e-Blood

MYELOID NEOPLASIA

Development of acute megakaryoblastic leukemia in Down syndrome isassociated with sequential epigenetic changesSebastien Malinge,1 Tim Chlon,1 Louis C. Dore,1 Rhett P. Ketterling,2 Martin S. Tallman,3 Elisabeth Paietta,4 Alan S. Gamis,5

Jeffrey W. Taub,6 Stella T. Chou,7 Mitchell J. Weiss,7 John D. Crispino,1 and Maria E. Figueroa8

1Division of Hematology/Oncology, Northwestern University, Chicago, IL; 2Division of Laboratory Genetics, Mayo Clinic, Rochester, MN; 3Leukemia Service,

Memorial Sloan-Kettering Cancer Center, New York, NY; 4Department of Oncology, Montefiore Medical Center, Bronx, NY; 5Division of Hematology/

Oncology/Bone Marrow Transplantation, Children’s Mercy Hospitals and Clinics, Kansas City, MO; 6Division of Hematology/Oncology, Children’s Hospital

of Michigan, Wayne State University, Detroit, MI; 7The Children’s Hospital of Philadelphia, Division of Hematology, University of Pennsylvania School

of Medicine, Philadelphia, PA; and 8Department of Pathology, University of Michigan Medical School, Ann Arbor, MI

Key Points

• DNA methylation changesduring the development ofDS-AMKL occur in sequentialwaves of opposing losses andgains of methylation.

• Each wave of DNAmethylation abnormalitiestargets specific gene networksthat contribute to distinctbiological features of thedisease.

Acute megakaryoblastic leukemia (AMKL) is more frequently observed in Down syn-

drome (DS) patients, in whom it is often preceded by a transient myeloproliferative

disorder (TMD). The development of DS-TMD and DS-AMKL requires not only the

presence of the trisomy 21 but also that ofGATA1mutations. Despite extensive studies

into the genetics of DS-AMKL, the importance of epigenetic deregulation in this disease

has been unexplored. We performed DNA methylation profiling at different stages of

development of DS-AMKL and analyzed the dynamics of the epigenetic program. Early

genome-wide DNA methylation changes can be detected in trisomy 21 fetal liver

mononuclear cells, prior to the acquisition of GATA1mutations. These early changes

are characterized by marked loss of DNA methylation at genes associated with de-

velopmental disorders, including those affecting the cardiovascular, neurological, and

endocrine systems. This is followed by a second wave of changes detected in DS-TMD

and DS-AMKL, characterized by gains of methylation. This newwave of hypermethylation

targets a distinct set of genes involved in hematopoiesis and regulation of cell growth and

proliferation. These findings indicate that the final epigenetic landscape of DS-AMKL is

the result of sequential and opposing changes in DNA methylation occurring at specific times in the disease development. (Blood.

2013;122(14):e33-e43)

Introduction

Acute megakaryoblastic leukemia (AMKL) is a rare form of acutemyeloid leukemia (AML), accounting for only 1% to 2% of all denovo AMLs and as much as 10% of pediatric AML.1,2 Moreover, itsincidence is several-hundredfold higher in patients with Down syn-drome (DS) than in children without DS.3 Four to 5% of DS fetusesdevelop a preleukemic transientmyeloproliferative disorder (DS-TMD),diagnosed at the time of birth, that spontaneously resolves within afew weeks in the majority of patients.4,5 Within the first 4 years oflife, approximately 20% of DS cases that develop TMD in utero willdevelop AMKL (DS-AMKL).6-9

DS-TMD and DS-AMKL invariably present with acquired mu-tations in the GATA1 gene that encodes a master regulator of ery-thromegakaryocytic development. These mutations arise in uteroand result in the expression of a short isoform of the GATA1 protein,known as GATA1s.6,10 Oncogenic cooperation between GATA1mutations and trisomy 21 is associated with the development ofDS-TMD and DS-AMKL. However, several observations in animalmodels11-13 and in human specimens14-18 strongly suggest that

additional genetic events are required to develop leukemia, but it isstill unclear how they may contribute to the phenotypic progressionto AMKL. Moreover, the mechanisms behind the spontaneous re-mission of DS-TMD and the subsequent progression to DS-AMKLare still not fully understood.

Abnormalities in DNA methylation are a hallmark of AML andhave been shown to display subtype specificity.19,20 Moreover,murine models have been used to show that impaired DNAmethyltransferase 1 activity results in a decrease in the leukemo-genic potential of the MLL-AF9 fusion oncogene.21,22 Notably,despite the extensive research into the genetics of DS and DS-related leukemias, little is known about the state of DNA methy-lation in TMD and DS-AMKL and how epigenetic changes mightcontribute to malignant transformation. In order to address thisquestion we performed genome-wide DNA methylation profilingon specimens obtained at different stages of the disease and analyzedthe dynamics of epigenetic reprogramming in the development ofDS-AMKL.

Submitted May 17, 2013; accepted August 15, 2013. Prepublished online as

Blood First Edition paper, August 26, 2013; DOI 10.1182/blood-2013-05-

503011.

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.

© 2013 by The American Society of Hematology

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Methods

Samples

Fetal liver mononuclear cells (MNC) with either a normal karyotype ortrisomy 21 were obtained from 2nd-trimester abortions. MNC cells wereisolated using a standard Ficoll separation method from a cohort of 7DS-associated acute megakaryoblastic leukemias (DS-AMKL), 6 cases oftransient myeloproliferative disorder (DS-TMD), and 8 acute megakaryo-blastic leukemias from adults without DS (non–DS-AMKL) received throughthe Children’s Oncology Group and Eastern Cooperative Oncology Groupleukemia tissue banks. Supplemental Table 1 (found on the Blood Web site)describes the patients’ characteristics. The diagnosis of megakaryoblasticleukemia was established by morphology and confirmed by immunophe-notyping. Institutional review board approval was obtained at Weill CornellMedical College, University of Pennsylvania, Children’s Hospital of Michigan,and the University of Michigan. Eight specimens of human CD341 bonemarrow cells were provided by the Stem Cell and Xenograft Core Facility ofthe University of Pennsylvania or purchased from AllCells (Emeryville,CA). These studies were performed in accordance with the Declaration ofHelsinki protocols.

