Download TET2 Mutations Affect Non-CpG Island DNA

Published OnlineFirst May 13, 2015; DOI: 10.1158/0008-5472.CAN-14-0739
Cancer
Research
Molecular and Cellular Pathobiology
TET2 Mutations Affect Non-CpG Island DNA
Methylation at Enhancers and Transcription
Factor–Binding Sites in Chronic Myelomonocytic
Leukemia
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
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 (CGI) and 57,020 in non-CpG islands
(NCGI)]. 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. In 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 nonpromoters repressed gene expression.
Tet2-DMCs showed significant enrichment at hematopoieticspecific 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. Cancer
Introduction
dence of TET2 gene alterations ranges from 10% to 50% in
myeloid malignancies, with the highest frequency of mutations
found in CMML, where TET2 mutations were noted in 35% to
50% of cases (5–7). As first reported for TET1 (8), TET2 converts
5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC;
ref. 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; ref. 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 islands (CGI; ref. 16). The
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; ref. 1), and were later also described in systemic
mastocytosis (2), chronic myelomonocytic leukemia (CMML;
ref. 3), myelodysplastic syndrome (MDS; ref. 4), MDS/MPN
(5), and acute myelogenous leukemia (AML; ref. 6). The inci-
1
Fels Institute for Cancer Research and Molecular Biology, Temple
University, Philadelphia, Pennsylvania. 2Department of Leukemia, The
University of Texas MD Anderson Cancer Center, Houston, Texas.
3
Department of Molecular Carcinogenesis, The University of Texas MD
Anderson Cancer Center, Houston, Texas. 4Section of Hematology/
Oncology, Department of Medicine, The University of Chicago,
Chicago, Illinois. 5Department of Bioinformatics and Computational
Biology,The University of Texas MD Anderson Cancer Center, Houston,
Texas.
Note: Supplementary data for this article are available at Cancer Research
Online (http://cancerres.aacrjournals.org/).
Corresponding Author: Jean-Pierre J. Issa, Fels Institute for Cancer Research
and Molecular Biology, Temple University, 3307 N. Broad Street, Room 154
Pharmacy Allied Health Building, Philadelphia, PA 19140. Phone: 215-707-1454;
Fax: 215-707-1454; E-mail: [email protected]
doi: 10.1158/0008-5472.CAN-14-0739
2015 American Association for Cancer Research.
Res; 75(14); 2833–43. 2015 AACR.
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Yamazaki et al.
genome-wide studies reporting conflicting results used microarray analysis with limited validation. Here, we used the quantitative, sequence-based digital restriction enzyme analysis of
methylation (DREAM; ref. 18) method to study this issue and
found that hypermethylated sites in TET2-MT are mostly in
non-CpG islands (NCGI) and are enriched at hematopoieticspecific enhancers marked by H3K4me1, and at binding sites
for the transcription factor p300.
Patients and Methods
Patients
We analyzed whole bone marrow or peripheral blood samples (bone marrow was not available in one TET2-WT case)
before treatment from 40 patients with CMML referred to The
University of Texas MD Anderson Cancer Center (Houston,
TX) or The University of Chicago (Chicago, IL), or enrolled
in a multi-institution phase III trial comparing decitabine
with supportive care (19). The Institutional Review Board 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, PCR and direct sequencing of exons
3–11 were performed starting from 20 ng of genomic DNA, as
previously described (1). PCR amplicons were sequenced by
Beckman Coulter Genomics (Beckman Coulter Genomics). All
TET2 mutations were scored on both strands. Sequence traces
were analyzed with SeqMan Pro (DNASTAR, Inc.) and reviewed
visually. TET2 anomalies were numbered according to the
European Molecular Biology Laboratory nucleotide sequence
reference FM992369. Previously annotated SNPs 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 S1.
