Yamazaki et al., p 1
Research Article
Title
TET2 mutations affect non-CpG island DNA methylation at enhancers and transcription factor
binding sites in chronic myelomonocytic leukemia
Authors and affiliations
Jumpei Yamazaki1,2, Jaroslav Jelinek1,2, Yue Lu3, Matteo Cesaroni1, Jozef Madzo1,4, Frank
Neumann2, Rong He2, Rodolphe Taby2, Aparna Vasanthakumar4, Trisha Macrae4, Kelly R.
Ostler4, Hagop M. Kantarjian2, Shoudan Liang5, Marcos R. Estecio2,3, Lucy A. Godley4 and Jean-
Pierre J. Issa1,2
1Fels Institute for Cancer Research and Molecular Biology, Temple University, Philadelphia, PA
19140, USA
2Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX
77030, USA.
3Department of Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center,
Houston, TX 77030, USA.
4Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago,
IL 60637, USA.
5Department of Bioinformatics and Computational Biology, The University of Texas MD
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Anderson Cancer Center, Houston, TX
Running Title: Effects of TET2 mutations on DNA methylation in CMML
Keywords: TET2, DNA methylation, mutation, CMML, genome-wide profiling
Precis: Results show how mutations in a leukemia-associated gene act epigenetically by
influencing the DNA methylation of hematopoietic-specific enhancers that impact leukemia
development.
Financial support: This work was supported by the National Institutes of Health grants
CA100632, CA121104, and CA049639 (to JPI) and CA129831 and CA129831-03S1 (to LAG), and
supported by a Stand Up to Cancer grant from the American Association for Cancer Research.
JPI is an American Cancer Society Clinical Research professor supported by a generous gift from
the F. M. Kirby Foundation.
Authors’ contributions
Contribution: J.Y. and J.-P.J.I. designed the study; J.Y., R.T., A.V., T.M., and K.O. performed
sequence analysis, analyzed sequence traces, and validated mutation; J.Y., F. N., and R. H.
performed DREAM; J.Y., J.J., Y.L., and S.L. analyzed DNA methylation data; J.Y., T.M., and J.M.
performed and analyzed 5hmc pull-down assay; J.Y. and J.-P.J.I. wrote the manuscript with
assistance from J.J., H.M.K., M.R.E., and L.A.G.
Correspondence:
Jean-Pierre J. Issa,
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Fels Institute for Cancer Research and Molecular Biology, Temple University
3307 N. Broad Street, Room 154 Pharmacy Allied Health Building, Philadelphia, PA 19140
Email: [email protected]
Phone: 215-707-1454.
Conflicts of Interest: The authors declare no competing financial interests.
Word count: 4408
Total number of figures and tables: 6 figures, 7 supplementary figures, and 2 supplementary
tables
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Abstract
TET2 enzymatically converts 5-methylcytosine to 5-hydroxymethylcytosine as well as other
covalently-modified cytosines and its mutations are common in myeloid leukemia. However,
the exact mechanism and the extent to which TET2 mutations affect DNA methylation remain
in question. Here we report on DNA methylomes in TET2 wild type (TET2-WT) and mutant
(TET2-MT) cases of chronic myelomonocytic leukemia (CMML). We analyzed 85,134 CpG sites
(28,114 sites in CpG islands (CGIs) and 57,020 in non-CpG islands (NCGIs)). TET2 mutations do
not explain genome-wide differences in DNA methylation in CMML, and we found few and
inconsistent differences at CGIs between TET2-WT and TET2-MT cases. By contrast, we
identified 409 (0.71%) TET2-specific differentially methylated CpGs (tet2-DMCs) in NCGIs, 86%
of which were hypermethylated in TET2-MT cases, suggesting a strikingly different biology of
the effects of TET2 mutations at CGIs and NCGIs. DNA methylation of tet2-DMCs at promoters
and non-promoters repressed gene expression. Tet2-DMCs showed significant enrichment at
hematopoietic-specific enhancers marked by H3K4me1, and at binding sites for the
transcription factor p300. Tet2-DMCs showed significantly lower 5-hydroxymethylcytosine in
TET2-MT cases. We conclude that leukemia-associated TET2 mutations affect DNA methylation
at NCGI regions containing hematopoietic-specific enhancers and transcription factor binding
sites.
