JNCI | Articles 143
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Malignant glioma is the most common form of primary malignant
brain tumor and the glioma histological subtypes include glioblas-
tomas, grades 2 and 3 astrocytomas, grades 2 and 3 oligodendro-
gliomas, grades 2 and 3 oligoastrocytomas, ependymomas, and
pilocytic astrocytomas (1). Presently, there are limited treatment
options for glioma; glioblastoma, the most common glioma
subtype, remains an incurable disease with a median survival of
15 months, even with radiation and temozolomide therapy (2).
A comprehensive appreciation of the integrated genomics and
epigenomics of glioma is needed to better understand the multiple
cellular pathways involved in their development, establish markers
of resistance to traditional therapies, and contribute to the devel-
opment of targeted therapies. Epigenetic alterations can alter gene
expression, gene expression potential, or the regulation of gene
function, and thereby contribute to gliomagenesis. Arguably, the
most widely studied epigenetic mark is DNA methylation that
occurs at cytosine residues in the context of CpG dinucleotides.
Approximately half of human genes have concentrations of CpGs
in their promoter regions and the methylation state of these and
other gene-associated CpGs are widely regarded as critical indica-
tors of gene regulation.
Since 2008, sequencing of gliomas has identified mutations in
the isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2) genes
(3–5). The IDH1 and IDH2 enzymes convert isocitrate to alpha
(a)-ketoglutarate producing NADPH and participate in cellular
metabolic processes such as glucose sensing, lipid metabolism, and
oxidative respiration [reviewed in (6)]. Mutations in IDH1 are
consistently found in codon 132 for arginine (R132), and mutations
DNA Methylation, Isocitrate Dehydrogenase Mutation, and
Survival in Glioma
Brock C. Christensen, Ashley A. Smith, Shichun Zheng, Devin C. Koestler, E. Andres Houseman, Carmen J. Marsit,
Joseph L. Wiemels, Heather H. Nelson, Margaret R. Karagas, Margaret R. Wrensch, Karl T. Kelsey, John K. Wiencke
Manuscript received April 14, 2010; revised November 5, 2010; accepted November 8, 2010.
Correspondence to: John K. Wiencke, PhD, Department of Neurological Surgery, Helen Diller Family Cancer Center, University of California San Francisco,
San Francisco, CA 91458 (e-mail: email@example.com).
Background Although much is known about molecular and chromosomal characteristics that distinguish glioma histological
subtypes, DNA methylation patterns of gliomas and their association with other tumor features such as muta-
tion of isocitrate dehydrogenase (IDH) genes have only recently begun to be investigated.
Methods DNA methylation of glioblastomas, astrocytomas, oligodendrogliomas, oligoastrocytomas, ependymomas, and
pilocytic astrocytomas (n = 131) from the Brain Tumor Research Center at the University of California San
Francisco, as well as nontumor brain tissues (n = 7), was assessed with the Illumina GoldenGate methylation
array. Methylation data were subjected to recursively partitioned mixture modeling (RPMM) to derive methyla-
tion classes. Differential DNA methylation between tumor and nontumor was also assessed. The association
between methylation class and IDH mutation (IDH1 and IDH2) was tested using univariate and multivariable
analysis for tumors (n = 95) with available substrate for sequencing. Survival of glioma patients carrying mutant
IDH (n = 57) was compared with patients carrying wild-type IDH (n = 38) using a multivariable Cox proportional
hazards model and Kaplan–Meier analysis. All statistical tests were two-sided.
Results We observed a statistically significant association between RPMM methylation class and glioma histological
subtype (P < 2.2 × 10216). Compared with nontumor brain tissues, across glioma tumor histological subtypes, the
differential methylation ratios of CpG loci were statistically significantly different (permutation P < .0001).
Methylation class was strongly associated with IDH mutation in gliomas (P = 3.0 × 10216). Compared with glioma
patients whose tumors harbored wild-type IDH, patients whose tumors harbored mutant IDH showed statisti-
cally significantly improved survival (hazard ratio of death = 0.27, 95% confidence interval = 0.10 to 0.72).
Conclusion The homogeneity of methylation classes for gliomas with IDH mutation, despite their histological diversity,
suggests that IDH mutation is associated with a distinct DNA methylation phenotype and an altered metabolic
profile in glioma.
