Current Opinion in Hematology:
Myeloid disease: Edited by Martin S. Tallman
Molecular signatures in acute myeloid leukemia
Mrózek, Krzysztofa; Radmacher, Michael Da,b; Bloomfield, Clara Da; Marcucci, Guidoa,c
aDivision of Hematology and Oncology, Department of Internal Medicine, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA
bThe Cancer and Leukemia Group B (CALGB) Statistical Center, Duke University Medical Center, Durham, North Carolina, USA
cDivision of Human Cancer Genetics, Department of Microbiology, Virology, Immunology and Medical Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA
Correspondence to Krzysztof Mrózek, Division of Hematology and Oncology, the Comprehensive Cancer Center, Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, Room 1248B, The Ohio State University, 300 West Tenth Avenue, Columbus, OH 43210-1228, USA Tel: +1 614 293 3150; fax: +1 614 293 3575; e-mail: email@example.com
Purpose of review: Acute myeloid leukemia (AML) is characterized by a high degree of heterogeneity with respect to chromosome abnormalities, gene mutations and changes in expression of multiple genes and microRNAs. In this article, we review the results of recent studies of AML that used microarray-based genome-wide gene-expression and microRNA-expression profiling.
Recent findings: Genome-wide analyses of gene expression and microRNA expression have revealed AML signatures that are closely associated with some, but not all, cytogenetic and molecular genetic subsets, helped in identification of novel biologic subtypes and led to characterization of molecular pathways involved in leukemogenesis. For some AML categories, namely core-binding factor AML and/or cytogenetically normal AML, gene-expression and microRNA-expression profiling provided prognostic information additional to that obtained from cytogenetics and analyses of gene mutations and single gene expression changes.
Summary: Gene-expression and microRNA-expression profiling not only has the potential to enhance our understanding of the disease biology, but also appears to constitute an applicable approach for outcome prediction and identification of novel therapeutic targets.
Acute myeloid leukemia (AML) arises as a result of accumulation of acquired genetic alterations occurring in hematopoietic progenitor cells and altering their growth, differentiation and proliferation [1–4,5••,6,7,8••]. The AML heterogeneity at both the cytogenetic and molecular genetic levels has been associated with pretreatment features and clinical outcome of AML patients. The cytogenetic and molecular genetic aberrations associated with AML are not mutually exclusive and often coexist in the leukemic cells and concurrently affect the expression of downstream target genes that encode proteins involved in complex biologic networks supporting leukemogenesis. It is anticipated that microarray genome-wide gene-expression profiling (GEP) and microRNA-expression profiling assays are well suited to reveal characteristic patterns (signatures) of activation or silencing or both of multiple genes or microRNAs that reflect the underlying biology of the disease subtypes, provide diagnostic and prognostic information, and potentially reveal novel molecular targets for therapeutic intervention. Recent reports have shown high interlaboratory reproducibility and standardization of GEP of leukemia samples, thereby demonstrating the routine applicability of this approach for prospective studies of AML patients [9,10].
Usefulness of gene-expression profiling for the diagnosis of patients with acute myeloid leukemia
Several recurrent chromosome abnormalities and their molecular equivalents, such as t(8;21)(q22;q22)/RUNX1-RUNX1T1, inv(16)(p13q22) or t(16;16)(p13;q22)/CBFB-MYH11, t(15;17)(q22;q12)/PML-RARA, t(9;11)(p22;q23)/MLLT3-MLL and other balanced abnormalities involving 11q23/MLL, t(6;9)(p23;q34)/DEK-NUP214, inv(3)(q21q26.2) or t(3;3)(q21;q26.2)/RPN1-EVI1 and t(1;22)(p13;q13)/RBM15-MKL1, are now included in the 2008 World Health Organization (WHO) classification of de-novo AML, and together with morphology, immunophenotype and clinical features are being used to define different disease entities . Moreover, de-novo AML with mutations in distinct genes such as CEBPA and NPM1 have been included as provisional entities in the 2008 WHO classification.