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Introduction
TET2 (ten-eleven translocation (TET) oncogene family member 2) is a tumor suppressor
gene on chromosome 4q24 (1). TET2 mutations were first described in myeloproliferative
neoplasms (MPN) (1), and were later also described in systemic mastocytosis (2), chronic
myelomonocytic leukemia (CMML) (3), myelodysplastic syndrome (MDS) (4), MDS/MPN (5),
and acute myeloid leukemia (AML) (6). The incidence of TET2 gene alterations ranges from 10-
50% in myeloid malignancies, with the highest frequency of mutations found in CMML, where
TET2 mutations were noted in 35-50% of cases (5-7). As first reported for TET1 (8), TET2
converts 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC) (9) as well as other
covalently-modified cytosines (10,11) in embryonic stem cells, and thus mutations of TET2 were
theorized to contribute to leukemogenesis by altering the epigenetic regulation of transcription
through DNA methylation. In fact, among the three members of the TET gene family (TET1,
TET2, and TET3), TET2 is the sole gene found to be frequently mutated in myeloid malignancies
(6) and to disrupt hematopoietic differentiation (12,13). Furthermore, in murine models, Tet2
deficiency impairs hematopoietic differentiation with the expansion of myeloid precursors
(14,15). However, the exact mechanism and the extent to which TET2 mutations affect DNA
methylation remain in question. There are conflicting reports (12,13,16,17) on the effect of
TET2 mutations on DNA methylation. It has been reported that overall loss of 5mC content
(hypomethylation) (12) was a remarkable characteristic of CMML patients with TET2 mutations.
Two groups studied TET2 mutant AMLs and CMML and identified a promoter hypermethylation
phenotype (13,17). Another group reported predominantly hypomethylation in TET2 mutant
CMML, and we previously reported no effect of TET2 mutations on DNA methylation in CpG
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islands (CGIs) (16). The genome-wide studies reported conflicting results used microarray
analysis with limited validation. Here, we employed the quantitative, sequence-based digital
restriction enzyme analysis of methylation (DREAM) (18) method to study this issue and found
that hypermethylated sites in TET2-MT are mostly in non-CpG islands (NCGIs) and are enriched
at hematopoietic-specific enhancers marked by H3K4me1, and at binding sites for the
transcription factor p300.
Materials and Methods
Patients
We analyzed whole bone marrow or peripheral blood samples (bone marrow was not
available in one TET2-WT case) prior to treatment from 40 patients with CMML referred to The
University of Texas MD Anderson Cancer Center or The University of Chicago, or enrolled in a
multi-institution Phase III trial comparing decitabine with supportive care (19). The Institutional
Review Boards at The University of Texas MD Anderson Cancer Center and The University of
Chicago approved each institution’s respective protocols, and all patients gave informed
consent for the collection of residual tissues as per institutional guidelines and in accordance
with the Declaration of Helsinki.
Mutation analysis
For TET2 gene analysis, polymerase chain reaction (PCR) and direct sequencing of exons
3-11 were performed starting from 20 ng of genomic DNA, as previously described (1). PCR
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amplicons were sequenced by Beckman Coulter Genomics (Beckman Coulter Genomics, MA).
All TET2 mutations were scored on both strands. Sequence traces were analyzed with SeqMan
Pro (DNASTAR, Inc., WI) and reviewed visually. TET2 anomalies were numbered according to
the European Molecular Biology Laboratory nucleotide sequence reference FM992369.
Previously annotated single nucleotide polymorphisms in the HapMap database (20) were
discarded. SIFT software (21) was used to determine the probability that a particular amino acid
substitution is tolerated. We used pyrosequencing to analyze mutations of the R132 residue in
IDH1, and residues R140 and R173 in IDH2 which have been reported in MDS (22) and
glioblastoma (23). Mutations encoding amino acid R882 residue in the DNMT3A gene (24,25)
were analyzed by pyrosequencing. Primer sequences are listed in Supplementary Table 1.