J Natl Cancer Inst 2011;103:143–153
144 Articles | JNCI Vol. 103, Issue 2 | January 19, 2011
in IDH2 consistently occur at the analogous amino acid R172 (3,7).
Mutations in IDH1 and IDH2 (IDH when referring to both) are
unlike most cancer-associated enzyme mutations because they
confer neomorphic enzyme activity rather than inactivating, or
constitutively activating, the enzyme. The mutant form of IDH
enzymes convert a-ketoglutarate to 2-hydroxyglutarate in an
NADPH-dependent manner, and via an unknown mechanism
contribute to the pathophysiology of gliomas and leukemias
(5,7,8). IDH mutations occur in approximately 80% of grades 2–3
gliomas and secondary glioblastomas, but less than 10% of primary
glioblastomas (4,5). In gliomas, IDH mutation has been associated
with genetic alterations in other genes including tumor suppres-
sors and oncogenes (5). IDH mutation also has been associated
with younger age and improved survival in glioma patients (5,9).
The somatic genetic signature of any individual tumor is critical
to assessing its clinical and etiologic character. Similarly, the pro-
file of somatic epigenetic alterations is central to forming a com-
plete understanding of the pattern of disrupted cellular functioning
responsible for the deadly behavior of gliomas. Major advances in
the clinical role of epigenetics in gliomas include the findings that
promoter methylation silencing of the O-6-methylguanine-DNA
methyltransferase (MGMT) gene is associated with response to
temozolomide treatment (10). Epigenetic silencing of MGMT
gene is found in approximately 80% of gliomas with mutant IDH1,
compared with approximately 60% of gliomas with wild-type
IDH1 (9). Other common alterations in gliomas are mutations in
tumor protein p53 (TP53) (11) and amplification of the epidermal
growth factor receptor (EGFR) oncogene (12). Better definitions
of the somatic nature of gliomas should integrate both their ge-
netic and epigenetic alterations. In this study, we assessed CpG
methylation patterns, IDH mutation, TP53 mutation, and EGFR
amplification in histologically diverse gliomas to define epigenetic
subgroups of potential clinical and etiologic relevance.
Patients, Materials, and Methods
Patients and Tissue Samples
Fresh frozen tumor tissues of patients (n = 131) diagnosed with
glioma between 1990 and 2003 were obtained from the University
of California San Francisco (UCSF) Brain Tumor Research Center
Tissue Bank. Tumors were previously reviewed by UCSF neuro-
pathologists to assign histological subtypes and grades according to
the World Health Organization classification for patients operated
on at the UCSF Medical Center (1). Tumor samples were defined
as secondary glioblastoma if the patients had previous histological
diagnosis of a lower-grade glioma. Nontumor brain tissue samples
were obtained from cancer-free patients (n = 7) who underwent
temporal lobe resection for treatment of epilepsy at the UCSF
Medical Center. Patient ages were documented at the time of ini-
tial diagnosis. Other demographic and survival data were obtained
from UCSF patient records and the California Cancer Registry.
The Institutional Review Board approval certification was obtained
from the UCSF Committee on Human Research, and subjects
provided written informed consent for tissue collection.
Cell Lines, Cell Culture, and Reagents
A431 cells (a human epidermoid cancer cell line that is known to
have EGFR amplification and overexpression) and HT29 cells
(a human colon adenocarcinoma cell line without EGFR amplifica-
tion) were obtained from American Type Culture Collection
(Manassas, VA). Cell lines were maintained in Dulbecco’s modi-
fied Eagle medium and RPMI 1640 medium (both from Invitrogen,
Carlsbad, CA), respectively, with 10% fetal bovine serum (Hyclone,
Logan, UT) at 37°C in 5% CO2. When cultures reached 80%
confluency, cells were harvested for DNA extraction.
DNA Extraction, Bisulfite Modification, and Methylation
Genomic DNA from 131 glioma tissue samples and seven
nontumor brain tissue samples was isolated from approximately
25 mg wet weight of each frozen tissue sample using QIAamp DNA
mini kit (Qiagen, Inc, Valencia, CA) according to the manufactur-
er’s instructions. DNA was eluted twice in a total of 100 µL of elu-
tion buffer. The same DNA extraction method was applied to A431
and HT29 cell lines that served as EGFR amplification controls.