DNA methylation by HELP

Genomic DNA was extracted using the Puregene kit from Qiagen (Valencia,CA) or a standard phenol-chloroform isolation followed by ethanol pre-cipitation. HELP (HpaII tiny fragment enrichment by ligation-mediatedpolymerase chain reaction) representations were prepared from 100 ng ofDNA as previously described23,24 and hybridized onto a custom long-oligonucleotide microarray designed to cover HpaII amplifiable frag-ments (HAF) at gene promoter regions. HG19 array was manufactured byRoche-NimbleGen, design ID: 100128_HG19_MKF_HELP_ChIP, cov-ering 117 521 HAFs annotated to 29 606 unique RefSeq genes.MM9 array wasmanufactured by Roche-NimbleGen, design ID: 100520_MM9_KF_Meth,covering 119 260HAFs annotated to 24 377 unique RefSeq genes. All sampleswere processed and hybridized at the Weill Cornell Medical CollegeEpigenomics Core Facility. Labeling, hybridization, and scanning wereperformed as previously described.25 Quality control and array normalizationwere performed following our previously described methods,26 and any arraysthat did not pass our quality control were excluded from the analysis. In order tocorrect for the introduction of hybridization batch effect, we subjected eachindividual channel to batch correction using the ComBat algorithm,27 and theadjusted channels were then used to calculate the final HpaII:MspI ratio.

Microarray analysis

Supervised analysis for the different group comparisons was performed usingBioconductor and the R 2.15 statistical software, and the genefilter, stats, andgplots packages. Pairwise comparisons were performed using Student t test,followed by adjustment of the P value with the Benjamini-Hochberg approachfor multiple comparisons.28 Significant differences were defined as those withadjusted P value , .05 and an absolute difference in log2(HpaII/MspI) .2,which corresponds to a minimal methylation difference of 30%.

Gene expression arrays

Normalized gene expression values for DS-AMKL, non–DS-AMKL, andDS-TMD specimens were downloaded from the GEO repository from thestudy by Bourquin et al29 (accession number GSE4119). Gene set enrich-ment analysis was performed using the Molecular Signatures Database(MSigDB) c1 collection (positional).30

Network and pathway analysis

Network and pathway analysis using the Ingenuity Pathway Analysis software(Ingenuity Systems, Redwood City, CA) was performed using the signaturesidentified for the different comparisons and the Ingenuity Knowledge databaseas a background reference. Network significance was scored as the negative

exponent of the right-tailed Fisher exact test used to estimate the likelihood thatthe network-eligible molecules that are part of a network are found therein byrandom chance alone.

Trisomic mice cells

Bone marrow cells were isolated from the long bones of either Ts1Rhr orwild-type mice at .12 months of age from 3 independent experiments.CD411 cells were isolated from red cell lyzed whole bone marrow cells byfluorescence-activated cell sorter using an anti-CD41 antibody (Emfret,Germany). The animal study was approved by the Northwestern UniversityInstitutional Animal Care and Use Committee.

Accession numbers

All microarray data generated for this study was deposited in the GeneExpression Omnibus database under accession number GSE46167.

Results

With the goal of studying epigenetic deregulation associated withmalignant transformation in the hematopoietic system of Downsyndrome patients, we performed genome-wide DNA methylationanalysis using the HELP assay23,24 on a custom high-densityoligonucleotide array designed to query to methylation status at;200 000 distinct CpG sites (annotated to 29 606 RefSeq genes).Given that the HELP assay uses as a reference an MspI-restrictedrepresentation of the patient’s own genome, which serves as aninternal control for copy number and sequence variants,24,31 thepresence of a trisomic chromosome in some of the specimens in thisstudy is automatically controlled for and does not have an impact onthe ability of the assay to detect methylation differences, even withinchromosome 21. In order to evaluate epigenetic changes during thedifferent stages of disease development, the following samples werecollected: fetal liver (FL) MNC with normal karyotype (FL-MNC,n 5 4), trisomy 21 FL MNC (Tri21 FL-MNC, n 5 4), MNC fromDown syndrome patients with either transient myeloproliferativedisorder (DS-TMD, n 5 6) or acute megakaryoblastic leukemia(DS-AMKL, n 5 7), and MNC cells from patients with adultnon–DS-AMKL (n 5 8).

DS-AMKL is epigenetically distinct from non–DS-AMKL

DNA methylation patterns in AML are not uniform across allsubtypes, and distinct epigenetic profiles characterize the differentcytogenetic and molecular subtypes of AML.19,20 Given this fact,the first goal of our study was to define the nature and extent ofepigenetic abnormalities in leukemic blasts of DS-AMKL. There-fore, we compared the DNAmethylation status of DS-AMKL blaststo that of normal CD341 hematopoietic cells isolated from the bonemarrows of healthy donors (n5 8). At a false discovery rate (FDR)of 5% and a minimum methylation difference of at least 30%between the 2 groups (absolute log2(HpaII/MspI) difference .2),we identified 1396 differentially methylated regions (DMRs), ofwhich 96% (n 5 1339) were aberrantly hypomethylated in theleukemic samples in relation to the normal CD341 cells (Figure 1A-B;supplemental Table 2). In order to ensure that the detection of suchprofound hypomethylation was not due to an artifact generated bythe comparison of the more fetal-like hematopoiesis of the DS-AMKL to a normal cohort obtained from adult CD341 cells, we alsocompared DS-AMKL specimens to control FL-MNCs and found an

e34 MALINGE et al BLOOD, 3 OCTOBER 2013 x VOLUME 122, NUMBER 14

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identical pattern of profound hypomethylation in the DS-AMKLsin relation to FL-MNCs (supplemental Figure 1). By contrast, asupervised analysis between non–DS-AMKL and normal CD341

cells revealed that epigenetic abnormalities in non–DS-AMKL aremuch less widespread than in DS-AMKL, with only 191 DMRs inrelation to normal CD341 cells detected at the same significancecutoff. Furthermore, this aberrant methylation in non–DS-AMKL ischaracterized by an opposite pattern to that seen in DS-AMKL,with a predominance of hypermethylated DMRs (87% hyper-methylated DMRs vs 13% hypomethylated DMRs) (Figure 1C-D;supplemental Table 3).