The aforementioned recurrent chromosome aberrations and gene mutations are also recognized to be prognostically relevant in addition to other molecular alterations such as FLT3 internal tandem duplication (FLT3-ITD) and FLT3 tyrosine kinase domain (FLT3-TKD) and KIT, WT1 and MLL mutations, and CXCR4, ERG, BAALC and MN1 overexpression [2,12–18]. To date, in order to identify and prioritize all these alterations for diagnosis and risk-adapted stratification of AML patients, pretreatment samples are subjected to a variety of assays. Therefore, it is reasonable to raise the question of whether GEP as a single assay is readily applicable for diagnosis and outcome class prediction in AML and can substitute for the current multitest diagnostic and prognostic work-up.
GEP has been proven successful in distinguishing AML patients from those with acute lymphoblastic leukemia  and, among the AML patients, those with prognostically favorable inv(16)/t(16;16), t(8;21) and t(15;17) from other cytogenetic/molecular subsets. Patients with prognostically favorable karyotypes were accurately identified with 100% sensitivity and specificity in most recent studies [20–22,23•]. Notably, in a study by Verhaak et al. [23•], four of 37 patients with inv(16) and four of 25 patients with t(15;17) were correctly identified by GEP as confirmed by reverse transcriptase-PCR, but not routine cytogenetic analysis. However, the accuracy of prediction for other cytogenetic AML subsets, such as those with abnormalities involving band 11q23, abnormalities involving 3q, -5/5q-, -7/7q- or t(9;22), has been much lower [22,23•,24]. Similarly, the prediction accuracy for specific molecular subsets of patients such as those harboring FLT3-ITD, FLT3-TKD and mutations in the NRAS and KRAS genes [23•] was quite disappointing. In another study, restricted to patients with cytogenetically normal AML (CN-AML) [25•], a 20-gene classifier signature for FLT3-ITD mutation status had only moderate classification accuracy, with 73% sensitivity and 85% specificity. Furthermore, although patients with NPM1 mutations were recognized based on an HOX and TALE gene-specific signature [26,27], this signature was prone to generate false-positive results. Patients with 11q23/MLL abnormalities, known to be associated with increased expression of HOX genes , were often misclassified, as were all patients with t(6;9) [23•]. In pediatric AML, however, it seems possible to distinguish patients with NPM1 mutations from those with 11q23/MLL translocations based on different patterns of HOX gene expression .
Altogether, these results support the notion that although genome-wide gene-expression analyses can accurately predict AML with t(8;21), inv(16)/t(16;16) and t(15;17), they are less successful in predicting other cytogenetic and molecular genetic subsets of AML. Therefore, at present, the usefulness of GEP for AML diagnosis appears limited, although large, multiinstitutional collaborative studies are planned to develop, refine and validate diagnostic GEP-based classifiers for less frequent cytogenetic or distinct molecular AML subtypes [30••].
Gene-expression profiling to identify distinct prognostic subsets in specific acute myeloid leukemia subtypes
Microarray GEP has been applied to study specific AML categories to derive signatures that would identify subsets of patients with differing outcomes. Initial studies showed that gene-expression signatures predictive of outcome among AML patients have strong associations with other cytogenetic or molecular prognostic factors, thereby raising the question of whether GEP could provide predictive information in addition to that already offered by standard cytogenetics or single gene assays or both. More recently, GEP has been adapted to identify subsets of AML patients with diversified outcome within subgroups of patients with already specified cytogenetic and molecular alterations. One of these subgroups is core-binding factor (CBF) AML that includes patients with t(8;21) or inv(16)/t(16;16) [31,32]. Bullinger et al.  used unsupervised two-way hierarchical cluster analysis to stratify 93 CBF-AML patients into two groups with dissimilar gene-expression profiles and significantly different survival. Patients who clustered in the group with worse survival more often harbored inv(16) and had higher white blood counts (WBC) than the remaining patients. Although four patients found to carry the prognostically adverse FLT3-ITD were included in the group with worse outcome, in general patients did not seem to cluster based on known prognostic factors in CBF-AML such as KIT mutations or secondary trisomy 22 in inv(16) patients . Patients with worse outcome showed overexpression of genes controlling cell proliferation and downregulation of those involved in apoptosis . In another GEP study, Paschka et al.  dichotomized patients with inv(16)/t(16;16) as a sole chromosome abnormality and no mutations in the KIT gene, and, separately, t(8;21) patients without KIT mutations into subsets with significantly different event-free survival (EFS) based on two different gene-expression signatures. These results, however, require corroboration.