For DNA methylation analysis, 1 µg of genomic DNA was first
subjected to bisulfite modification using the EZ DNA Methylation
Kit (Zymo Research Corporation, Orange, CA) according to the
manufacturer’s instructions. Bisulfite modification converts
unmethylated cytosine residues to uracil and preserves methylated
cytosine residues as cytosines.
CONTEXT AND CAVEATS
Human gliomas often have mutations in the isocitrate dehydroge-
nase genes (IDH1 and IDH2). IDH mutation is associated with
improved survival in glioma patients. Epigenetic alterations like
DNA methylation at CpG dinucleotides play an important role in
gene regulation. Integration of genetic and epigenetic data is
important for a better understanding of glioma development.
DNA methylation profile of CpG loci and methylation class of 131
glioma and seven non-glioma brain tissues were determined. The
association between IDH mutation and methylation class was ana-
lyzed. Survival analysis of patients carrying IDH mutation vs wild-
type IDH was also performed.
CpG loci showed differential methylation between glioma and non-
glioma tissues. Statistically significant associations were found
between DNA methylation class and histological subtypes and
between DNA methylation class and IDH mutation of gliomas.
Patients carrying IDH mutation in gliomas showed improved sur-
vival compared with patients carrying IDH wild-type after adjust-
ment for age and grade-specific tumor histology.
A distinct methylation pattern in glioma tissues is associated with
Mutation data were not available for all tissue samples, which may
have limited the statistical power of the analyses.
From the Editors
JNCI | Articles 145
GoldenGate DNA methylation bead arrays (Illumina, Inc, San
Diego, CA) were used to interrogate methylation of 1505 CpG
loci associated with 803 cancer-related genes according to the
manufacturer’s instructions. GoldenGate methylation arrays were
used to analyze bisulfite-modified DNA from 131 glioma and
seven nontumor samples for methylation, and processed at the
UCSF Institute for Human Genetics, Genomics Core Facility.
The GoldenGate array methylation data were deposited in the
Gene Expression Omnibus and are publicly available (accession
GSE20395). The Cancer Genome Atlas (TCGA), a public data
portal, was used to obtain GoldenGate methylation array data for
validation of methylation classes. Quantitative methylation-
specific polymerase chain reaction (PCR) (QMSP) was used to
confirm methylation data from the GoldenGate array. Candidate
genes were selected based on previous studies (13–16) that
reported aberrant methylation in astrocytic glioma and included
MGMT, Ras association domain family member 1 (RASSF1), PYD
and CARD domain containing (PYCARD), homeobox A9
(HOXA9), paternally expressed 3 (PEG3), and slit homolog 2
(SLIT2). CpGenome Universal Methylated DNA (Millipore,
Billerica, MA) was bisulfite modified and used as a positive control
for QMSP analysis. QMSP was performed using Applied
Biosystems 7900HT Fast Real-Time PCR System (Applied
Biosystems, Carlsbad, CA). The reaction plate was prepared using
the Beckman Coulter automated liquid handler-Biomex 3000
(Beckman Coulter, Fullerton, CA). Each reaction contained
10.0 µL 2× Power SYBR Green PCR Master Mix (Applied
Biosystems), 100–400 nM of forward and reverse primers
(Supplementary Table 1, available online) and 25 ng of DNA
template in a total reaction volume of 20 µL. For the amplification
of RASSF1, 2%–3% dimethyl sulfoxide was added to the reaction
mix. PCR conditions are modified by different primer concentra-
tions, and dimethyl sulfoxide was added to ensure that primer
dimers and nonspecific amplification products were not included
in the threshold cycle (Ct) calculation. To confirm specificity of
amplicons from QMSP, we performed dissociation curve
analysis. The PCR conditions were 95°C for 10 minutes, and 40