Digital restriction enzyme analysis of methylation (DREAM)
Genome-wide DNA methylation analysis using next-generation sequencing (18,26) was
performed for 20 samples for which a sufficient amount of DNA was available. Briefly, genomic
DNA (5 μg) was digested with 5 μl of FastDigest SmaI endonuclease (Fermentas, MD) for 3 h at
37 °C. Subsequently, 50 U (5 μl) of XmaI endonuclease (NEB) was added, and digestion
continued for an additional 16 h. The digested DNA was purified using a QIAquick PCR
purification kit (Qiagen, CA). The 3′ recessed ends of the DNA created by XmaI digestion were
filled in with 3′-dA tails added by Klenow DNA polymerase lacking 3′-to-5′ exonuclease activity
(New England Biolabs, MA) and a dCTP, dGTP, and dATP mix (0.4 mM of each). Illumina paired-
end sequencing adaptors were ligated using Rapid T4 DNA ligase (Enzymatics, MA). The ligation
mix was size-selected by electrophoresis in 2% agarose. A slice corresponding to a 250- to 500-
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bp window, according to the DNA ladder, was cut out, and DNA was extracted from the
agarose. Eluted DNA was amplified with Illumina paired-end PCR primers using iProof High-
Fidelity DNA Polymerase (Bio-Rad, CA) and 18 cycles of amplification. The resulting sequencing
library was cleaned with AMPure magnetic beads (Beckman Coulter Genomics) and sequenced
on an Illumina Genome Analyzer II or HiSeq 2000 (Illumina, CA). Sequencing reads were
mapped to SmaI sites in the human genome (hg18), and signatures corresponding to
methylated and unmethylated CpGs were enumerated for each SmaI site. Methylation
frequencies for individual SmaI sites were then calculated. The methylation ratio is the ratio of
the number of tags starting with CCGGG divided by the total number of tags mapped to a given
SmaI site. We used at least 10 sequencing reads to analyze methylation levels at individual SmaI
sites. Based on technical replicate experiments (data not shown), we could distinguish
differences in methylation of >10% with an FDR of 2.4%. We used the UCSC definition of CpG
islands: GC content of 50% or greater, length > 200 bp, ratio greater than 0.6 of observed
number of CG dinucleotides to the expected number on the basis of the number of Gs and Cs in
the segment (27). Sites at promoter regions are defined as being located within -1 kb to +500 b
from transcription start sites (TSS) of RefSeq genes.
Identification of Tet2-DMCs
P-values for the methylation difference between TET2-MT and WT for each CpG site were
calculated using a combinatorial approach. We created 1000 pseudo-datasets by sampling
patients between the two groups of TET2 mutant and TET2 wild type patients without
replacement. For each CpG site we created the sampling distribution of the average difference
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between methylation levels in group one and group two. The proportion of shuffled datasets
where the difference was greater than or equal to the real difference was defined as the p-
value. We identified 506 CpG sites with a p-value less than or equal to 0.01. After we set the
cutoff for the minimum difference of 5% to exclude low level changes of uncertain significance,
the number of CpG sites differentially methylated in TET2 mutant and TET2 wild type CMML
patients dropped from 506 to 472. We applied the same approach to a control dataset
generated from 20 samples of total white blood cells (WBC) from age-matched healthy subjects
to evaluate the probability of obtaining 472 differentially methylated CpG sites by creating
random combinations of 2 groups containing 8 and 12 samples. After reshuffling all possible
125970 combinations and applying the filter for a minimum 5% difference between the two
groups, the probability of obtaining 472 differentially methylated CpG sites by random
reshuffling data from healthy WBCs was 0.0074 (935 combinations of 125970 total).
Quantitative DNA methylation analyses by bisulfite pyrosequencing
We used bisulfite pyrosequencing to quantitatively assess DNA methylation (28) for
differentially methylated genes from the DREAM analysis as well as for AIM2 and SP140 from a
previous report.(16) We analyzed the same sites of these genes which were analyzed by
DREAM. The number of patients with successful results (mostly >90% success rate) varied
slightly for each gene. Primer sequences are listed in Supplementary Table 1.
Quantitative real-time PCR
RNA was isolated using Trizol (Invitrogen, NY). cDNA was synthesized using High Capacity cDNA
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Reverse Transcription Kits (Applied Biosystems, NY). Quantitative PCR was performed on a
StepOne™ Real-time PCR System (Applied Biosystems) using SYBR Green gene expression
assays (Bio-Rad). Gene expression data was normalized to GAPDH. Primer sequences are listed
in Supplementary Table 1.
Gene Set Enrichment Analysis (GSEA)
For Gene Set Enrichment Analysis (GSEA), gene sets were downloaded from the Broad
Institute’s MSigDB website (29). Gene set permutations were used to determine the statistical
enrichment of the gene sets using the difference in gene expression between TET2-MT and
TET2-WT cases in a gene expression microarray data with TET2 mutational status in the
previous study was used (30).
Analysis for enrichment of transcription factor binding sites and enhancer sites
The locations of transcription factor binding sites and enhancer sites were downloaded
from ENCODE project data (31). We annotated individual SmaI sites in the DREAM analysis
depending upon whether each site was in peaks for transcription factors and enhancers
(marked by H3K4me1). We calculated fold enrichment scores of tet2-DMCs over all sites
analyzed for each transcription factor binding site and enhancer site. We also mapped
individual SmaI sites in the DREAM analysis to regulatory regions by through publicly available
Ensembl Regulatory Build data (32).