Next we performed a direct comparison of the DNAmethylationprofiles of DS-AMKL and non–DS-AMKL and identified 280 dif-ferentially methylated regions (FDR ,5% and methylation dif-ference >30%) between the 2 types of AMKL (supplementalTable 4). These DMRs were annotated to 267 unique genes. Thiscomparison revealed that DS-AMKL is not only hypomethylated inrelation to normal CD341 cells, but it is also significantly hypo-methylated relative to non–DS-AMKL (Figure 1E). Differentialmethylation between these 2 forms of AMKL-targeted genes isinvolved in key pathways such as the Breast Cancer–dependentDNA damage response pathway (CHEK1, E2F1, FANCM, andHLTF), the extracellular signal-regulated kinase (ERK5) signalingpathway (EGF, MEF2B, PRKCZ, and SGK1), and the B-celltranslocation gene (BTG)–dependent regulation of cell cycle (BTG2,

E2F1, and PPP2R2C), indicating that these differences may playa functional role in the development of AMKL in these 2 distinctbiological contexts. In order to ensure that any differences in DNAmethylation seen between non–DS-AMKL and DS-AMKL were notsimply a reflection of age-related changes in DNA methylation, wenext performed a direct comparison of the DNA methylation profilesof FL-MNC and normal CD341 cells. This analysis revealed thatFL-MNC tend to have higher levels of promoter methylation than donormal CD341 bonemarrow cells, with 81% (1599 out of 1965) of theDMRs being hypermethylated in FL-MNC in relation to the normalCD341 cells (supplemental Figure 2). Notably, only 7 of 366DMRs with greater methylation in normal CD341 cells overlappedwith the hypermethylated DMRs from non–DS-AMKL (Fishertest P value: not significant). Thus, these higher levels of DNAmethylation in FL-MNC cells lead us to conclude that the hyper-methylation seen in non–DS-AMKL specimens in relation to DS-AMKL cannot be interpreted as an age-dependent effect reflectingthe different age groups affected by these 2 disorders.

Aberrant hypomethylation in Down syndrome is detected early

on in fetal liver mononuclear cells

In order to understand how aberrant DNA methylation patternsbecome established in DS-AMKL, we investigated the dynamicchanges in these patterns throughout the evolution of development

Figure 1. DS-AMKL and non–DS-AMKL are epigenet-

ically distinct. (A) Heatmap representation of differen-

tially methylated regions between normal CD341 cells

(NBM) and DS-AMKL blasts. Each row represents a

genomic region, and each column represents a sample.

(B) Volcano plot representing the comparison of DS-AMKL

blasts to normal CD341 cells. X-axis represents the

difference in DNA methylation between the 2 groups,

and the y-axis represents the statistical significance of

the differences. Red dots denote DMRs with absolute

log ratio difference .2 and FDR ,5%. (C) Heatmap

representation of differentially methylated regions be-

tween normal CD341 cells (NBM) and non–DS-AMKL

blasts. Each row represents a genomic region, and each

column represents a sample. (D) Volcano plot represent-

ing the comparison of non–DS-AMKL blasts to normal

CD341 cells. X-axis represents the difference in DNA

methylation between the 2 groups, and the y-axis

represents the statistical significance of the differences.

Red dots denote DMRs with absolute log ratio difference

.2 and FDR ,5%. (E) Heatmap representation of

differentially methylated regions between non–DS-AMKL

and DS-AMKL blasts. Each row represents a genomic

region, and each column represents a patient sample.

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of this disease. We first determined whether the sole presence of anisolated extra copy of chromosome 21 is sufficient to impact theepigenome prior to the acquisition of GATA1 and other mutationsaffecting TP53 and JAK3. For this we compared the DNA meth-ylation status at promoter regions of FL-MNCswith normal karyotypeto those obtained from fetuses harboring trisomy 21. This analysisrevealed marked differences in the genome-wide DNA methylationprofiles of trisomic cells in relation to the control cells. At a signi-ficance cutoff of an FDR,5% and methylation difference>30%, weidentified 846 DMRs annotated to 964 unique Refseq genes (supple-mental Table 5). Of these DMRs, 99.4% (841 of 846) consisted ofregions in which there was a loss of DNA methylation in the Tri21FL-MNCs in comparison with the control FL-MNCs (Figure 2A-B).Analysis of the chromosomal locations of the DMRs revealed thathypomethylated regions in Tri21 FL-MNC were not restricted tochromosome 21 but instead were distributed across all chromo-somes. Only chromosome 17 showed a statistically significant en-richment of DMRs over background (Benjamini-Hochberg–adjustedFisher test,P5 .024), whereas no significant enrichmentwas detectedfor chromosome 21 (Figure 2C).

In order to confirm that the observed hypomethylation wasdependent on the presence of the trisomic chromosome 21, weperformed genome-wide DNA methylation analysis on CD411

cells isolated from a partial trisomic mouse model (Ts1Rhr)32

that has been recently shown to promote DS-leukemogenesis invivo.13 Although this system displayed the same trend toward ahypomethylation phenotype as was observed in the human Tri21FL-MNCs, aberrant methylation in the Ts1Rhr model was not aspronounced as in the human Tri21 FL-MNCs, with only 461 regionsshowing at least a 25%difference inmethylation between the wild-typeand trisomic cells. However, despite the difference in magnitudedetected, the main effect on methylation was one of predominanthypomethylation in the trisomic cells, with 89% (411 of 461) of DMRs

presenting with loss of methylation in these cells in relation to theirwild-type counterparts (Figure 2D).

