survival (Panel A), Event-free Survival (Panel B), and Relapse Rates after Complete Remission (Panel C) among Patients with AML with Specimens in Clusters 5, 9, 10, 12, and 13.
Cluster 5 was characterized by a French–American–British classification of M4 or M5, cluster 9 by inv(16) abnormalities, cluster 10 by a high level of expression of EVI1, cluster 12 by t(15;17) abnormalities, and cluster 13 by t(8;21) abnormalities. P values were calculated with the use of the log-rank test.
Discussion
In this study of 285 patients with AML that was characterizedby cytogenetic analyses and extensive molecular analyses, weused gene-expression profiling to comprehensively classify thedisorder. This method identified 16 groups on the basis of unsupervisedanalyses involving Pearson's correlation coefficient. Our resultsprovide evidence that each of the assigned clusters representstrue subgroups of AML with specific molecular signatures.
We were able to cluster all cases of AML with t(8;21), inv(16),or t(15;17), including those that had not been identified bycytogenetic examination, into three clusters with unique gene-expressionprofiles. Correlations between gene-expression profiles andprognostically favorable cytogenetic aberrations have been reportedby others,12,13 but we found that these cases can be recognizedwith a high degree of accuracy within a representative cohortof patients with AML.
The SAM and PAM methods were highly concordant for the genesidentified within the assigned clusters, indicating that theseclusters contained discriminative genes. For instance, clusters4 and 15, with overlapping signatures, both included specimenswith normal karyotypes and mutations in CEBPA. Multiple genesappeared to be down-regulated in both clusters but were unaffectedin any other subgroup of AML.
The discriminative genes identified by SAM and PAM may revealfunctional pathways that are critical for the development ofAML. These methods of statistical treatment of the data identifiedseveral genes that are implicated in specific subtypes of AML,such as the interleukin-5 receptor (IL5R) gene in AML witht(8;21) abnormalities25 and FLT3-STAT-5 targets — thegene for interleukin-2 receptor (IL2R)26 and the pim1 kinasegene (PIM1)27 — in AML with FLT3 internal tandem duplicationmutations.
Five clusters (5, 9, 10, 12, and 13) with 20 or more specimenswere evaluated in relation to outcome of disease. As expected,clusters 9 (involving CBF-MYH11), 12 (involving PML-RAR), and13 (involving AML1-ETO) contained specimens with a relativelyfavorable prognosis.
Specimens in cluster 10 had a distinctly poor outcome. A randomlyselected subgroup of patients with specimens in this clustercould be identified with a high degree of accuracy with theuse of a minimal number of genes. The high frequency of poorprognostic markers in this cluster (–7(q), –5(q),t(9;22), or high levels of expression of EVI1) is in accordwith the poor outcome of patients in this cluster. Since thiscluster is heterogeneous with regard to both known poor-riskmarkers and the presence or absence of these markers, the molecularsignature of this cluster may signify a biochemical pathwaythat causes a poor outcome. The fact that normal CD34+ cellssegregate into this cluster suggests that the molecular signatureof treatment resistance resembles that of normal hematopoieticstem cells.
The 44 patients with specimens in cluster 5 had an intermediateduration of survival. Since these specimens were of the FABM4 or M5 subtype, it is possible that genes related to monocytesor macrophages were important in the clustering of these cases.
In three clusters more than 75 percent of specimens had a normalkaryotype (clusters 2, 6, and 11). Most of the patients withspecimens in clusters 2 and 6 had FLT3 internal tandem duplicationmutations, whereas patients with specimens in cluster 11, whichhad a discriminative molecular signature, did not have any consistentmolecular abnormality.