Digital restriction enzyme analysis of methylation
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 mg) was digested with 5 mL of FastDigest SmaI endonuclease
(Fermentas) for 3 hours at 37 C. Subsequently, 50 U (5 mL) of
XmaI endonuclease (NEB) was added, and digestion continued
for an additional 16 hours. The digested DNA was purified using
a QIAquick PCR Purification Kit (Qiagen). The 30 recessed ends
of the DNA created by XmaI digestion were filled in with 30 -dA
tails added by Klenow DNA polymerase lacking 30 -to-50 exonuclease activity (New England Biolabs) and a dCTP, dGTP, and
dATP mix (0.4 mmol/L of each). Illumina paired-end sequencing
adaptors were ligated using Rapid T4 DNA ligase (Enzymatics).
The ligation mix was size selected by electrophoresis in 2%
agarose. A slice corresponding to a 250- to 500-bp window,
2834 Cancer Res; 75(14) July 15, 2015
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) 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). 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. On the basis of 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 þ500 b from
transcription start sites 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 1,000 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
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 two groups containing 8 and 12 samples. After
reshuffling all possible 125,970 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 125,970 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 that 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 S1.
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Effects of TET2 Mutations on DNA Methylation in CMML
Quantitative real-time PCR
RNA was isolated using TRIzol (Invitrogen). cDNA was synthesized using High Capacity cDNA Reverse Transcription Kits
(Applied Biosystems). qPCR was performed on a StepOne Realtime PCR System (Applied Biosystems) using SYBR Green gene
expression assays (Bio-Rad). Gene expression data were normalized to GAPDH. Primer sequences are listed in Supplementary
Table S1.
Gene Set Enrichment Analysis
For Gene Set Enrichment Analysis, 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 from a previous
study (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 publicly available
Ensembl Regulatory Build data (32).
5hmC pull-down assay followed by qPCR
The 5hmC affinity purification was performed as previously
described (33). Briefly, sonicated genomic DNA (15–30 mg)
was labeled with chemically modified uridine diphosphoglucose glucose (UDP-6-N3-Glu). The click chemistry reaction was
performed by addition of 150 mmol/L Biotin-S-S-DBCO
(dibenzylcylooctyne). After pull-down with streptavidin magnetic beads (Dynabeads, MyOne Streptavidin C1, Invitrogen),
DNA was released with 50 mmol/L 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 S1.
regulatory regions of CD14þ monocytes and the GM12878
lymphoblastoid cell line.
Results
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 used 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 to 94 million unique usable reads (quality filtered
and aligned to the human genome) were successfully generated
for DNA methylation analyses (Supplementary Table S2). Supplementary Figure S1 shows representative DREAM data for
DNA methylation from two TET2-MT and two TET2-WT cases
compared with normal peripheral blood. Compared with 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 (Supplementary Fig. S1).
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. S2).
Next, we analyzed 38,282 CpG sites (all those with >9 reads in
all 20 patients) alongside those in five normal blood samples
using unsupervised hierarchical clustering analysis. Using either
Table 1. TET2 mutation status of the samples studied for DREAM
Statistical analysis
Statistical analyses were performed using PRISM (GraphPad
Software, Inc.). We used the Mann–Whitney test to compare
continuous variables of DNA methylation levels between TET2MT 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 exact test was
used to calculate the enrichment of tet2-DMCs over all sites
analyzed for enhancers and transcription factors within known
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Patient
MT1
MT2
MT3
MT4
MT5
MT6
MT7
MT8
Nucleotide change
c.5163C>T
c.4435G>T
Ins c.2519 (G)
c.5109G>T
Del 3509_3510 (TC)
4914 G>T
2508 C>T
4506 C>T
Amino acid
change
Q1435Xa
G1192V
V553FS
V1417Fa,b
F883FS
E1352X
R550Xb
R1216X
Abbreviation: MT, mutant.
a
Biallelic/homozygous mutations.
b
Previously reported.
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Figure 1.
Scatter plots for DNA methylation levels analyzed by DREAM. Average DNA
methylation levels of TET2-MT versus TET2-WT in CGIs (top) and in
NCGIs (bottom). Differentially methylated sites are shown in black dots.
2
R values are denoted in the top right corner.
all sites (Supplementary Fig. S3A) or only the most variable
sites (Fig. 2A), we found that although normal blood and
CMML clustered separately, TET2-MT and TET2-WT cases were
not clearly separated. This was also the case for a setting that
analyzed CGI or NCGI sites separately (Fig. 2B and C and
Supplementary Fig. S3B and S3C), 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–F).