GATA1 mutant DS-TMD blasts acquire additional epigenetic

abnormalities

Next we determined whether acquisition of GATA1 mutations,which lead to expression of the shorter isoform GATA1s, and thedevelopment of a DS-TMDphenotype have any impact on epigeneticpatterns. For this purpose we compared the DNA methylation pro-files of Tri21 FL-MNC, none of which carried mutations in GATA1,to those of GATA1s mutant DS-TMD. Detailed analysis of theDMRs acquired during the DS-TMD stage revealed that theseadditional epigenetic changes were predominantly in the form ofgains of DNA methylation. Out of 400 DMRs identified betweenTri21 FL-MNCs and DS-TMD blasts (FDR ,5% and methylationdifference >30%), only 8.5% of them (n 5 34) correspondedto regions with decreased DNA methylation levels in the DS-TMDspecimens. The remaining 366 DMRs (91.5%) all showed an in-crease in DNA methylation>30% at the DS-TMD stage in relationto the Tri21 FL-MNCs (Figure 3A; supplemental Table 6). In orderto determine whether the changes seen during the Tri21 FL-MNC toDS-TMD transition represented new epigenetic abnormalities acqui-red at this stage, or whether they were simply caused by a reversal ofthe original loss of methylation seen at the Tri21 FL-MNC stage,we compared the 2 epigenetic profiles. We found that only 51 (13%)of the DMRs observed at the DS-TMD stage corresponded toregions that had originally become hypomethylated at the Tri21FL-MNC stage to later become hypermethylated in the GATA1smutant DS-TMD stage. The remaining 87% of DMRs representednew epigenetic abnormalities acquired at the DS-TMD stage(Figure 3B). These findings indicate that hypermethylation acquiredat the DS-TMD stage occurs in the setting of persisting global

Figure 2. Trisomy 21–associated hypomethylation

is detected early on in fetal MNC. (A) Heatmap rep-

resentation of differentially methylated regions be-

tween Tri21 FL-MNC and control FL-MNC. Each row

represents a genomic region, and each column repre-

sents a patient sample. (B) Volcano plot representing

the comparison of Tri21 to control FL-MNC. The x-axis

represents the difference in DNA methylation between

the 2 groups, and the y-axis represents the statistical

significance of the differences. Red dots denote DMRs

with absolute log ratio difference .2 and FDR ,5%.

(C) Stacking barplots representing the relative pro-

portion of chromosomes 21 (left) and 17 (right) in the

whole array and the Tri21 FL-MNC DMRs. P values for

Fisher test followed by correction for multiple compar-

isons using the Bonferroni method. (D) Heatmap repre-

sentation of regions with at least 25% difference in

methylation between Ts1Rhr and control CD411 cells.

Each row represents a genomic region, and each column

represents a sample.

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hypomethylation, with only a few regions presenting with novelgains in methylation.

Sequential methylation changes target biologically relevant

gene networks

Given that epigenetic abnormalities associated with the acquisitionof GATA1 mutations are not only of a different nature, ie, gains inmethylation, but also target a different set of genes than thehypomethylation seen associated with trisomy 21 in FL-MNCs, wesought to better understand the role that these epigenetic changesmight play at these different stages. Using the Ingenuity PathwayAnalysis software (Ingenuity Systems), we identified the top 10gene networks targeted by aberrant hypomethylation in Tri21FL-MNCs and those targeted by aberrant hypermethylation in DS-TMD blasts. Aberrant hypomethylation in trisomic FL-MNCs wasassociated mainly with gene networks involved in developmentaldisorders, including cardiovascular, neurological, and endocrinedisorders, all of which form part of the developmental abnormal-ities of Down syndrome (Table 1; supplemental Table 7). Notably,when this analysis was performed using the hypomethylatedDMRs identified in the Ts1Rhr mice, we also found an enrichmentof gene networks involved in developmental disorders of thecardiovascular, endocrine, musculoskeletal, and digestive systems(supplemental Table 8). On the other hand, aberrant hyper-methylation in DS-TMD more frequently targeted gene networksassociated with hematological system development and cellulargrowth and proliferation, as well as cell signaling, cell death, andcell cycle, implicating these epigenetic abnormalities in the de-velopment of the leukemic phenotype (Table 2; supplementalTable 9).

DS-AMKL blasts are epigenetically indistinguishable from

DS-TMD blasts

Given the fact that many TMD cases likely go undiagnosed, it isunclear whether DS-AMKL is always preceded by a DS-TMDphase. Conversely, only about 20% of patients with diagnosedTMDs eventually develop DS-AMKL. In order to determine whetherthese 2 entities are epigenetically related, we performed a supervisedanalysis in which we compared the DNA methylation patterns ofDS-TMD and DS-AMKL blasts. At an FDR cutoff of ,5% wefailed to detect any statistically significant DMRs between these2 groups. In order to determine whether this phenomenon wasalso observed at the gene expression level, we downloaded publiclyavailable data from Bourquin et al29 and performed a directcomparison of the gene expression profiles of DS-TMD blasts

and DS-AMKL blasts. Only 7 genes were found differentially ex-pressed at an FDR ,5% and a minimum fold change of 2. Thesefindings demonstrate that DS-TMD and DS-AMKL are virtuallyidentical, consistent with other reports that found relatively minordifferences in gene expression levels between TMD and DS-AMKL.33,34 Moreover, although additional genetic lesions arelikely present at the DS-AMKL stage,14,15,35 they do not appear tohave a significant impact on the transcriptional and epigeneticprograms of these cells.

DS-related hypomethylation on chromosome 21 is linked to

trisomic gene overexpression and affects the DS critical region

and nearby neighboring regions

Next we focused our analysis on the DNAmethylation changes seenon chromosome 21. This analysis revealed that aberrant hypo-methylation on chromosome 21 observed in Tri21 FL-MNCs andpersisting until the final DS-AMKL stage was more prominent onand around theDScritical region (DSCR)35-37 (supplemental Figure 3).In order to identify epigenetic changes in the DSCR that are directlyrelated to the acquisition of an additional copy of chromosome 21and that are independent of any additional epigenetic reprogram-ming secondary to malignant transformation, we examined DNAmethylation differences of genes in the DSCR and neighboringregions between control FL-MNCs and Tri21 FL-MNCs. Amongthe genes in this region, POFUT2, BTG3, ATP5J, and GABPA wereidentified as hypomethylated by using our initial stringent criteriafor differential methylation, ie, .30% difference in methylation(Figure 4A). In addition, CHAF1B, DYRK1A, SUMO3, USP25,BACH1, USP16, and RUNX1 also showed statistically significantchanges in DNA methylation between these 2 groups, thoughthese changes were of a smaller magnitude. Notably,DYRK1A andCHAF1B13,38 have previously been implicated in DS-leukemogenesisin human specimens and murine models of DS-AMKL.13

In order to determine whether these changes in DNA methyl-ation correlated with changes in gene expression levels in DS-related malignancies, we analyzed the expression levels of thesegenes in the Bourquin et al expression data set.29 With the exceptionof RUNX1, all of these genes showed significantly higher expressionlevels in both DS-AMKL and DS-TMD in relation to non–DS-AMKL (Figure 4B). In parallel, we performed gene set enrichmentanalyses using positional gene set MSigDB collections (c1) andrevealed that genes localized in 21q22 band are the only ones,among the whole genome, that are significantly enriched in DS-AMKL samples from this same microarray dataset29 (Figure 4C).Taken together, these results show that DNA hypomethylation ofspecific chromosome 21 genes, included or not in the DSCR region,

Figure 3. Acquisition of GATA1 mutations is accom-

panied by a wave of hypermethylation changes at

distinct loci. (A) Heatmap representation of differentially

methylated regions between Tri21 FL-MNC and DS-TMD

blasts. Each row represents a genomic region, and each

column represents a patient sample. (B) Venn diagram

representation of the overlap between the Tr1 FL-MNC vs

control FL-MNC DMRs and the Tri21 FL-MNC vs DS-

TMD DMRs.