Clusters 1 and 16 harbored 11q23 abnormalities, representingdefects involving the mixed-lineage leukemia (MLL) gene. Thedifferent gene-expression profiles of these two clusters aremost likely due to additional distinctive genetic defects. Incluster 1, this additional abnormality may be a high level ofexpression of the oncogene EVI1, which was not apparent in cluster16. Similarly, distinctive additional genetic defects may explainthe separation of clusters 4 and 15, both of which containedspecimens with CEBPA mutations, clusters 1 and 10, both of whichhad high levels of EVI1 expression, and clusters 8 and 10, bothof which had a high frequency of monosomy 7.
Internal tandem duplications in FLT3 adversely affect the clinicaloutcome.6,7 The molecular signature associated with this abnormalityis not distinctive; however, the clustering of specimens withthese abnormalities within assigned clusters (e.g., cluster12) suggests that these internal tandem duplications resultin different biologic entities within the scope of AML.
Our study demonstrates that cases of AML with known cytogeneticabnormalities and new clusters of AML with characteristic gene-expressionsignatures can be identified with the use of a single assay.The applicability and performance of genome-wide analysis willadvance with the availability of novel whole-genome arrays,improved sequence annotation, and the development of sophisticatedprotocols and software, allowing the analysis of subtle differencesin gene expression and predictions of pathogenic pathways.
Supported by grants from the Dutch Cancer Society (KoninginWilhelmina Fonds) and the Erasmus University Medical Center(Revolving Fund).
We are indebted to Gert J. Ossenkoppele, M.D. (Free UniversityMedical Center, Amsterdam), Edo Vellenga, M.D. (University Hospital,Groningen, the Netherlands), Leo F. Verdonck, M.D. (UniversityHospital, Utrecht, the Netherlands), Gregor Verhoef, M.D. (HospitalGasthuisberg, Leuven, Belgium), and Matthias Theobald, M.D.(Johannes Gutenberg University Hospital, Mainz, Germany), forproviding AML samples; to our colleagues from the bone marrowtransplantation group and molecular diagnostics laboratory forstoring the samples and performing the molecular analyses, respectively;to Guang Chen (Omniviz, Maynard, Mass.); to Elisabeth M.E. Smit(Erasmus Medical Center, Rotterdam, the Netherlands) for cytogeneticanalyses; to Wim L.J. van Putten, Ph.D. (Erasmus Medical Center,Rotterdam, the Netherlands), for statistical analyses; to IvoP. Touw, Ph.D. (Erasmus Medical Center, Rotterdam, the Netherlands),for helpful discussions; and to Eveline Mank (Leiden GenomeTechnology Center, Leiden, the Netherlands) for initial technicalassistance.
Source Information
From the Departments of Hematology (P.J.M.V., R.G.W.V., M.A.B., C.A.J.E., S.B.W.D.-K., B.L., R.D.), Clinical Genetics (H.B.B.), and Bioinformatics (M.J.M., P.J.S.), Erasmus University Medical Center, Rotterdam; and the Leiden Genome Technology Center and the Center for Human and Clinical Genetics, Leiden University Medical Center, Leiden (J.M.B.) — both in the Netherlands.
Address reprint requests to Dr. Valk at Erasmus University Medical Center Rotterdam, Department of Hematology, Ee13, Dr. Molewaterplein 50, 3015 GE Rotterdam Z-H, the Netherlands, or at p.valk{at}erasmusmc.nl.
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(2009). Identification of AML1-ETO modulators by chemical genomics. Blood
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(2009). The Noncanonical WNT Pathway Is Operative in Idiopathic Pulmonary Arterial Hypertension. Am. J. Respir. Cell Mol. Bio.
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(2009). A retroviral mutagenesis screen reveals strong cooperation between Bcl11a overexpression and loss of the Nf1 tumor suppressor gene. Blood
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(2009). A decade of genome-wide gene expression profiling in acute myeloid leukemia: flashback and prospects. Blood
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(2008). Distinct microRNA expression profiles in acute myeloid leukemia with common translocations. Proc. Natl. Acad. Sci. USA
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(2008). MN1 affects expression of genes involved in hematopoiesis and can enhance as well as inhibit RAR/RXR-induced gene expression. Carcinogenesis
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Mao, X., Liang, S.-b., Hurren, R., Gronda, M., Chow, S., Xu, G. W., Wang, X., Zavareh, R. B., Jamal, N., Messner, H., Hedley, D. W., Datti, A., Wrana, J. L., Zhu, Y., Shi, C.-x., Lee, K., Tiedemann, R., Trudel, S., Stewart, A. K., Schimmer, A. D.