5hmc pull-down assay followed by qPCR
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The 5-hmC affinity purification was performed as previously described (33). Briefly,
sonicated genomic DNA (15-30 µg) was labeled with chemically modified uridine
diphosphoglucose glucose (UDP-6-N3-Glu). The click chemistry reaction was performed by
addition of 150 μM Biotin-S-S-DBCO (dibenzylcylooctyne). After pull-down with streptavidin
magnetic beads (Dynabeads, MyOne Streptavidin C1, Invitrogen), DNA was released with 50
mM DTT and purified by MinElute Reaction Cleanup Kit (Qiagen). DNA concentration after
affinity enrichment was measured by the Quant-iT PicoGreen dsDNA quantitation assay
(Invitrogen). The enrichment for target loci was assessed by qPCR using the Power SYBR Green
assay (Applied Biosystems) and the 7500 Applied Biosystems PCR machine. Primer sequences
are listed in Supplementary Table 1.
Statistical analysis
Statistical analyses were performed using PRISM (GraphPad Software, Inc., CA). We
used the Mann-Whitney test to compare continuous variables of DNA methylation levels
between TET2-MT and TET2-WT cases. All p values were two-tailed. Unsupervised hierarchical
analyses were performed by ArrayTrack (http://edkb.fda.gov/webstart/arraytrack/) with
standard criteria. Principal component analysis was performed in R using the princomp function
in the stats package. Fisher's exact test was used to calculate the enrichment of tet2-DMCs over
all sites analyzed for enhancers and transcription factors within known regulatory regions of
CD14+ monocytes and the GM12878 lymphoblastoid cell line.
Results
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TET2 mutation status in CMML
We first analyzed the mutational status of the TET2 coding sequence (exons 3-11) in
samples from 20 patients with CMML, according to WHO criteria. TET2 missense or nonsense
mutations were detected in 8 out of 20 patients (40%) studied. Five patients had a single
heterozygous mutation, two had a biallelic or homozygous mutation, and one had two
mutations. Altogether, nine mutations were identified, including two missense, four nonsense,
and three frameshift mutations. Detailed mutation information is shown in Table 1. Using SIFT
software (21), both of the identified missense mutations were predicted to affect protein
function. Furthermore, we identified an IDH2 R140Q mutation in 1 out of 20 CMML patients
(5%), and a mutation at the R882 residue in DNMT3A was found in the same patient (TET2-WT).
Genome-wide DNA methylation analysis
We employed digital restriction enzyme analysis of methylation (DREAM) (18) using
next-generation sequencing, which allowed us to identify differentially methylated sites in the
human genome for TET2-MT and TET2-WT cases at high resolution, independently of bisulfite
treatment. From all the samples used for DREAM, 5-94 million unique usable reads (quality
filtered and aligned to the human genome) were successfully generated for DNA methylation
analyses (Supplementary Table 2). Supplementary Fig. 1 shows representative DREAM data for
DNA methylation from two TET2-MT and two TET2-WT cases compared to normal peripheral
blood. Compared to normal blood, hypermethylation in CGIs and hypomethylation in NCGIs
were found in a considerable number of sites in all patients regardless of TET2 mutation status
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(Supplementary Fig. 1). This was also the case when averages of DNA methylation levels in each
population were analyzed. Direct comparison of averages of DNA methylation in TET2-MT
versus TET2-WT cases revealed a slight increase of CGI hypermethylation in TET2-WT cases and
NCGI hypermethylation in TET2-MT cases (Fig. 1). There were no differences in the methylation
status of 7 classes of repeat sequences examined (SINE, LINE, LTR, etc.) (Supplementary Fig. 2).
Next, we analyzed 38,282 CpG sites (all those with >9 reads in all 20 patients) alongside
those in 5 normal blood samples using unsupervised hierarchical clustering analysis. Using
either all sites (Supplementary Fig. 3a) or only the most variable sites (Fig. 2a), we found that
while normal blood and CMML clustered separately, TET2-MT and TET2-WT cases were not
clearly separated. This was also the case for a setting which analyzed CGI or NCGI sites
separately (Supplementary Fig. 3b and Supplementary Fig. 3c and Figs. 2b and 2c), suggesting
that TET2 mutations do not explain genome-wide differences in DNA methylation in CMML.
PCA analysis also showed interspersed patterns between TET2-MT and TET2-WT while normal
peripheral blood controls are tightly clustered and separated from CMML patients (Fig.2d, 2e,
and 2f).