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is correlated with changes in gene expression levels in DS-relatedmalignancies, and the results suggest that the hypomethylation ofthis region is associated with an increased expression of trisomicgenes that predispose and cooperate with GATA1s and other geneticabnormalities to foster TMD and DS-AMKL.

Discussion

DS-AMKL development is associated with a series of well-characterized sequential genetic events.6 However, much less isknown about the epigenetic changes that occur during this process.In the current study we report the first genome-wide DNA meth-ylation study of DS leukemogenesis. Taking advantage of themultistep developmental process of DS-AMKL, we performed DNAmethylation profiling at sequential stages of the disease. Our findingsdemonstrate that epigenetic deregulation inDS-AMKL leukemogenesis

occurs in 2 distinct epigenetic “waves.” The first wave of epigeneticreprogramming seems to be directly linked to the additional copy ofchromosome 21, which results in genome-wide hypomethylation. Thishypomethylation was detected in primary human Tri21 FL-MNCspecimens, in DS-AMKL in relation to non–DS-AMKL, and in theTs1Rhr murine model. Given that the Ts1Rhr mice are partiallytrisomic, it is possible that the difference inmagnitude in hypomethyl-ation observed between the primary human FL-MNCs and the partialtrisomic cells in these animals is due to this difference and that a largertrisomic region, or even a full trisomy 21, is required to impose the fullextent of the hypomethylation phenotype observed in the humantrisomy 21 specimens. Comparable hypomethylation had beenpreviously reported in normal peripheral blood leukocytes from DSpatients. However, the extent of this hypomethylation and its impacton developmentally related gene regulatory networks was not pre-viously recognized.39 Notably, both the Ts1Rhr and Ts65Dn murinemodels of DS40 have been linked to decreased intestinal polyp