Another AML subset in which GEP has been relatively successful for prognostication is CN-AML, the largest subset of de-novo AML comprising 40–45% of patients . The first study showing that GEP can divide CN-AML patients into prognostically relevant subgroups was from Bullinger et al. . Subsequently, Radmacher et al.  validated the prognostic significance of the ‘Bullinger’ signature in a larger group of CN-AML patients dissimilarly treated, with a longer follow-up and using a different microarray platform. Despite these differences, the signature was validated using a class prediction algorithm and supported the clinical applicability of this approach for AML prognostication. However, the prediction accuracy of the classifier was overall modest, with approximately 60% of the patients having their outcome predicted correctly .
A more recent GEP study  reported a signature capable of predicting outcome in CN-AML. The signature was developed using supervised principal component analysis from GEP data obtained from a training set of 163 intensively treated German CN-AML patients, and consisted of 86 probe sets representing 66 different genes that were significantly associated with survival. A prognostic score based on this signature was defined and then applied to an independent test set of 79 German CN-AML patients. Despite clinical and treatment differences, the prognostic score was shown to be an independent predictor for outcome in multivariable analyses of the test set after adjustments for age, FLT3-ITD/FLT3-wild-type allelic ratio and NPM1 mutation status. Likewise, the prognostic score retained its predictive significance in a multivariable model for survival of a validation cohort of 64 younger (age <60 years) CN-AML patients enrolled on Cancer and Leukemia Group B protocols. These data demonstrated that the performance of the GEP-based predictor was robust in CN-AML patient populations even when they came from different continents (Europe and America) and differed with regard to pretreatment features and treatment received .
Interestingly, both the signatures developed by Metzeler et al.  and that validated by Radmacher et al.  strongly correlated with FLT3-ITD status. However, the fact that in multivariable analyses, the ‘Metzeler’ signature was found to be independently associated with outcome after adjusting for FLT3-ITD status suggests that additional prognostic information can be provided by the GEP. Indeed, this finding is consistent with a report by Bullinger et al. [25•] showing that a GEP-based predictor of FLT3-ITD status in CN-AML misclassified roughly only 20% of patients and outperformed FLT3-ITD status in its ability to predict outcome.
Gene-expression profiling to delineate biologic pathways
GEP has also been instrumental for the discovery of novel, biologic subgroups of AML. CEBPA encodes a transcription factor involved in myeloid differentiation, and mutations of this gene have been associated mostly with CN-AML and predict favorable outcome. Wouters et al.  identified a subset of AML patients who did not harbor CEBPA mutations, but were characterized by a GEP signature resembling that of AML patients with these mutations. Further investigations revealed that in these cases, CEBPA was silenced epigenetically, mostly through promoter hypermethylation, and the leukemic blasts exhibited expression of genes associated with aberrantly activated Notch signaling and NOTCH1 mutations.
In another recent study, Marcucci et al.  derived a gene-expression signature associated with CEBPA mutations in a high-risk molecular group of CN-AML, that is, in patients with wild-type NPM1 genes with or without FLT3-ITD or those with NPM1 mutation and FLT3-ITD. In addition to overexpression of CD34, CD38 and CD7, a prominent characteristic of this signature was the upregulation of genes involved in erythroid differentiation (e.g. GATA1, ZFPM1, HEMGN, EPOR, TFRC), and genes encoding erythrocyte membrane proteins and hemoglobin chains. These findings were consistent with higher hemoglobin levels observed in patients with CEBPA mutations at diagnosis. In contrast, genes involved in myeloid differentiation (e.g. RUNX1, SPI1, ID1) were downregulated in patients with CEBPA mutations, as were several members of the homeobox family. Concurrent downregulation of HOX genes and upregulation of CD34 and CD38 and genes involved in erythroid lineage differentiation suggest that cells harboring CEBPA mutations represent a more functionally mature type of malignant blasts and, perhaps, this may explain their increased sensitivity to chemotherapy . Furthermore, these findings were consistent with the reported association of CEBPA mutations with NPM1 wild-type rather than NPM1 mutations, which are usually accompanied by HOX gene overexpression.