(2008). Cyproheptadine displays preclinical activity in myeloma and leukemia. Blood
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(2008). Clinical Significance of the Most Common Chromosome Translocations in Adult Acute Myeloid Leukemia. J Natl Cancer Inst Monogr
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Hackanson, B., Bennett, K. L., Brena, R. M., Jiang, J., Claus, R., Chen, S.-S., Blagitko-Dorfs, N., Maharry, K., Whitman, S. P., Schmittgen, T. D., Lubbert, M., Marcucci, G., Bloomfield, C. D., Plass, C.
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(2008). Mutations and Treatment Outcome in Cytogenetically Normal Acute Myeloid Leukemia. NEJM
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(2008). An FLT3 gene-expression signature predicts clinical outcome in normal karyotype AML. Blood
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(2008). High EVI1 levels predict adverse outcome in acute myeloid leukemia: prevalence of EVI1 overexpression and chromosome 3q26 abnormalities underestimated. Blood
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(2008). Gaining confidence in biological interpretation of the microarray data: the functional consistence of the significant GO categories. Bioinformatics
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(2008). Acute Myeloid Leukemia: The Challenge of Capturing Disease Variety. ASH Education Book
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(2007). Genomic and Nongenomic Effects of Estrogen Signaling in Human Endometrial Cells: Involvement of the Growth Factor Receptor Signaling Downstream AKT Pathway. Reproductive Sciences
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(2007). MN1 overexpression induces acute myeloid leukemia in mice and predicts ATRA resistance in patients with AML. Blood
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(2007). Gene-expression profiling identifies distinct subclasses of core binding factor acute myeloid leukemia. Blood
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Yuan, W., Payton, J. E., Holt, M. S., Link, D. C., Watson, M. A., DiPersio, J. F., Ley, T. J.
(2007). Commonly dysregulated genes in murine APL cells. Blood
109: 961-970
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Mrozek, K., Marcucci, G., Paschka, P., Whitman, S. P., Bloomfield, C. D.
(2007). Clinical relevance of mutations and gene-expression changes in adult acute myeloid leukemia with normal cytogenetics: are we ready for a prognostically prioritized molecular classification?. Blood
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Bull, T. M., Coldren, C. D., Geraci, M. W., Voelkel, N. F.
(2007). Gene Expression Profiling in Pulmonary Hypertension. Proc Am Thorac Soc
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Testa, U., Riccioni, R.
(2007). Deregulation of apoptosis in acute myeloid leukemia. haematol
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Zimpfer, A., Schonberg, S., Lugli, A., Agostinelli, C., Pileri, S. A, Went, P., Dirnhofer, S.
(2007). Construction and validation of a bone marrow tissue microarray. J. Clin. Pathol.
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Wouters, B. J., Louwers, I., Valk, P. J. M., Lowenberg, B., Delwel, R.
(2007). A recurrent in-frame insertion in a CEBPA transactivation domain is a polymorphism rather than a mutation that does not affect gene expression profiling-based clustering of AML. Blood
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Pasqualucci, L., Liso, A., Martelli, M. P., Bolli, N., Pacini, R., Tabarrini, A., Carini, M., Bigerna, B., Pucciarini, A., Mannucci, R., Nicoletti, I., Tiacci, E., Meloni, G., Specchia, G., Cantore, N., Di Raimondo, F., Pileri, S., Mecucci, C., Mandelli, F., Martelli, M. F., Falini, B.