Table 1. Top 10 networks targeted by aberrant hypomethylation in Tri21 FL-MNC

Number Molecules in network Top functions Score

1 ARID1B, AS3MT, BAI1, BCS1L, C12orf52, C21orf56, CDK2AP2, CUL2,

FAM107B, FAM117A, FAM158A, GMDS, HNF4A, IL11RA, JINK1/2, LSR,

MREG, MRPL18, NDUFV1, NUCB1, OAZ2, PABPN1, PPP1R3B, PRAF2,

RFPL2, SBNO2, SLC17A5, SLMO2, SPATA6, STOML2, TBC1D23, TEF,

THOC3, TLE3, VN1R1

Cardiovascular disease, genetic disorder,

neurological disease

56

2 AEBP1, BBC3, CARD11, CD7, CHRFAM7A, EDAR, ELF5, ETS, FABP6,

HMG20B, IP6K2, KRT16, KRT17, LRDD, LSP1, NFkB, CHRN, NR1H2, NR1H3,

NR1H, PIM3, PLEC, PPP1R13B, RASGRF1, RNF31, ROMO1, RRAS, RXR,

SECTM1, SRPX, TRADD, TRAF1, TUBA1C, ZHX2

Cell death, dermatological diseases & conditions,

endocrine system disorders

45

3 GRIA1-4, AP2A1/2, ATP1A2, ATP1A4, BABAM1, CYP11B2, EPB41L1, ERK1/2,

FXYD7, GABAR-A, GABRG2, GABRR1, GNAL, GPT2, HSF4, IL27RA,

MRPL53, NR5A1, NSF, NTF3, PITPNM1, PRKAG1, PTGDS, RLN3, SOX7,

SOX9, SOX10, STX5, TFF2, TH, TMEM109, UCN2, VAPB

Small molecule biochemistry, developmental

disorder, gastrointestinal disease

44

4 PSMA/PSMB, PSME/PSMF, ADIPOR1, ADRM1, TUBA1A, AMPK, ASCL2,

BARD1, TUBB, CLINT1, CUL7, FES, GP1BA, Hsp70, LRP, MLLT4, MST1R,

ORC1, PIK3R1/2, PIK3R6, PLXNB3, PFN1/2, PSENEN, PSME1, PSME2,

RAB3B, PSEN1/2, SLC39A4, SNCG, SPDEF, TCP1, TRIM9, UBB, VASP

Cell cycle, dermatological diseases & conditions,

embryonic development

32

5 ABLIM1, ACP2, ACTG2, ADAP1, FOS/JUN, CBFA2T3, CGA/B, CGB1, CGB2,

CGB7, CGBB, COL1A1/2, NR2F1/2/6, CYP17A1, CYP2D6, DIABLO, FSH,

GALP, GPSM1, HAUS4, HCG, HIST1H1A-E, HMBS, MA2K4/10/12, LHB, LIF,

MAP2K1-7, RPS6KB1/3, PRKACA/B/G/G1/G2, PTMS, RXRA, TGFB1/2/3,

USHBP1, WNT5B, ZBED1

Cellular movement, reproductive system

development & function, nervous system

development & function

30

6 CAPN1/10/11/, CAPN3, CASP1-12, CCNE1, CDK2, COBRA1, CRYAA, CCNA1/2,

CCNE1/2, CYP27B1, DEFA1, DNAJB8, DNAJC9, E2f, ESRRB, ESR1/2, GPC1,

HBG1, HIST2H3C, HIST2H4, Hsp90, Hsp22/Hsp40/Hsp90, HVCN1, KIAA1967,

LIN37, OSGIN1,PRKCA/B, PNLIPRP2, SATB1, SF3B2, TFDP1, TFF1, VDR,

RXR, ZDHHC11

Cell cycle, cellular assembly & organization,

embryonic development

30

7 ABCC1, ACR, ADCY, ADORA2A, AGAP1, AVPR2, DRD1, PTK2/2B, FOXO1,

GHRHR, GIT1, Gpcr, GPR84, GPR157, GPRC5D, INS, KCNAB2, KISS1R,

NPFF, P110, PAX4, PEMT, PEPCK, PRKCZ, Rab5, Ras, SRC, TBC1D3F,

THRA/B, NTRK1/2/3, USP17, USP17L2, VEGF, VN1R4

Cell signaling, nucleic acid metabolism, molecular

transport

28

8 CBP, AFAP1, PPP3CA/B/C, CALM1, CAMK1G, CAMK2A/B, DTNA, DVL1,

EPHB2, ACTA1/2, GATA2, GRIN2D, KSR1, LEFTY2, MAPK1, MYLK3, NCS1,

NFATC1-4, GRIN2A-D, PIP5K1C, RHOA-H, RCVRN, REM1, RHOT2, ROCK1/2,

SIT1, SLC8A2, SNX33, TNNI3, TNNT3, TPM4, TPM1/2/3/4, TUBA1A-E, UTS2

Skeletal & muscular system, development &

function, tissue morphology, cell-to-cell

signaling & interaction

26

9 ACE, ACTA2, Akt, CD68, CD151, CD276, COL1, COL1A1/2, COL4, Cpla2, CSF2,

FGA/B/G, FMOD, ITGA5, ITGB5, KLK1/3/B1, LAG3, LAMA1/5, MAP2K1/2,

MFAP5, MMP26, PAK1-4, PDGFA-D, PLA2G2F, PMEL, PP2A, PROK1, PRSS1/

PRSS3, RAP1A/B, SERPINF2, TPSAB1/TPSB2, VWF

Antigen presentation, inflammatory response,

cardiovascular disease

24

10 ABR, ACTA1/2, ADHFE1, ADH, APP, AVPR1B, CKII, CNGB1, CREB1/3/5,

CRYAB, CYC1, DCTN1, DHRS2, DHRS4, DMPK, DNAJB1, ENO1, FAM162A,

GDP, GSK3A/B, HSPB2, NUDC, NUMBL, MAPK1/11-14, PDGFA/B, PLCB1-4,

RAC1/2/3, POLR2A-L, RPL13, RYBP, PRSS1/2/3, TTLL12, WDR45, WNT1-11

Cellular assembly & organization, organismal

development, organismal injury & abnormalities

23

Genes in the network with differential methylation are in boldface type.

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formation in the Apcmin model.41 This reduction is analogous to thereduced polyp formation described for the Apcminmodel in theDnmt1hypomorph background in which polyp formation was reduced in aDnmt1 dose-dependent manner in comparisonwithApcmin Dnmt11/1

animals.42 Similarly, despite their increased risk for the developmentof acute leukemias, DS patients have an overall reduced risk fordeveloping cancers, in particular solid tumors.43 Further studies willbe required to determine whether or not the hypomethylation as-sociated with the presence of trisomy 21 results in a protective effectagainst malignant transformation and if so, what the mechanismresponsible for this phenomenon is. One attractive model is thathypomethylation may occur in DS due to overexpression of cys-tathionine b synthase, which can lead to lower S-adenosylmethio-nine levels.44

The second wave of aberrant DNA methylation was detected inGATA1s mutant DS-TMD MNCs and consisted mainly of gains in

methylation at a distinct set of target genes. Unlike hypomethy-lation associated with trisomy 21, which preferentially affected genenetworks associated with developmental disorders such as those seenin DS, genes affected by aberrant hypermethylation in DS-TMDblasts were specific to hematological development and regulationof key cellular processes such as growth, proliferation, cell cycleregulation, and cell death. How trisomy 21 may lead to hypomethy-lation while GATA1 mutations result in hypermethylation remainsunclear. These findings, however, indicate that these 2 waves ofepigenetic reprogramming contribute to distinct aspects of DSabnormalities (Figure 5). Additional experiments will be requiredto functionally determine whether the predisposing role of theadditional chromosome 21 to leukemogenesis is mediated throughthis trisomy 21–driven hypomethylation, as well as to further definethe precise nature of the epigenetic changes in sorted subpopulations(hematopoietic stem cell and MEP) obtained from the different

Table 2. Top 10 networks targeted by aberrant hypermethylation in GATA1s-positive DS-TMD