Differentially methylated sites in TET2 mutants are mostly NCGIs
To dissect the difference in DNA methylation between TET2-MT and TET2-WT cases
more systematically, we calculated average DNA methylation in each population for each site
analyzed which had >9 reads in more than 10 out of 20 patients. In this setting, 85,134 CpG
sites were analyzed (28,114 sites in CGIs and 57,020 sites in NCGIs). Volcano plots of these sites
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revealed that TET2-MT cases have more NCGI methylated sites, while minor differences were
seen in CGIs (Figs. 3a, 3b, and 3c). Using permutation analysis to control for overfitting, we
found 472 CpG sites (0.55%) which were differentially methylated in TET2-MT and TET2-WT
cases (TET2-specific differentially methylated CpGs; tet2-DMCs) (see Methods). We found more
methylated sites in TET2-MT (375 sites) than in TET2-WT cases (97 sites), supporting previous
findings of an increased amount of 5mC in TET2-MT compared to TET2-WT cases.(16)
Interestingly, we found a strikingly different biology at CGI and NCGI sites. Of these tet2-DMCs,
13% are in CGIs (63 sites) and 87% are in NCGIs (409 sites). Thus, 0.22% of CGI sites and 0.71%
of NCGI sites were affected by TET2 mutations (p<0.0001 for the difference between CGI and
NCGI). Furthermore, among the 63 CGI sites, 62% were more methylated in TET2-WT (39 sites).
By contrast, among the 409 NCGI sites, 86% (351 sites) were more methylated in TET2-MT
(p<0.0001). The differences were confirmed in additional cases by bisulfite-pyrosequencing for
several genes (Figs. 3d and 3e). Next, we performed supervised hierarchical clustering analysis
for only tet2-DMCs and found clearer clusters than in the analysis of all the sites
(Supplementary Fig. 4 and Fig. 2), suggesting that TET2 mutations mostly affect tet2-DMCs.
To further gain insight into the characteristics of tet2-DMCs, we compared their DNA
methylation levels to that seen in normal blood. This analysis gave us an idea of the role of the
temporal and spatial difference of tet2-DMCs in leukemogenesis in CMML. We again found a
striking difference between CGIs and NCGIs. Tet2-DMCs in CGI sites were often unmethylated in
normal blood and hypermethylated in TET2-WT cases but not in TET2-MT cases (Fig. 4a). On the
other hand, there is a considerable number of tet2-DMCs in NCGIs whose DNA methylation
levels in normal blood range from intermediate to high (Fig. 4b). For these sites, TET2-WT cases
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showed hypomethylation, while TET2-MT cases showed identical to higher methylation levels
when compared to normal blood. To clarify these findings, we plotted the difference in DNA
methylation between TET2-MT and TET2-WT cases for tet2-DMCs against DNA methylation
levels in normal peripheral blood. Overall, tet2-DMCs in CGIs are mainly the sites whose
methylation levels are higher in TET2-WT cases than in TET2-MT cases, and which are
unmethylated in normal blood (Fig. 4c). By contrast, tet2-DMCs in NCGIs are the sites whose
methylation levels are higher in TET2-MT than in TET2-WT cases, and are intermediately to fully
methylated in normal blood (Fig. 4d). In other words, TET2-WT CMML is characterized by loss of
methylation at these normally methylated sits while TET2-MT CMML shows preserved or
enhanced methylation at these sites.
Functional relevance of TET2 mutations to leukemogenesis in CMML
To address the possible mechanism of TET2 mutations in leukemogenesis in CMML, we
sought a functional relevance for methylated sites in TET2-MT cases by correlating them with
gene expression levels in TET2-MT, TET2-WT, and normal peripheral blood. We looked at
expression profiles for genes with tet2-DMCs at their promoters (-1,000 bp to +500 bp from
transcription start site), since DNA methylation at promoters is well known to be correlated
with gene expression. We measured gene expression levels for GGA2, AIM2, and SP140 which
have NCGI promoters hypermethylated in TET2-MT cases. Within the complement of NCGI
sites, TET2 mutations affected promoters and non-promoter sites equally. Indeed, the CpG sites
previously validated as potential TET2 targets were AIM2 and SP140, both in NCGI
promoters.(16) As expected, we found a strong correlation between DNA methylation and gene
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repression (Fig. 5a). We also selected three genes with tet2-DMCs in NCGIs more methylated in
TET2-MT at non-promoter regions and found that gene expression changes also correlated with
DNA methylation at these sites (Fig. 5b). Interestingly, TET2-WT cases showed expression levels
similar to TET2-MT cases in at least two out of three genes analyzed, but had lower DNA
methylation levels compared to TET2-MT cases.