(2006). Mutated nucleophosmin detects clonal multilineage involvement in acute myeloid leukemia: impact on WHO classification. Blood
108: 4146-4155
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Greiner, J., Schmitt, M., Li, L., Giannopoulos, K., Bosch, K., Schmitt, A., Dohner, K., Schlenk, R. F., Pollack, J. R., Dohner, H., Bullinger, L.
(2006). Expression of tumor-associated antigens in acute myeloid leukemia: implications for specific immunotherapeutic approaches. Blood
108: 4109-4117
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Heilman, S. A., Kuo, Y.-H., Goudswaard, C. S., Valk, P. J., Castilla, L. H.
(2006). Cbf{beta} Reduces Cbf{beta}-SMMHC-Associated Acute Myeloid Leukemia in Mice. Cancer Res.
66: 11214-11218
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D'Andrea, R., Perugini, M., Kok, C., Wilkinson, C., Brown, A., Gonda, T.
(2006). Common Leukemic Signaling Pathways Identified by Comparative Analysis of GM-CSF and FLT3 Activated Receptor Mutations.. ASH ANNUAL MEETING ABSTRACTS
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[Abstract]
Zhan, F., Huang, Y., Colla, S., Stewart, J. P., Hanamura, I., Gupta, S., Epstein, J., Yaccoby, S., Sawyer, J., Burington, B., Anaissie, E., Hollmig, K., Pineda-Roman, M., Tricot, G., van Rhee, F., Walker, R., Zangari, M., Crowley, J., Barlogie, B., Shaughnessy, J. D. Jr
(2006). The molecular classification of multiple myeloma. Blood
108: 2020-2028
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Sunde, J. S., Donninger, H., Wu, K., Johnson, M. E., Pestell, R. G., Rose, G. S., Mok, S. C., Brady, J., Bonome, T., Birrer, M. J.
(2006). Expression Profiling Identifies Altered Expression of Genes That Contribute to the Inhibition of Transforming Growth Factor-{beta} Signaling in Ovarian Cancer.. Cancer Res.
66: 8404-8412
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Wunderlich, M., Krejci, O., Wei, J., Mulloy, J. C.
(2006). Human CD34+ cells expressing the inv(16) fusion protein exhibit a myelomonocytic phenotype with greatly enhanced proliferative ability. Blood
108: 1690-1697
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Radmacher, M. D., Marcucci, G., Ruppert, A. S., Mrozek, K., Whitman, S. P., Vardiman, J. W., Paschka, P., Vukosavljevic, T., Baldus, C. D., Kolitz, J. E., Caligiuri, M. A., Larson, R. A., Bloomfield, C. D.
(2006). Independent confirmation of a prognostic gene-expression signature in adult acute myeloid leukemia with a normal karyotype: a Cancer and Leukemia Group B study. Blood
108: 1677-1683
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Brown, A. L., Wilkinson, C. R., Waterman, S. R., Kok, C. H., Salerno, D. G., Diakiw, S. M., Reynolds, B., Scott, H. S., Tsykin, A., Glonek, G. F., Goodall, G. J., Solomon, P. J., Gonda, T. J., D'Andrea, R. J.
(2006). Genetic regulators of myelopoiesis and leukemic signaling identified by gene profiling and linear modeling. J. Leukoc. Biol.
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Camos, M., Esteve, J., Jares, P., Colomer, D., Rozman, M., Villamor, N., Costa, D., Carrio, A., Nomdedeu, J., Montserrat, E., Campo, E.
(2006). Gene Expression Profiling of Acute Myeloid Leukemia with Translocation t(8;16)(p11;p13) and MYST3-CREBBP Rearrangement Reveals a Distinctive Signature with a Specific Pattern of HOX Gene Expression.. Cancer Res.