Number Molecules in network Top functions Score

1 ADCY, ADORA3, ADRB3, FOS/JUN, ATN1, CD9, CELSR1, CXCR7, DRD2, ESR1/

2, FPR3, GNAI1/2/3, GPR83, GPR101, HTR4, HTR5A, KCNA2, KCNA3,

KCNJ6, LTF, NCOR1/2, NGEF, NRL, NUPR1, PI3K, RAC1/2/3, RHOA-D, RXR,

S1PR3, VIPR1, VN1R4, ZFHX3

Cell signaling, molecular transport, nucleic acid

metabolism

23

2 ACTA2, ACTN1, ALPI/L/P, ANKRD2, APOA2, DHTR, CALM1, CAMK1G, COL1A1,

COL1, CREB1/3/5, CREBL2, DCT, ERK1/2, ACTAB/C1, FSHB, GH1/2, GSN,

IGFBP5, IL27RA, KCND2, KCNIP4, LEP, LHB, MYO7A, RIMS1, RLN3,

SERPINA12, SGK223, SLC15A1, TFF2, TGFB, TPM1, TSEN34

Tissue morphology, cell morphology, cellular

movement

23

3 AEBP1, ALDH7A1, CREBP/EP300, CLEC10A, ELF2, ETS, FGA, FGA/B/G, GAS7,

IFI27L2, IFNA1, IFNB1, IGHG1-4, IL24, IL25, IL12, IRF8, KLK2, LGALS7/

LGALS7B, MAFG, MHC II, NFE2, NFkB, PPFIA2, PRSS1/PRSS3, PTPN2,

REN, SERPINA5, SERPINF2, PRSS1/2/3, UBP1, UGCG, VWF

Cellular growth & proliferation, lymphoid tissue

structure & development, cellular development

23

4 PSMA/PSMB, AKT1/2/3, ATAD2, CASP1-12, CYC1, DAB2IP, DLEU1, DUB, E2F1/

6, ERN1, ESR1, GREB1, HIST1H3A, HIST2H3C, HIST2H4, Hsp70, HSPA1A/

HSPA1B, HSPA1L, IL1, MAP2K4/8/10/12, KIAA1967, MAPK1/11/12/13/14,

PEPD, POLR2H, PPARGC1A, PRR11, POLR2A-L, SF3B2, SIRT7, SKA2,

SORBS2, UCHL3, USP12, VEGF, ZBTB17

Gene expression, endocrine system development

& function, small molecule biochemistry

21

5 ADAMTS20, ATL3, CHUK/IKBKB/IKBKG, CPVL, CSDE1, DMRT1, DMRTC1/

DMRTC1B, DNTTIP2, DUPD1, E2F1, EIF4E2, ERLIN1, ETF1, FOS, GSPT2,

HIST1H2AA, HIST1H2AG, HIST1H2BA, HIST1H2BJ/HIST1H2BK, HIST1H4C,

HRK, HRNR, KIAA0802, LIMD1, LPPR4, MAP3K3, MARK4, PABPC1, PRDM2,

PREP, SFMBT1, TBCA, TRAF6, YWHAZ, ZNF652

Cell death, cellular development, hematological

system development & function

15

6 ANXA8L2, ASB2, B3GALT1, CACNG5, CLC, Cyp2c70, FOXD3, GBX2, GLI1,

HEBP1, HOXA3, HOXB3, HOXD13, KCTD11, L1TD1, NAT2, NAV2, NDN,

NKX2-3, PAPSS2, PDZRN3, PLA2G15, POU5F1, RORA, SMPD3, SOX3,

SOX7, SOX15, TCEB1, TCEB3C, TMEM100, WNT2B, WSB1, ZCCHC8

Cellular development, cellular growth &

proliferation, cell-to-cell signaling & interaction

15

7 CCDC82, CCL16, CRIPT, CSTF2T, DLG4, DNAH7, EPO, FAM107B, GLCE, GPX7,

HGS, HNF4A, ISOC1, LRRTM2, MCTS1, MIF4GD, MRPS7, MRPS23, MSRB2,

PAPOLA, PCDHA13, PDIA5, PKD1L3, PKD2L1, PRDM14, PREB, PRL, RAI2,

RBMY1A1, SEC22B, TNNI3, UBQLN4, UROS, VHL, ZG16B

Cell cycle, gene expression, hematological

disease

14

8 ACTA1/2, AGAP1, ART1, CSNK2A1/A2/B, EPS8L2, ERK, PTK2/2B, GCK, GGA3,

HMGN2, INS, LGALS12, MAPK, MAZ, Mml1, MMP20, MMP24, MMP27,

MMP28, MMP23A, MMP23B, MYO5B, NEUROG1, NMUR1, PCCB, PCOLCE,

PDGFA/B, PRKAR1A/1B/2A/2B, PLC, KRAS/NRAS, RASA2, SLC7A7, STAT,

TCOF1

Tissue development, cellular assembly &

organization, digestive system development &

function

12

9 ATXN1, BEND2, CCDC164, CLEC4F, DDX46, DYSF, EBNA1BP2, EGLN, EGOT,

FAM203A/FAM203B, GADD45G, GCAT, GLIPR2, GSTT2/GSTT2B, IGKV, IL4,

IL5, ITGB1, KLHDC2, OBFC2A, PPIF, RAD54L2, RWDD2B, SASH3, SDF2L1,

SERPINA3, SKA1, SLAIN1, TOMM40L, TRIM72, UBE2I, WNK2, ZNF609,

ZNF777

Cellular movement, hematological system

development & function, hypersensitivity

response

12

10 RAB7B, ART5, BST1, BTNL2, C11orf82, CD80/CD86, CECR1, CERK, CLEC10A,

CLEC3B, CYBA, NCF1/2/4, DPAGT1, GAL3ST3, IFNG, IL4I1, MCHR1,

MOBKL1A, MTMR11, NAD1, Pc-Plc, PHKA2, RAB36, RAB38, RAF1, RCN1,

SBSN, SCN4A, SLC6A12, TCF3, TLR4, TMEM180, TREM2

Cell morphology, cell-to-cell signaling &

interaction, cellular function & maintenance

12

Genes in the network with differential methylation are in boldface type.

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Figure 4. DNA hypomethylation is associated to overexpression of trisomic genes. (A) Visualization on the University of California at Santa Cruz (UCSC) genome

browser of DNA methylation changes detected by the HELP assay on 4 genes localized in neighboring regions of the DSCR. Each custom track represents the mean HELP

values measured for that group. Negative deflections below the zero line represent negative log ratios (methylated regions), and positive deflections above the zero line

represent positive log ratios (hypomethylated regions). Red arrows indicate HELP probesets with detectable differences in methylation between control samples (FL-MNC and

non–DS-AMKL) and DS-related samples (Tri21 FL-MNC, DS-AMKL, and DS-TMD). (B) Boxplots representing gene expression values for genes differentially methylated in

and around the DS critical region. P values are for t test comparisons between each of the 2 DS-related conditions vs the non–DS-AMKL values. (C) Gene set enrichment

analysis using positional gene sets MSigDB collections (c1) showing significant enrichment for genes localized in 21q22 band in DS-AMKL samples from a publicly available

DS-AMKL microarray dataset.29

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specimen types collected in this study. Notably, an analysis ofGATA1 and GATA1s occupancy by ChIP-seq45,46 failed to showsubstantial enrichment at regions that acquired hypermethylationin GATA1s mutant specimens (data not shown). These findingsindicate that epigenetic reprogramming during DS-AMKL devel-opment is not mediated through binding of GATA1/GATA1s toits target regions. Whether transcription factors or chromatin modu-lators downstream of GATA1 may be responsible for the specificgains of DNA methylation remains a possibility.

The general pattern of global hypomethylation with focal hyper-methylation at promoter regions in cancer has been recognized formany years.47 However, the precise sequence in which this patternis established is not clear, and whether an initial wave of hypometh-ylation followed by acquisition of genetic abnormalities and focalhypermethylation as described here are also observed in othertumors is yet unknown.