cycles of 95°C for 15 seconds, 60°C for 30 seconds, and 72°C for
30 seconds. SYBR Green (the commonly used DNA binding
dye) fluorescence data were collected only during the extension
reaction at 72°C. Ct values were calculated by the 7900HT
system software, and average relative quantification (RQ) values
were obtained for each sample using actin, beta (ACTB) amplifi-
cation as the referent, where RQ = (target gene/ACTB)/
(Universal methylation calibrator/ACTB). Spearman rank corre-
lation coefficients (rho) and P values were calculated to assess
the correlation between GoldenGate array data and QMSP
IDH Mutation. The region spanning R132 codon of IDH1 and the
region spanning R172 codon of IDH2 were amplified by PCR with
primers designed with Primer 3 software (v.0.4.0) with the excep-
tion of the forward sequencing primer, which was selected from
Balss et al. (4). PCR reaction mixtures contained 10–25 ng DNA,
1× buffer, 0.2 mM dNTP mix, 0.2 µM forward and reverse
primers, 0.04 U of HotStarTaq, and 1 mM MgCl2 (Qiagen, Inc) in
a 25 µL volume. The PCR conditions were 95°C for 10 minutes,
40 cycles of 94°C for 30 seconds, 60°C for 30 seconds, and 72°C
for 1 minute. The resulting products were analyzed on a 1.5%
agarose gel. DNA was purified using the QIAquick PCR
Purification Kit (Qiagen, Inc) and sent to Rhode Island Genomics
and Sequencing Center at the University of Rhode Island, where
it was sequenced in both directions using the BigDyeTerminator
v3.1 Cycle Sequencing Kit (Applied Biosystems). Sequences were
analyzed with Applied Biosystems Sequence Scanner Software
v1.0. All primers for IDH1 and IDH2 mutation analysis are listed
in Supplementary Table 1 (available online).
TP53 Mutation. For TP53 mutation analysis, PCR–single-strand
conformation polymorphism technique was used, and DNA se-
quencing was done as previously described (8). Primers for PCR
amplification of fragments of exons 5–8 of TP53 are listed in
Supplementary Table 1 (available online). PCR reaction mixtures
contained 50 ng DNA, 20 µmol/L dNTP, 10 mmol/L Tris–HCl
(pH 9.0), 1.5 mmol/L MgCl2, 0.1% Triton X-100, 10 pmol of
forward and reverse primers, 1 U Taq (Perkin-Elmer Cetus,
Norwalk, CT), and 0.2 µCi [33P]dCTP (DuPont New England
Nuclear, Boston, MA) in a 30 µL volume. DNA with TP53 muta-
tion confirmed by sequencing was included as positive control. The
PCR reaction was carried out using 35 cycles 94°C for 30 seconds,
annealed for 30 seconds at 58°C for exons 5 and 8, and 60°C for
exons 6 and 7 (primers listed in Supplementary Table 1, available
online) and 72°C for 1 minute. Three microliters of PCR product
was mixed with 2 µL of 0.1 N NaOH and then mixed with 5 µL of
gel loading buffer solution (United States Biochemical Corp,
Cleveland, OH) and heated at 94°C for 4 minutes. DNA was ana-
lyzed on 6% nondenatured polyacrylamide gel, supplemented with
10% glycerol. Electrophoresis was performed at room temperature
for 20 hours and exposed to autoradiography films for 16 hours for
detection of bands. Direct sequencing of PCR fragments for both
DNA strands was done on all tumor DNAs that showed aberrant
migration patterns on single-strand conformation polymorphism
gel to determine the corresponding DNA sequences using dsDNA
cycle sequencing system (Life Technologies, Gaithersburg, MD),
as described in Wiencke et al. (17).
EGFR Amplification. EGFR amplification was measured by a
quantitative PCR method using the ABI 7900 Real-Time PCR
system (Applied Biosystems) and SYBR Green I, which has been
shown to be equivalent to TaqMan PCR assay for the assessment
of gene copy number (18). Quality control measures for the real-
time SYBR green assay included running both EGFR and control
gene, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) in
triplicate. DNA from A431 and HT29 cell lines, with known copy
number states for EGFR, served as positive and negative controls,
respectively, for amplification.