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Wilson, C. S., Davidson, G. S., Martin, S. B., Andries, E., Potter, J., Harvey, R., Ar, K., Xu, Y., Kopecky, K. J., Ankerst, D. P., Gundacker, H., Slovak, M. L., Mosquera-Caro, M., Chen, I-M., Stirewalt, D. L., Murphy, M., Schultz, F. A., Kang, H., Wang, X., Radich, J. P., Appelbaum, F. R., Atlas, S. R., Godwin, J., Willman, C. L.
(2006). Gene expression profiling of adult acute myeloid leukemia identifies novel biologic clusters for risk classification and outcome prediction. Blood
108: 685-696
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Takeda, A., Goolsby, C., Yaseen, N. R.
(2006). NUP98-HOXA9 Induces Long-term Proliferation and Blocks Differentiation of Primary Human CD34+ Hematopoietic Cells.. Cancer Res.
66: 6628-6637
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Monzo, M., Brunet, S., Urbano-Ispizua, A., Navarro, A., Perea, G., Esteve, J., Artells, R., Granell, M., Berlanga, J., Ribera, J. M., Bueno, J., Llorente, A., Guardia, R., Tormo, M., Torres, P., Nomdedeu, J. F., Montserrat, E., Sierra, J., for CETLAM,
(2006). Genomic polymorphisms provide prognostic information in intermediate-risk acute myeloblastic leukemia. Blood
107: 4871-4879
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Bloomfield, C. D., Mrozek, K., Caligiuri, M. A.
(2006). Cancer and Leukemia Group B Leukemia Correlative Science Committee: Major Accomplishments and Future Directions.. Clin. Cancer Res.
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Zeng, Z., Samudio, I. J., Zhang, W., Estrov, Z., Pelicano, H., Harris, D., Frolova, O., Hail, N. Jr., Chen, W., Kornblau, S. M., Huang, P., Lu, Y., Mills, G. B., Andreeff, M., Konopleva, M.
(2006). Simultaneous Inhibition of PDK1/AKT and Fms-Like Tyrosine Kinase 3 Signaling by a Small-Molecule KP372-1 Induces Mitochondrial Dysfunction and Apoptosis in Acute Myelogenous Leukemia.. Cancer Res.
66: 3737-3746
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Ge, Y., Dombkowski, A. A., LaFiura, K. M., Tatman, D., Yedidi, R. S., Stout, M. L., Buck, S. A., Massey, G., Becton, D. L., Weinstein, H. J., Ravindranath, Y., Matherly, L. H., Taub, J. W.
(2006). Differential gene expression, GATA1 target genes, and the chemotherapy sensitivity of Down syndrome megakaryocytic leukemia. Blood
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Baldus, C. D., Thiede, C., Soucek, S., Bloomfield, C. D., Thiel, E., Ehninger, G.
(2006). BAALC Expression and FLT3 Internal Tandem Duplication Mutations in Acute Myeloid Leukemia Patients With Normal Cytogenetics: Prognostic Implications. JCO
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Lee, S., Chen, J., Zhou, G., Shi, R. Z., Bouffard, G. G., Kocherginsky, M., Ge, X., Sun, M., Jayathilaka, N., Kim, Y. C., Emmanuel, N., Bohlander, S. K., Minden, M., Kline, J., Ozer, O., Larson, R. A., LeBeau, M. M., Green, E. D., Trent, J., Karrison, T., Liu, P. P., Wang, S. M., Rowley, J. D.
(2006). Gene expression profiles in acute myeloid leukemia with common translocations using SAGE. Proc. Natl. Acad. Sci. USA
103: 1030-1035
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Erkeland, S. J., Verhaak, R. G.W., Valk, P. J.M., Delwel, R., Lowenberg, B., Touw, I. P.
(2006). Significance of Murine Retroviral Mutagenesis for Identification of Disease Genes in Human Acute Myeloid Leukemia. Cancer Res.
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Pitarch, A., Jimenez, A., Nombela, C., Gil, C.
(2006). Decoding Serological Response to Candida Cell Wall Immunome into Novel Diagnostic, Prognostic, and Therapeutic Candidates for Systemic Candidiasis by Proteomic and Bioinformatic Analyses. Mol. Cell. Proteomics
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Mrozek, K., Bloomfield, C. D.