Differentially methylated sites in TET2 mutants are mostly
NCGIs
To dissect the difference in DNA methylation between TET2MT and TET2-WT cases more systematically, we calculated
2836 Cancer Res; 75(14) July 15, 2015
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
revealed that TET2-MT cases have more NCGI methylated sites,
whereas minor differences were seen in CGIs (Fig. 3A–C).
Using permutation analysis to control for overfitting, we found
472 CpG sites (0.55%) that were differentially methylated in
TET2-MT and TET2-WT cases (TET2-specific differentially methylated CpGs; tet2-DMCs; see Materials and 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 with 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). In 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 (Fig. 3D and E). 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. S4), 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 showed
hypomethylation, whereas TET2-MT cases showed identical to
higher methylation levels when compared with normal blood.
To clarify these findings, we plotted the difference in DNA
methylation between TET2-MT and TET2-WT cases for tet2DMCs 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 TET2MT cases and that are unmethylated in normal blood (Fig. 4C).
In 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, whereas
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
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Effects of TET2 Mutations on DNA Methylation in CMML
Figure 2.
Unsupervised hierarchical analyses for DNA methylation levels with the top 1,000 variable sites. Samples include eight TET2-MT (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 CGIsþNCGIs (A), CGIs (B), and NCGIs (C) were used. Also shown is Principal Component Analysis of DNA methylation
at the 1,000 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.
at their promoters (1,000 bp to þ500 bp from transcription
start site), because 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
nonpromoter 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 repression (Fig. 5A).
We also selected three genes with tet2-DMCs in NCGIs more
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methylated in TET2-MT at nonpromoter 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
with 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
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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 sites in CGIsþNCGIs (A), sites in CGIs (B), and sites in NCGIs (C).
Also shown is validation of DNA methylation levels by bisulphite-pyrosequencing for two tet2-DMCs in CGIs (D) and three tet2-DMCs in NCGIs (E) for TET2-MT
and TET2-WT cases. DNA methylation levels of normal blood samples are shown in the light gray rectangle.
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 with all NCGI sites analyzed
(16%, P < 0.001; Fig. 5C and Supplementary Fig. S5). 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 colocalized with
2.0% of these sites, which was a 4-fold enrichment compared with
all NCGI sites analyzed (0.5%, P ¼ 0.001; Fig. 5D and Supplementary Fig. S5). 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 CD14þ monocytes and the GM12878 lymphoblastoid cell line (supplementary
2838 Cancer Res; 75(14) July 15, 2015
Fig. S6). 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
Because 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
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Effects of TET2 Mutations on DNA Methylation in CMML
Figure 4.
DNA methylation levels in TET2-MT and TET2-WT cases and their change compared with normal blood. Scatter plots for the DNA methylation levels of tet2-DMCs
in CGIs (A) and NCGIs (B) 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 CGIs (C) and NCGIs (D), 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).
significantly lower 5hmC enrichment in TET2-MT cases (Fig. 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 ¼ 0.03; Fig.
6B). Hierarchical clustering analysis of 5hmC enrichment clearly
separated subsets of TET2-MT cases and TET2-WT cases (Fig. 6C).
Discussion
Genome-wide screening for DNA methylation by DREAM
for eight TET2-MT and twelve TET2-WT CMML 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
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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 analyses 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 with 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, whereas TET2-MT cases showed identical to higher methylation levels compared with normal blood. This suggests
that TET2 mutations block hypomethylation in NCGIs during
tumorigenesis. Interestingly, although TET1 has a CXXC domain responsible for binding to unmethylated sites, and is
enriched at CGIs (35, 36), TET2 lacks the CXXC domain (37).
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Yamazaki et al.
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 nonpromoters (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- DCT
to GAPDH. Linear regression curve is indicated with a black line. C, enrichment for enhancer sites (marked by H3K4me1) with tet2-DMCs in NCGIs overall
sites analyzed in NCGIs in several cell lines. Red bars, 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 bars, significant enrichment.