MicroRNA-expression profiling to identify cytogenetic acute myeloid leukemia subtypes
Recent studies analyzed genome-wide expression of microRNAs for both biology discovery and clinical outcome prediction. MicroRNAs are naturally occurring, small RNAs, 19–25 nucleotides in length, cleaved from 70–100 nucleotide hairpin precursors, that hybridize to complementary mRNA targets and inhibit their translation or cause degradation . Initially discovered as regulators of normal cell homeostasis, microRNAs have recently been shown to be frequently located at cancer-associated genomic regions  and to represent a new class of genes that play a role in malignant transformation [43,44].
MicroRNA-expression profiling has been shown to be able to readily distinguish between AML and acute lymphoblastic leukemia  or AML blasts from normal CD34-positive blasts . Furthermore, microRNA-expression profiling studies have demonstrated that microRNA signatures can also distinguish between cytogenetic subtypes of AML, with AML patients who carry t(8;21), inv(16) or t(15;17) having unique microRNA-expression signatures [7,47]. Similar to GEP studies, although there was not a perfect concordance between the signatures derived from various studies, some commonalities emerged, such as upregulation of several microRNAs transcribed from genes located in the imprinted region within the 14q32 band in acute promyelocytic leukemia with t(15;17) [7,47,48] and the downregulation of miR-133a in CBF-AML with t(8;21) [7,47]. Patterns of microRNA expression associated with trisomy 8, rearrangements of 11q23/MLL, and CN-AML have also been reported [6,48].
Abnormal microRNA-expression signatures have also been associated with distinct molecular aberrations in AML. Upregulation of miR-155 in patients with FLT3-ITD has been independently reported by two research groups [6,47,49]. This observation is consistent with the reported high blast proliferation and decreased survival in FLT3-ITD-positive AML; in a mouse model, sustained expression of miR-155 was shown to drive granulocyte/monocyte expansion and cause pathological features characteristic of myeloid neoplasia [50•].
Mutations of the NPM1 gene have been associated with a characteristic microRNA-expression signature that includes the upregulation of miR-10a, miR-10b and miR-196a . Interestingly, these microRNAs reside in the genomic cluster of HOX genes, whose upregulation is a prominent feature of gene-expression signatures in patients with NPM1 mutations. Consistent with this observation, expression of miR-10a, miR-10b and miR-196a-1 has been correlated with HOX gene expression in CN-AML by Debernardi et al. . Interestingly, among the downregulated microRNAs in the NPM1-associated microRNA signature, miR-204 and miR-128 were found. Both of these microRNAs are predicted to target HOX genes, as confirmed by Garzon et al.  in cell line experiments showing that miR-204 inhibits expression of HOXA10 and MEIS1.
Upregulation of mir-181a and miR-335 in AML patients with CEBPA mutations was observed in two studies [40,47], one of which was restricted to CN-AML . In another study , miR-124a was demonstrated to target CEBPA mRNA in vitro, and it was shown to be epigenetically silenced in leukemic cell lines.
Finally, expression of miR-181a  and miR-181b  has been associated with French American British (FAB) marrow morphology of CN-AML patients, with higher expression of these microRNAs detected in FAB M1 and M2 compared with FAB M4 and M5 subtypes.
Associations of microRNA-expression profiles with clinical outcome
Recently, microRNA expression in AML has begun to be associated with clinical outcome. Overexpression of miR-199a and miR-191 negatively affected survival and EFS across all cytogenetic subgroups in univariable analyses and retained its adverse prognostic influence in a multivariable model together with cytogenetics . Dixon-McIver et al.  reported that expression of miR-9 and miR-let7b was correlated with cytogenetic risk groups: the expression was low in the favorable and high in intermediate and adverse cytogenetic risk groups.