Finally, to understand the mechanisms by which these genes are regulated by tet2-
DMCs in NCGI, we focused on enhancers and transcription factors which are known to control
gene expression in cis and trans from distal regions such as gene-body and outside the
genes.(34) We found that 25% (104 sites out of 409) of tet2-DMCs in NCGI were shown to be at
enhancer sites marked by H3K4me1 in a lymphoblastoid cell line, which was significantly
enriched compared to all NCGI sites analyzed (16%, p<0.001) (Fig. 5c and Supplementary Fig. 5).
We also noticed that these sites are localized at several transcription factor binding sites as
well. Among all analyzed, binding sites for p300 in the lymphobastoid cell line were co-localized
with 2.0% of these sites, which was a fourfold enrichment compared to all NCGI sites analyzed
(0.5%, p=0.001) (Fig. 5d and Supplementary Fig. 5). We also observed that tet2-DMCs in NCGIs
were significantly enriched in enhancer regions and in regions flanking promoters but depleted
within active promoters in CD14+ monocytes and the GM12878 lymphoblastoid cell line
(supplementary Fig. 6). No enrichment was observed in CTCF binding sites. Altogether, our data
suggest that methylation at tet2-DMCs in NCGIs is linked with dysregulation of gene expression
through altering hematopoietic specific transcription factor binding sites and enhancers.
Decreased 5hmc at tet2-DMCs in TET2-MT cases
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Since neither DREAM nor bisulfite-pyrosequencing can discriminate 5hmc from 5mc, we
developed an assay for assessing enrichment of 5hmc at tet2-DMCs by 5hmc labeling followed
by an affinity enrichment method. We quantified 5hmc amounts at 6 tet2-DMCs in 13 CMML
patients (4 TET2-MT and 9 TET2-WT cases) and found that 3 out 6 tet2-DMCs analyzed showed
significantly lower 5hmc enrichment in TET2-MT cases (Figure 6a) while the remaining 3 tet2-
DMCs also showed a trend toward lower 5hmc enrichment in TET2-MT cases. We calculated a
z-score of 5hmc enrichment for each tet2-DMC and found that average of z-scores for 6 tet2-
DMCs in TET2-MT cases were significantly lower than TET2-WT cases (median z-scores -0.73 vs
0.40, P=0.03) (Figure 6b). Hierarchical clustering analysis of 5hmc enrichment clearly separated
subsets of TET2-MT cases and TET2-WT cases (Figure 6c).
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Discussion
Genome-wide screening for DNA methylation by DREAM for eight TET2-MT and twelve
TET2-WT cases revealed that the general tumor phenotype in DNA methylation,
hypermethylation in CGIs and hypomethylation in NCGIs, is found in patients with both TET2-
MT and TET2-WT. Unsupervised hierarchical clustering analysis revealed that TET2-MT and
TET2-WT cases are not clearly separated, suggesting that the effects of TET2 mutations are
relatively minor in the human CMML methylome. We moved on to further analysis to clarify the
characteristics of the difference in DNA methylation between TET2-MT and TET2-WT cases. We
found that 0.55% of all sites analyzed were differentially methylated with a high proportion of
hypermethylation in TET2-MT, supporting previous findings of an increased amount of 5mC in
mutants compared to wild-types (16).
Strikingly, tet2-DMCs are primarily at NCGI sites that had intermediate to high DNA
methylation levels in normal blood. For these sites, TET2-WT cases showed hypomethylation,
while TET2-MT cases showed identical to higher methylation levels compared to normal blood.
This suggests that TET2 mutations block hypomethylation in NCGIs during tumorigenesis.
Interestingly, while TET1 has a CXXC domain responsible for binding to unmethylated sites, and
is enriched at CGIs (35,36), TET2 lacks the CXXC domain (37). This could explain why TET2
mutations primarily affect NCGI sites that are methylated in normal blood.
To gain insight into the functional relevance of TET2 mutations in leukemogenesis, we
investigated the effects of tet2-DMC methylation on gene expression. Although we found
relatively small numbers of genes with tet2-DMCs at promoter regions, we found good
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Yamazaki et al., p 19
correlations between DNA methylation and gene expression. Importantly, these genes have
promoters in NCGIs. We also found a substantial number of NCGI sites hypermethylated at non-
promoter regions in TET2-MT cases and found good correlations between DNA methylation and
gene expression which are validated for three genes with tet2-DMCs in NCGIs at non-promoter
regions. Interestingly, TET2-WT cases showed expression levels similar to TET2-MT cases in at
least two out of three genes analyzed, but had lower DNA methylation levels compared to
TET2-MT cases, implying that there might be mechanisms different from DNA methylation
which result in gene expression deregulation in CMML.