Previous studies of epigenetic abnormalities in myelodysplasticsyndromes have shown that more aggressive forms of the disease areassociated with more intense epigenetic abnormalities.48 However,this was not the case in the current study, in which the DNAmethylation patterning in DS-TMD MNCs was indistinguishablefrom that of DS-AMKL MNCs. Likewise, a direct comparison ofthe transcriptional profiles of these 2 types of blasts also identifiedfew significant differences between TMD andDS-AMKL. In 1 study,the 2 populations could be separated only by supervised clusteringusing a small set of differentially expressed genes.33 A second studyreported only 67 genes whose expression was significantly differentin the TMD vs DS-AMKL.34 Given the almost identical transcrip-tional and epigenetic profiles of these 2 entities, it is possible thatthe initial clearance of DS-TMD blasts with subsequent reappear-ance in the DS-AMKL phase may be due to the effect of extrinsicfactors that might be correlated to different hematopoietic nichesin bone marrow vs fetal hematopoiesis.

Another interesting finding is that a specific region of the chro-mosome 21 around the DSCR remains hypomethylated during thewhole process of DS-leukemogenesis, whereas from the TMD stageonward, additional changes in methylation are dominated by hy-permethylation. This region has been specifically associated to DS-AMKL development using human specimens with segmentaltrisomy 21,38 gene expression profiling29,49 and animal models.13

We correlated this hypomethylation to the overexpression of genescontained in this region including DYRK1A, which has been shownto cooperate with Gata1s and MPL W515L expression to promoteDS-AMKL in vivo.13 Notably, only a small fraction of thechromosome 21 genes were upregulated in the DS-AMKL and DS-TMD specimens in relation to non–DS-AMKL, indicating that up-regulation of these genes is not simply the result of the increasedcopy number due to the trisomy. Taken together, these observationsstrongly suggest that the observed epigenetic memory of hypo-methylated DSCR genes is submitted to a selective pressure requiredfor DS-leukemogenesis.

In summary, our study shows that the genetic changes linked tothe specific stages in DS-AMKL are associated with distinct sets ofchanges in DNA patterning. Whether the epigenetic pattern changescontribute to disease or simply reflect the underlying genetic lesionsremains to be determined. In support of a pathogenic role of theaberrant gene methylation, it is notable that the earlier changesassociated with hypomethylation tended to affect developmentalpathways comprising Down syndrome. The later acquired changesin methylation in cell cycle control pathways also support the ideathat the changes in DNA methylation are a cause and not an effectof disease. The mechanisms through which these epigenetic changesbecome established are yet to be identified and require careful con-sideration of the genes in the critical region of chromosome 21 as wellas pathways downstream of GATA1, because GATA1 target geneswere not among the hypermethylated sites in TMD and DS-AMKL.

Figure 5. Model of sequential genetic and epigenetic

events associated with DS-AMKL development. Two

distinct waves of epigenetic reprogramming were detected.

The first wave, which happens upon acquisition of an

additional copy of chromosome 21, results in genome-wide

hypomethylation and affects genes involved in develop-

mental processes. The second wave, detected in DS-TMD

blasts, results in hypermethylation of genes involved

in hematological disorders and regulation of key cellular

processes. No additional epigenetic changes are detected

upon progression to DS-AMKL, but DS-AMKL blasts are

hypomethylated in comparison with NBM CD341 and

non–DS-AMKL blasts.

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Acknowledgments

The authors thank the Weill Cornell Medical Center EpigeneticsCore Facility for performing the HELP microarrays and JasonBerman, Soheil Meshinchi, Todd Alonzo, and Sommer Castro andthe Children’s Oncology Group for their assistance with DS-AMKLspecimens. This article’s contents are solely the responsibilityof the authors and does not necessarily represent the officialviews of the National Cancer Institute.

M.E.F. is supported in part by a Special Fellow Award from theLeukemia and Lymphoma Society. S.M. was supported by aLeukemia and Lymphoma Society Special Fellowship (no. 5110-10). This work was supported in part by grants from the NationalCancer Institute (NCI) (CA101774) and the Samuel WaxmanCancer Research Foundation. J.W.T. is supported by an R01 grantfrom the NCI (CA120772). This work was partially funded by grantK08 HL093290 from the National Heart, Lung, and Blood Institute(NHLBI) (to S.T.C.) and The American Society of Hematology (toS.T.C.). This study was coordinated by the Eastern CooperativeOncology Group (Robert L. Comis, Chair) and supported in part byPublic Health Service Grants CA23318, CA66636, CA21115,CA17145, CA13650, CA15488, and CA14958 from the NCI,National Institutes of Health, and the Department of Health and

Human Services. Research with DS-AMKL samples was supportedby the Chair’s Grant U10 CA98543 (to C.O.G.) from NCI.

Authorship

Contribution: J.C. and M.E.F. conceived and designed the study;S.M., T.C., L.C.D., andM.E.F. performed experiments; T.C., L.C.D.,andM.E.F. analyzed the data; S.M. and J.C. critically revised the data;S.C., R.P.K., M.S.T., E.P., M.J.W., A.G., and J.W.T. contributed keyresources and specimens; M.E.F., S.M., and J.C. wrote the manu-script; and T.C., S.C., M.J.W., R.P.K., E.P., M.S.T., A.G., and J.W.T.revised and approved the manuscript.

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

Correspondence: Maria E. Figueroa, Department of Pathology,University of Michigan, 109 Zina Pitcher Place, 2019 BSRB, AnnArbor, MI 48109-2200; e-mail: marfigue@med.umich.edu; or JohnD. Crispino, Northwestern University Feinberg School of Medicine,Division of Hematology and Oncology, 303 E Superior St, LurieResearch Building 5-113, Chicago, IL 60611; e-mail: j-crispino@northwestern.edu.

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BLOOD, 3 OCTOBER 2013 x VOLUME 122, NUMBER 14 EPIGENETICS OF DOWN SYNDROME AMKL e43

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online August 26, 2013 originally publisheddoi:10.1182/blood-2013-05-503011

2013 122: e33-e43  

Maria E. FigueroaPaietta, Alan S. Gamis, Jeffrey W. Taub, Stella T. Chou, Mitchell J. Weiss, John D. Crispino and Sébastien Malinge, Tim Chlon, Louis C. Doré, Rhett P. Ketterling, Martin S. Tallman, Elisabeth associated with sequential epigenetic changesDevelopment of acute megakaryoblastic leukemia in Down syndrome is 

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