In CN-AML with high-risk molecular features (i.e. with FLT3-ITD, wild-type NPM1, or both), we have recently identified a microRNA-expression signature associated with EFS and validated its prognostic relevance in an independent patient set [8••]. The important characteristic of this signature was an inverse association between expression of miR-181a and miR-181b and risk of an event. In this study, the genome-wide microRNA-expression profile was also correlated with gene expression data in an attempt to identify micro-RNA-regulated genes that contribute to leukemogenesis in the high-risk CN-AML subset. Expression levels of 452 genes significantly correlated with the prognostic microRNA signature. Several genes involved in mechanisms of innate immunity, including genes encoding Toll-like receptors (TLR2, TLR4, TLR8), interleukin-1-β (IL1B) and its regulators (CARD8, NLRC4, NOD2, PYCARD, CASP1) [53••], were overly represented in the microRNA-dependent gene-expression signature. Proteins encoded by some of these genes have been reported to maintain growth and proliferation of AML blasts and may represent suitable therapeutic targets [54,55].
Recent genome-wide analyses of gene expression and microRNA expression have revealed signatures that are closely associated with some, but not all, cytogenetic and molecular genetic subsets of AML. For CBF-AML and CN-AML, GEP has provided prognostic information additional to that obtained from analyses of leukemia karyotypes, gene mutations and changes in expression of specific genes. However, further studies should prospectively validate many of the results reviewed in this article. Importantly, GEP and microRNA-expression profiling, in addition to or in combination with such other techniques as single nucleotide polymorphism arrays , array-based comparative genomic hybridization  and genome sequencing , are invaluable for discovery of the molecular pathways involved in leukemogenesis that merit further investigation as potential targets for development of novel, personalized therapeutic approaches that would depend on the genetic make-up of the disease in individual patients.
The present study is supported in part by National Cancer Institute, Bethesda, Maryland (MD) grants CA16058 and R01CA102031, and the Coleman Leukemia Research Foundation.
References and recommended reading
Papers of particular interest, published within the annual period of review, have been highlighted as:
• of special interest
•• of outstanding interest
Additional references related to this topic can also be found in the Current World Literature section in this issue (p. 148).
1 Estey E, Döhner H. Acute myeloid leukaemia. Lancet 2006; 368:1894–1907.
2 Mrózek K, Marcucci G, Paschka P, et al. 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 2007; 109:431–448.
3 Döhner K, Döhner H. Molecular characterization of acute myeloid leukemia. Haematologica 2008; 93:976–982.
4 Fröhling S, Döhner H. Chromosomal abnormalities in cancer. N Engl J Med 2008; 359:722–734.
5•• Schlenk RF, Döhner K, Krauter J, et al. Mutations and treatment outcome in cytogenetically normal acute myeloid leukemia. N Engl J Med 2008; 358:1909–1918.
6 Garzon R, Volinia S, Liu C-G, et al. MicroRNA signatures associated with cytogenetics and prognosis in acute myeloid leukemia. Blood 2008; 111:3183–3189.
7 Dixon-McIver A, East P, Mein CA, et al. Distinctive patterns of microRNA expression associated with karyotype in acute myeloid leukaemia. PLoS ONE 2008; 3:e2141.
8•• Marcucci G, Radmacher MD, Maharry K, et al. MicroRNA expression in cytogenetically normal acute myeloid leukemia. N Engl J Med 2008; 358:1919–1928.
9 Kohlmann A, Haschke-Becher E, Wimmer B, et al. Intraplatform reproducibility and technical precision of gene expression profiling in 4 laboratories investigating 160 leukemia samples: the DACH study. Clin Chem 2008; 54:1705–1715.
10 Kohlmann A, Kipps TJ, Rassenti LZ, et al. An international standardization programme towards the application of gene expression profiling in routine leukaemia diagnostics: the Microarray Innovations in LEukemia study prephase. Br J Haematol 2008; 142:802–807.
11 Arber DA, Vardiman JW, Brunning RD, et al. Acute myeloid leukaemia with recurrent genetic abnormalities. In: Swerdlow SH, Campo E, Harris NL, et al., editors. WHO classification of tumours of haematopoietic and lymphoid tissues. Lyon: IARC Press; 2008. pp. 110–123.
12 Byrd JC, Mrózek 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; 100:4325–4336.
13 Paschka P, Marcucci G, Ruppert AS, et al. Wilms' tumor 1 gene mutations independently predict poor outcome in adults with cytogenetically normal acute myeloid leukemia: a Cancer and Leukemia Group B study. J Clin Oncol 2008; 26:4595–4602.