This finding prompted us to analyze the link to transcription factor binding sites (and
enhancers), which are known to control gene expression in cis and trans from distal regions in
order to achieve precise differentiation (34). We found that tet2-DMCs in NCGI case are
significantly enriched in enhancer sites—both in a lymphoblastoid cell line and in normal
monocytes— as well as in binding sites for transcription factors including p300. These data
suggest that methylation at tet2-DMCs in NCGIs might lead to reduced binding of p300 to
target sites, which in turn deregulate gene expression for nearby genes. In support of this, we
found that differentially expressed genes in TET2-MT and TET2-WT cases are enriched in
interferon-related genesets, which are known to be downstream targets of p300
(Supplementary Fig. 7) (38). Furthermore, it has recently been shown that approximately half of
5-hydroxymethylcytosines are located in distal regulatory elements immediately adjacent to
the binding sites of transcription factors such as p300 and CTCF (35,39-41). It is also worth
noting that two recent papers also reported preferential regulation of enhancer DNA
methylation by TET2 (42,43), which is very consistent with our findings.
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Yamazaki et al., p 20
Since the methods we used for identifying tet2-DMCs are not capable of discriminating
5hmc from 5mc and it is important to prove that not only 5mc but also 5hmc is variable at tet2-
DMCs, we developed a 5hmc pull-down assay to address this question. We found that 5hmc
amounts at tet2-DMCs are significantly lower in TET2-MT than TET2-WT cases, suggesting that
TET2 mutations lead to enzymatically deficient TET2 function at tet2-DMCs. Other covalently-
modified cytosines such as 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC) are also
possibly variable between TET2-MT and TET2-WT, and this has to be verified with more
sensitive and specific assays to detect these further oxidized derivatives which are far lower in
quantity.
The effect of TET2 mutations on DNA methylation has been controversial. There are
three reports in CMML including our previous report, two out of which supported
hypermethylation phenotypes in TET2-MT CMML (16,17) whereas one report showed a
remarkable hypomethylation phenotype in TET2-MT CMML (12). The dominant
hypermethylation phenotypes were also supported by three other reports in AML (13), diffuse
large B-cell lymphoma (44), and normal elderly individuals (45). Among these, only one report
(44) utilized genome-wide profiling of DNA methylation like our method and showed that TET2
mutations were primarily associated with hypermethylation within CGI and CpG-rich
promoters. Although our data where we found tet2-DMCs was associated with NCGI sites is
inconsistent with this result, it is possible that the effects of TET2 mutations could vary
depending on differences in disease-origins such as myeloid and lymphoid cells. Similar studies
with genome-wide profiling need to be performed to clarify this question.
Although the fact that we have used normal peripheral blood rather than sorted cells as a
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Yamazaki et al., p 21
control is a drawback of our study, we were mostly interested in a case-case comparison of
TET2-WT to TET2-MT CMML, which is unaffected by normal peripheral blood data. Moreover,
we have previously shown that there are very few differences in methylation between bone
marrow and blood in MDS and CMML and relatively little variation in methylation in different
subsets in blood from patients with AML (sorted CD34+ and CD3–/19– cells) (46).
In conclusion, our data suggest that TET2 mutations have a minor effect (<1%) on DNA
methylation throughout the human genome, and preferentially result in hypermethylation at
selected NCGI sites that are enriched at transcription factor binding sites and enhancers. Our
data are consistent with a model whereby transcription factors such as p300 recruit TET2 as
part of their mechanism of gene regulation, and provide an explanation for the differentiation
block seen in TET2 mutant hematopoietic cells.
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Yamazaki et al., p 22
Acknowledgements
The authors thank the patients who have contributed to our understanding of these disorders
and thank Jenna Al-Malawi for excellent editing of the manuscript. This work was supported by
the National Institutes of Health grants CA100632, CA121104, and CA049639 (to JPI) and
CA129831 and CA129831-03S1 (to LAG), and supported by a Stand Up to Cancer grant from the
American Association for Cancer Research. JPI is an American Cancer Society Clinical Research
professor supported by a generous gift from the F. M. Kirby Foundation.