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 tet2DMC methylation on gene expression. Although we found
relatively small numbers of genes with tet2-DMCs at promoter
regions, we found good 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 nonpromoter regions in
TET2-MT cases and found good correlations between DNA
methylation and gene expression that are validated for three
genes with tet2-DMCs in NCGIs at nonpromoter 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 with TET2MT cases, implying that there might be mechanisms different
from DNA methylation that 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
2840 Cancer Res; 75(14) July 15, 2015
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
IFN-related genesets, which are known to be downstream
targets of p300 (Supplementary Fig. S7; ref. 38). Furthermore,
it has recently been shown that approximately half of 5hydroxymethylcytosines 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.
Because the methods we used for identifying tet2-DMCs
are not capable of discriminating 5hmC from 5mC and it is
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Published OnlineFirst May 13, 2015; DOI: 10.1158/0008-5472.CAN-14-0739
Effects of TET2 Mutations on DNA Methylation in CMML
Figure 6.
Decreased 5hmC enrichment at tet2DMCs in TET2-MT cases. 5hmC
enrichment scores (A) of each tet2DMC or average (B) 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.
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 tet2DMCs 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 TET2WT, and this has to be verified with more sensitive and specific
assays to detect these further oxidized derivatives that 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 that tet2-DMCs was associated with
NCGI sites are 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 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 DNA methylation between bone marrow and blood
www.aacrjournals.org
in MDS and CMML and relatively little variation in methylation in
different subsets in blood from patients with AML (sorted CD34þ
and CD3/19 cells; ref. 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.
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Authors' Contributions
Conception and design: J. Yamazaki, H.M. Kantarjian, L.A. Godley, J.-P.J. Issa
Development of methodology: J. Yamazaki, J. Jelinek, M. Cesaroni, L.A. Godley
Acquisition of data (provided animals, acquired and managed patients,
provided facilities, etc.): J. Yamazaki, J. Jelinek, J. Madzo, F. Neumann,
R. Taby, A. Vasanthakumar, T. Macrae, K.R. Ostler, L.A. Godley
Analysis and interpretation of data (e.g., statistical analysis, biostatistics,
computational analysis): J. Yamazaki, J. Jelinek, Y. Lu, M. Cesaroni, J. Madzo,
F. Neumann, A. Vasanthakumar, K.R. Ostler, H.M. Kantarjian, S. Liang,
L.A. Godley, J.-P.J. Issa
Writing, review, and/or revision of the manuscript: J. Yamazaki, J. Jelinek,
M. Cesaroni, F. Neumann, H.M. Kantarjian, L.A. Godley, J.-P.J. Issa
Administrative, technical, or material support (i.e., reporting or organizing
data, constructing databases): J. Yamazaki, F. Neumann, R. He, M.R. Estecio,
J.-P.J. Issa
Study supervision: J. Yamazaki, J.-P.J. Issa
Acknowledgments
The authors thank the patients who have contributed to their understanding
of these disorders and thank Jenna Al-Malawi for excellent editing of the article.
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Yamazaki et al.
Grant Support
This work was supported by the NIH grants CA100632, CA121104, and
CA049639 (J.-P.J. Issa) and CA129831 and CA129831-03S1 (L.A. Godley),
and supported by a Stand Up to Cancer Dream Team Translational Research
Grant, Grant Number SU2C-AACR-DT0109. Stand Up To Cancer is a program
of the Entertainment Industry Foundation administered by the American Association for Cancer Research. J.-P.J. Issa is an American Cancer Society Clinical
Research professor supported by a generous gift from the F. M. Kirby Foundation.
The costs of publication of this article were defrayed in part by the
payment of page charges. This article must therefore be hereby marked
advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate
this fact.
Received March 16, 2014; revised February 23, 2015; accepted March 7, 2015;
published OnlineFirst May 13, 2015.
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Published OnlineFirst May 13, 2015; DOI: 10.1158/0008-5472.CAN-14-0739
TET2 Mutations Affect Non-CpG Island DNA Methylation at
Enhancers and Transcription Factor−Binding Sites in Chronic
Myelomonocytic Leukemia
Jumpei Yamazaki, Jaroslav Jelinek, Yue Lu, et al.
Cancer Res 2015;75:2833-2843. Published OnlineFirst May 13, 2015.
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