14 Virappane P, Gale R, Hills R, et al. Mutation of the Wilms' tumor 1 gene is a poor prognostic factor associated with chemotherapy resistance in normal karyotype acute myeloid leukemia: the United Kingdom Medical Research Council Adult Leukaemia Working Party. J Clin Oncol 2008; 26:5429–5435.
15 Spoo AC, Lübbert M, Wierda WG, Burger JA. CXCR4 is a prognostic marker in acute myelogenous leukemia. Blood 2007; 109:786–791.
16 Marcucci G, Maharry K, Whitman SP, et al. High expression levels of the ETS-related gene, ERG, predict adverse outcome and improve molecular risk-based classification of cytogenetically normal acute myeloid leukemia: a Cancer and Leukemia Group B study. J Clin Oncol 2007; 25:3337–3343.
17 Langer C, Radmacher MD, Ruppert AS, et al. High BAALC expression associates with other molecular prognostic markers, poor outcome, and a distinct gene-expression signature in cytogenetically normal patients younger than 60 years with acute myeloid leukemia: a Cancer and Leukemia Group B (CALGB) study. Blood 2008; 111:5371–5379.
18 Heuser M, Beutel G, Krauter J, et al. High meningioma 1 (MN1) expression as a predictor for poor outcome in acute myeloid leukemia with normal cytogenetics. Blood 2006; 108:3898–3905.
19 Golub TR, Slonim DK, Tamayo P, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999; 286:531–537.
20 Ross ME, Mahfouz R, Onciu M, et al. Gene expression profiling of pediatric acute myelogenous leukemia. Blood 2004; 104:3679–3687.
21 Valk PJM, Verhaak RGW, Beijen MA, et al. Prognostically useful gene-expression profiles in acute myeloid leukemia. N Engl J Med 2004; 350:1617–1628.
22 Haferlach T, Kohlmann A, Schnittger S, et al. Global approach to the diagnosis of leukemia using gene expression profiling. Blood 2005; 106:1189–1198.
23• Verhaak RGW, Wouters BJ, Erpelinck CAJ, et al. Prediction of molecular subtypes in acute myeloid leukemia based on gene expression profiling. Haematologica 2009; 94:131–134.
24 Bullinger L, Döhner K, Bair E, et al. Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. N Engl J Med 2004; 350:1605–1616.
25• Bullinger L, Döhner K, Kranz R, et al. An FLT3 gene-expression signature predicts clinical outcome in normal karyotype AML. Blood 2008; 111:4490–4495.
26 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; 106:899–902.
27 Verhaak RGW, 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; 106:3747–3754.
28 Krivtsov AV, Armstrong SA. MLL translocations, histone modifications and leukaemia stem-cell development. Nat Rev Cancer 2007; 7:823–833.
29 Mullighan CG, Kennedy A, Zhou X, et al. Pediatric acute myeloid leukemia with NPM1 mutations is characterized by a gene expression profile with dysregulated HOX gene expression distinct from MLL-rearranged leukemias. Leukemia 2007; 21:2000–2009.
30•• Wouters BJ, Löwenberg B, Delwel R. A decade of genome-wide gene expression profiling in acute myeloid leukemia: flashback and prospects. Blood 2009; 113:291–298.
31 Schlenk RF, Benner A, Krauter J, et al. Individual patient data-based meta-analysis of patients aged 16 to 60 years with core binding factor acute myeloid leukemia: a survey of the German Acute Myeloid Leukemia Intergroup. J Clin Oncol 2004; 22:3741–3750.
32 Marcucci G, Mrózek K, Ruppert AS, et al. 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. J Clin Oncol 2005; 23:5705–5717.
33 Bullinger L, Rücker FG, Kurz S, et al. Gene-expression profiling identifies distinct subclasses of core binding factor acute myeloid leukemia. Blood 2007; 110:1291–1300.
34 Mrózek K, Marcucci G, Paschka P, Bloomfield CD. Advances in molecular genetics and treatment of core-binding factor acute myeloid leukemia. Curr Opin Oncol 2008; 20:711–718.
35 Paschka P, Radmacher MD, Marcucci G, et al. Gene expression profiling improves outcome prediction in adult core binding factor (CBF) acute myeloid leukemia (AML): a Cancer and Leukemia Group B (CALGB) study. J Clin Oncol 2007; 25:359s.