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Yamazaki et al., p 23
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Table 1. TET2 mutation status of the samples studied for DREAM
MT; mutant * Biallelic/homozygous mutations † Previously reported
Patient Nucleotide change Amino acid change
MT1 c.5163C>T Q1435X*
MT2 c.4435G>T G1192V
MT3 Ins c.2519 (G) V553FS
MT4 c.5109G>T V1417F*†
MT5 Del 3509_3510 (TC) F883FS
MT6 4914 G>T E1352X
MT7 2508 C>T R550X†
MT8 4506 C>T R1216X
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Figure Legends
Figure 1. Scatter plots for DNA methylation levels analyzed by DREAM. Average DNA
methylation levels of TET2-MT versus TET2-WT in CGIs (upper) and in NCGIs (lower).
Differentially methylated sites are shown in black dots. R2 values are denoted in the upper right
corner.
Figure 2. Unsupervised hierarchical analyses for DNA methylation levels with the top 1,000
variable sites. Samples include eight TET2-MT (shown in red), twelve TET2-WT (blue), and five
normal bloods (green) (and an average of the five normal bloods) analyzed by DREAM. The
sample from the patient with IDH2/DNMT3A mutations is shown in purple. Sites in (a)
CGIs+NCGIs, (b) CGIs, and (c) NCGIs were used. Also shown is Principal Component Analysis of
DNA methylation at the 1000 most variable CpG sites in all sites analyzed (d), CGI sites (e), and
NCGI sites (f). TET2-WT patients (blue) and TET2-MT patients (red) are interspersed while
normal peripheral blood controls (NPB, green) are tightly clustered and separated from CMML
patients. The axes show loadings of the first two principal components and their scale is
arbitrary.
Figure 3. Difference in DNA methylation levels of TET2-MT and TET2-WT cases. Volcano plots
with the difference in DNA methylation between averages of TET2-MT versus TET2-WT on the
x-axis, and the unadjusted p-value for each site on the y-axis for (a) sites in CGIs+NCGIs, (b) sites
in CGIs, and (c) sites in NCGIs. Also shown is validation of DNA methylation levels by bisulphite-
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Yamazaki et al., p 28
pyrosequencing for (d) two tet2-DMCs in CGIs and (e) three tet2-DMCs in NCGIs, for TET2-MT
and TET2-WT cases. DNA methylation levels of normal blood samples are shown in the light
gray rectangle.
Figure 4. DNA methylation levels in TET2-MT and TET2-WT cases and their change compared to
normal blood. Scatter plots for the DNA methylation levels of tet2-DMCs in (a) CGIs and (b)
NCGIs in TET2-MT and TET2-WT cases versus normal blood. Each dot represents each tet2-DMC
in TET2-MT (red) and TET2-WT (blue). Heatmaps for tet2-DMCs in (c) CGIs and (d) NCGIs with
the difference in DNA methylation between TET2-MT and TET2-WT plotted on the x-axis, DNA
methylation levels in normal blood plotted on the y-axis, and their density in number of tet2-
DMCs (density increases from blue to red).
Figure 5. Deregulation of gene expression for genes with tet2-DMCs and their enrichment at
enhancer and transcription factor binding sites. Shown are correlations between DNA
methylation and gene expression for genes with tet2-DMCs at promoters (a) and non-
promoters (b). Red: TET2-MT; blue: TET2-WT; green: normal blood. The sample from the
patient with IDH2/DNMT3A mutations is shown in purple. Gene expression levels are calculated
as 40- ∆CT to GAPDH. Linear regression curve is indicated with a black line. (c) Enrichment for
enhancer sites (marked by H3K4me1) with tet2-DMCs in NCGIs over all sites analyzed in NCGIs
in several cell lines. Red bars indicate significant enrichment. (d) Enrichment for transcription
factor binding sites in GM12878, a lymphoblastoid cell line, with tet2-DMCs in NCGIs over all
sites analyzed s in NCGIs. Red bar indicates significant enrichment.
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Figure 6. Decreased 5hmc enrichment at tet2-DMCs in TET2-MT cases. 5hmc enrichment scores
of (a) each tet2-DMC or (b) average of 6 tet2-DMCs in TET2-MT and TET2-WT cases. (c)
Unsupervised hierarchical clustering analysis for 5hmc enrichment scores from TET2-MT and
TET2-WT cases.
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Published OnlineFirst May 13, 2015.Cancer Res Jumpei Yamazaki, Jaroslav Jelinek, Yue Lu, et al. myelomonocytic leukemiaenhancers and transcription factor binding sites in chronic TET2 mutations affect non-CpG island DNA methylation at
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