Data Assembly. Methylation data were assembled with BeadStudio
methylation software from Illumina. All GoldenGate methyla-
tion array data points are represented by fluorescent signals
146 Articles | JNCI Vol. 103, Issue 2 | January 19, 2011
(Cy dyes) from both methylated (Cy5) and unmethylated (Cy3)
alleles. The methylation level, designated as beta (b), is calculated
as b = (max[Cy5, 0])/(|Cy3| + |Cy5| + 100), in which the average
b value is derived from the approximately 30 replicate methylation
measurements because each CpG probe set is present on the array
and measured in each sample approximately 30 times. Raw average
b values were analyzed without normalization as recommended by
Illumina. At each CpG locus, for each tissue DNA sample, the
detection P value was used to determine sample performance; all
samples had detection P values less than 1 × 1025 at more than 75%
of CpG loci and passed performance criteria. There were eight
CpG loci that had a median detection P value of greater than .05,
and these eight CpGs were excluded from the analysis. All CpG
loci on the X chromosome were excluded from analysis. The final
dataset contained 1413 autosomal CpG loci associated with 773
genes. For each CpG locus, the differential methylation values
(delta–beta [Db]) were calculated by subtracting the average b
value of tumors from the mean b value of the seven nontumor
brain samples. Subsequent analyses were carried out using the R
software (19). All statistical tests were two-sided.
Unsupervised Clustering, Recursively Partitioned Mixture
Modeling (RPMM), and Survival. Hierarchical clustering of the
DNA methylation data was performed using the R function hclust
with Euclidean distance metric and Ward linkage. To discern and
describe the relationships between CpG methylation data and
patient and tumor covariates, a modified model-based form of
unsupervised clustering known as RPMM was used as described in
Houseman et al. (20) and as used in Christensen et al. (21). The
analysis of associations between methylation class (categorical) and
individual categorical covariates was performed using the Fisher
exact test. To test for association between methylation class and
continuous covariates, a permutation test was run with the
Kruskal–Wallis test statistic, and a likelihood ratio test was used
for comparing the association between methylation class and IDH
mutation to a model including age and grade-specific histology.
To test for associations between IDH mutation and grade-specific
tumor histology, and IDH mutation and tumor grade, Fisher exact
tests were used. To test for associations between IDH mutation
and primary vs secondary glioblastoma, IDH mutation and TP53
mutation, and IDH mutation and EGFR amplification, x2 tests
were used. The assumption of proportionality for Cox propor-
tional hazards modeling was verified by calculating Pearson corre-
lation coefficients for the corresponding set of Schoenfeld residuals
with a transformation of time based on the Kaplan–Meier estimate
of the survival function (22) and graphically by plotting log(survival
time) vs log(2log[survival as a function of time, t]).
Locus-by-Locus Analysis. To examine differential methylation
between tumor and nontumor tissues, gliomas were stratified by
grade-specific histological subtypes, and individual CpG loci were
compared between subtypes of glioma and nontumor samples
using a Wilcoxon rank-sum test. Because this results in the simul-
taneous comparison of all CpG loci between glioma subtypes and
nontumor sample types, false discovery rate estimation and Q
values computed by the qvalue package in R (23) were used to
adjust for multiple testing. Differentially methylated CpGs were
counted as hyper- or hypomethylated if both the tumor vs
nontumor Q less than .05 and the median methylation value |Db|
greater than 0.2. An equivalent approach was used in the analysis
of differential methylation for gliomas with mutant or wild-type
IDH compared with nontumor tissues.
Pathway Analysis. A canonical pathway analysis was conducted
with the use of Ingenuity Pathway Analysis software (Ingenuity
Systems, Redwood City, CA). CpG gene-loci associated with the
Illumina GoldenGate methylation array were used as reference,
and loci from differential methylation analysis, as described later in
the article, were investigated for pathways enrichment. The statis-
tical significance of gene-locus enrichment within canonical path-
ways was measured with a Fisher exact test.
Unsupervised Clustering and Modeling of Glioma and
Nontumor DNA Methylation Data
Histological grade and patient demographic data for the 131 gli-
omas and patient demographic data for the seven nontumor brain
tissues are presented in Table 1. To characterize DNA methylation
of gliomas and nontumor brain tissues, the bisulfite-modified DNA
samples were hybridized to the GoldenGate DNA methylation
array. Unsupervised clustering of DNA methylation data from
1413 autosomal CpG loci showed that nontumor brain tissues cluster
with each other and are distinct from tumor tissues (Figure 1, A).