(2006). Chromosome Aberrations, Gene Mutations and Expression Changes, and Prognosis in Adult Acute Myeloid Leukemia. ASH Education Book
2006: 169-177
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Andersson, A., Olofsson, T., Lindgren, D., Nilsson, B., Ritz, C., Eden, P., Lassen, C., Rade, J., Fontes, M., Morse, H., Heldrup, J., Behrendtz, M., Mitelman, F., Hoglund, M., Johansson, B., Fioretos, T.
(2005). Molecular signatures in childhood acute leukemia and their correlations to expression patterns in normal hematopoietic subpopulations. Proc. Natl. Acad. Sci. USA
102: 19069-19074
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Gielen, S C J P, Kuhne, L C M, Ewing, P C, Blok, L J, Burger, C W
(2005). Tamoxifen treatment for breast cancer enforces a distinct gene-expression profile on the human endometrium: an exploratory study. Endocr Relat Cancer
12: 1037-1049
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Verhaak, R. G. W., Goudswaard, C. S., van Putten, W., Bijl, M. A., Sanders, M. A., Hugens, W., Uitterlinden, A. G., Erpelinck, C. A. J., Delwel, R., Lowenberg, B., Valk, P. J. M.
(2005). 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
106: 3747-3754
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Tomlins, S. A., Rhodes, D. R., Perner, S., Dhanasekaran, S. M., Mehra, R., Sun, X.-W., Varambally, S., Cao, X., Tchinda, J., Kuefer, R., Lee, C., Montie, J. E., Shah, R. B., Pienta, K. J., Rubin, M. A., Chinnaiyan, A. M.
(2005). Recurrent Fusion of TMPRSS2 and ETS Transcription Factor Genes in Prostate Cancer. Science
310: 644-648
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Paz-Priel, I., Cai, D. H., Wang, D., Kowalski, J., Blackford, A., Liu, H., Heckman, C. A., Gombart, A. F., Koeffler, H. P., Boxer, L. M., Friedman, A. D.
(2005). CCAAT/Enhancer Binding Protein {alpha} (C/EBP{alpha}) and C/EBP{alpha} Myeloid Oncoproteins Induce Bcl-2 via Interaction of Their Basic Regions with Nuclear Factor-{kappa}B p50. Mol Cancer Res
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Bullinger, L., Valk, P. J.M.
(2005). Gene Expression Profiling in Acute Myeloid Leukemia. JCO
23: 6296-6305
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Marcucci, G., Mrozek, K., Ruppert, A. S., Maharry, K., Kolitz, J. E., Moore, J. O., Mayer, R. J., Pettenati, M. J., Powell, B. L., Edwards, C. G., Sterling, L. J., Vardiman, J. W., Schiffer, C. A., Carroll, A. J., Larson, R. A., Bloomfield, C. D.
(2005). Prognostic Factors and Outcome of Core Binding Factor Acute Myeloid Leukemia Patients With t(8;21) Differ From Those of Patients With inv(16): A Cancer and Leukemia Group B Study. JCO
23: 5705-5717
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Haferlach, T., Kohlmann, A., Schnittger, S., Dugas, M., Hiddemann, W., Kern, W., Schoch, C.
(2005). Global approach to the diagnosis of leukemia using gene expression profiling. Blood
106: 1189-1198
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Alcalay, M., Tiacci, E., Bergomas, R., Bigerna, B., Venturini, E., Minardi, S. P., Meani, N., Diverio, D., Bernard, L., Tizzoni, L., Volorio, S., Luzi, L., Colombo, E., Lo Coco, F., Mecucci, C., Falini, B., Pelicci, P. G., for the Gruppo Italiano Malattie Ematologiche Mali,
(2005). 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
106: 899-902
[Abstract][Full Text]
-MYH11
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