36 Mrózek K, Heerema NA, Bloomfield CD. Cytogenetics in acute leukemia. Blood Rev 2004; 18:115–136.
37 Radmacher MD, Marcucci G, Ruppert AS, et al. 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 2006; 108:1677–1683.
38 Metzeler KH, Hummel M, Bloomfield CD, et al. An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia. Blood 2008; 112:4193–4201.
39 Wouters BJ, Jordà MA, Keeshan K, et al. Distinct gene expression profiles of acute myeloid/T-lymphoid leukemia with silenced CEBPA and mutations in NOTCH1. Blood 2007; 110:3706–3714.
40 Marcucci G, Maharry K, Radmacher MD, et al. Prognostic significance of, and gene and microRNA expression signatures associated with, CEBPA mutations in cytogenetically normal acute myeloid leukemia with high-risk molecular features: a Cancer and Leukemia Group B study. J Clin Oncol 2008; 26:5078–5087.
41 Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004; 116:281–297.
42 Calin GA, Croce CM. MicroRNA signatures in human cancers. Nat Rev Cancer 2006; 6:857–866.
43 Zhang B, Pan X, Cobb GP, Anderson TA. microRNAs as oncogenes and tumor suppressors. Dev Biol 2007; 302:1–12.
44 Garzon R, Croce CM. MicroRNAs in normal and malignant hematopoiesis. Curr Opin Hematol 2008; 15:352–358.
45 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; 104:19971–19976.
46 Isken F, Steffen B, Merk S, et al. Identification of acute myeloid leukaemia associated microRNA expression patterns. Br J Haematol 2008; 140:153–161.
47 Jongen-Lavrencic M, Sun SM, Dijkstra MK, et al. MicroRNA expression profiling in relation to the genetic heterogeneity of acute myeloid leukemia. Blood 2008; 111:5078–5085.
48 Li Z, Lu J, Sun M, et al. Distinct microRNA expression profiles in acute myeloid leukemia with common translocations. Proc Natl Acad Sci U S A 2008; 105:15535–15540.
49 Garzon R, Garofalo M, Martelli MP, et al. Distinctive microRNA signature of acute myeloid leukemia bearing cytoplasmic mutated nucleophosmin. Proc Natl Acad Sci U S A 2008; 105:3945–3950.
50• O'Connell RM, Rao DS, Chaudhuri AA, et al. Sustained expression of microRNA-155 in hematopoietic stem cells causes a myeloproliferative disorder. J Exp Med 2008; 205:585–594.
51 Debernardi S, Skoulakis S, Molloy G, et al. 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; 21:912–916.
52 Hackanson B, Bennett KL, Brena RM, et al. Epigenetic modification of CCAAT/enhancer binding protein α expression in acute myeloid leukemia. Cancer Res 2008; 68:3142–3151.
53•• Baltimore D, Boldin MP, O'Connell RM, et al. MicroRNAs: new regulators of immune cell development and function. Nat Immunol 2008; 9:839–845.
54 Turzanski J, Grundy M, Russell NH, Pallis M. Interleukin-1β maintains an apoptosis-resistant phenotype in the blast cells of acute myeloid leukaemia via multiple pathways. Leukemia 2004; 18:1662–1670.
55 Estrov Z, Shishodia S, Faderl S, et al. Resveratrol blocks interleukin-1β-induced activation of the nuclear transcription factor NF-κB, inhibits proliferation, causes S-phase arrest, and induces apoptosis of acute myeloid leukemia cells. Blood 2003; 102:987–995.
56 Gondek LP, Tiu R, O'Keefe CL, et al. Chromosomal lesions and uniparental disomy detected by SNP arrays in MDS, MDS/MPD, and MDS-derived AML. Blood 2008; 111:1534–1542.
57 Rücker FG, Bullinger L, Schwaenen C, et al. Disclosure of candidate genes in acute myeloid leukemia with complex karyotypes using microarray-based molecular characterization. J Clin Oncol 2006; 24:3887–3894.
58 Ley TJ, Mardis ER, Ding L, et al. DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature 2008; 456:66–72.
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