Furthermore, we observed that oligodendrogliomas and astrocy-
tomas generally clustered together and demonstrated a greater
number of methylated loci relative to ependymomas, pilocytic as-
trocytomas, as well as nontumor brain tissues. Concomitantly,
glioblastomas (also known as grade 4 astrocytoma), predominantly
clustered together at the bottom of the heatmap (Figure 1, A) and
displayed more hypermethylated CpG loci than ependymomas.
To further investigate the DNA methylation patterns of gli-
omas and nontumor brain tissue, we implemented an agnostic ap-
proach by applying a modified model-based form of unsupervised
clustering known as RPMM (20). RPMM allows for precise infer-
ence regarding the potential covariates associated with intrinsic
similarities and differences in CpG methylation by generating
distinct classes of DNA methylation for the modeled samples
based on the DNA methylation array data. We applied RPMM
clustering to all 131 tumors, which generated 11 methylation
classes (Figure 1, B). Methylation classes contain samples with
DNA methylation patterns that are most similar to each other,
and samples with different DNA methylation patterns are distin-
guished by their membership in a different methylation class.
Methylation class was statistically significantly associated with
both tumor histological subtype (P < 2.2 × 10216) and grade (P <
2.2 × 10216) (Supplementary Table 2, available online).
Methylation Array and Methylation Class Validation
Methylation data from GoldenGate arrays have been extensively
validated by our group and others using a variety of methods (24–28).
The methylation array data presented in this study were validated by
correlating CpG methylation array data to QMSP data for genes
commonly methylated in gliomas—MGMT, RASSF1, PYCARD,
JNCI | Articles 147
Table 1. Patient demographic and tumor characteristics*
(n = 7)
Tumor histology and grade of glioma tissues (n = 131)
(n = 20)
(n = 12)
(n = 9)
(n = 20)
(n = 9)
(n = 22)
(n = 20)
(n = 15)
(n = 4)
Age at diagnosis, y
Sex, No. (%)
Race, No. (%)
* Nontumor brain tissues (n = 7) were obtained from cancer-free patients who underwent temporal lobe resection for treatment of epilepsy at the University of California San Francisco Medical Center. Glioma tissues
(n = 131) were obtained between 1990 and 2003 from the University of California San Francisco Brain Tumor Research Center Tissue Bank.
HOXA9, PEG3, and SLIT2 (Supplementary Table 3, available
online). To determine the validity of association between histology
and methylation class, we utilized publicly available GoldenGate
methylation array data for 71 glioblastoma samples from TCGA.
Using the RPMM classification (Figure 1, B), we predicted the meth-
ylation class for each glioblastoma sample of TCGA and confirmed
that 70 (99%) of the 71 TCGA glioblastoma samples were classified
in RPMM methylation classes that contained glioblastoma samples
(Supplementary Table 2, available online). The identification
numbers and the predicted RPMM methylation classes of TCGA
tumors are listed in Supplementary Table 4 (available online).
Ratios of Hypermethylated to Hypomethylated CpG Loci
and Tumor Histology
We examined the differential methylation (Db) between tumor
and nontumor brain tissues and observed a striking pattern of the
number of hyper- and hypomethylated CpG loci among different
tumor subtypes (Figure 2, A). Glioblastomas showed a low ratio of
hyper- to hypomethylated loci (ratio = 1.3) compared with the
ratio for grades 2 and 3 astrocytomas, grades 2 and 3 oligoastrocy-
tomas, and grade 2 oligodendrogliomas (ratios = 3.7, 7.6, and 9.7,
respectively). Conversely, ependymomas showed increased hypo-
methylation (ratio = 0.3). The ratios of hyper- to hypomethylated
CpG loci were statistically significantly different across glioma
tumor histological subtypes (permutation P < .0001). Histology-
related hyper- and hypomethylation patterns were also evident in
unsupervised hierarchical clustering of Db methylation values for
all 1413 autosomal CpG loci (Figure 2, B).
We next assessed the cellular pathways associated with statisti-
cally significantly differentially hypomethylated and (separately)
hypermethylated CpG loci that were common among glioblas-
tomas, astrocytomas, oligoastrocytomas, and oligodendrogliomas.
There were 18 CpG loci with statistically significant differential
hypomethylation (Q < .05) and common among glioblastomas,
astrocytomas, oligoastrocytomas, and oligodendrogliomas. An
analysis of cellular pathways enriched among these 18 CpG loci,
compared with all genes represented on the methylation array,
revealed statistically significant enrichment of metabolism and
biosynthesis pathways (Supplementary Table 5, available online).
In addition, there were 35 statistically significantly differentially
hypermethylated (Q < .05) CpG loci common among glioblas-
tomas, astrocytomas, oligoastrocytomas, and oligodendrogliomas.
An analysis of cellular pathways enriched among these 35 CpG loci
showed that oxidative stress response and retinoic acid–mediated
apoptosis signaling pathways were statistically significantly
enriched (Supplementary Table 5, available online). For each
grade-specific tumor histology, all statistically significant differen-
tially hypomethylated and hypermethylated CpG loci are detailed
in Supplementary Tables 6 and 7, respectively (available online).
Glioma Methylation Classes, IDH Mutation, and Survival
The analysis of differentially methylated CpG loci in cellular path-
ways suggested that metabolic pathways as a group were commonly
hypomethylated in gliomas. We hypothesized that genetic muta-
tions in the metabolic pathways were associated with the observed
DNA methylation phenotype. To test this hypothesis, we sequenced
a subset of 95 tumors with available DNA for IDH1 and IDH2 mu-
148 Articles | JNCI Vol. 103, Issue 2 | January 19, 2011
Figure 1. Association between glioma histological subtypes and
DNA methylation pattern. A) The average methylation beta (b)
values of both gliomas (n = 131) and nontumor tissue samples (n =
7) were subjected to unsupervised hierarchical clustering based on
Euclidean distance metric and Ward linkage and are shown in the
heatmap. Each row represents a sample and each column represents
a CpG locus (all 1413 autosomal loci). The scale bar at the bottom
shows the range of b values (0–1). Tissue histology and grade are
defined in color keys next to the heatmap, on the left. GBM2 = sec-
ondary glioblastoma multiforme; GBM = pri mary glioblastoma mul-
tiforme; AS3 = grade 3 astrocytoma; AS2 = grade 2 astrocy toma;
OA3 = grade 3 oli goastrocytoma; OA2 = grade 2 oligoastrocytoma;
OD2 = grade 2 oligodendroglioma; EP = ependymoma; PA = pilocytic
astrocytoma. B) Recursively partitioned mixture model (RPMM) of
glioma and nontumor brain tissue samples (n = 138). Methylation
profile classes are stacked in rows separated by red lines and class
height corresponds to the number of samples in each class. Class
methylation at each CpG locus (columns) is the mean methylation
for all samples in a class. To the left of the RPMM is the clustering
dendrogram. In the heatmap and RPMM, blue designates methylated
CpG loci (average b = 1), and yellow designates unmethylated CpG
loci (average b = 0).
JNCI | Articles 149
tations. IDH2 mutation was detected in only two tumors, and IDH1
mutation was detected in 56 tumors (total IDH mutation prevalence =
60.0%). IDH mutations were more common in oligoastrocytoma,
oligodendroglioma, or astrocytoma histological subtypes than in
glioblastomas, pilocytic astrocytomas, or ependymomas (P = 6.4 ×
1029); in lower-grade than higher-grade tumors (P = .01); in tumors
with TP53 mutation compared with wild-type TP53 (P = .06);
and in younger patients (mean age = 36.6 vs 47.4 years, P = .0009)
(Table 2). However, IDH mutation was not associated with EGFR
amplification (P = .10) (Table 2). Additionally, tumors with IDH
mutation showed statistically significantly higher MGMT methyla-
tion (P = 3.6 × 1024) (Supplementary Figure 1, available online).
Next, we investigated the number of statistically significantly
differentially methylated CpG loci between tumor and nontumor
samples stratified by IDH mutation status. Tumors with IDH mu-
tation revealed a striking contrast between the number of statisti-
cally significantly differentially hypermethylated loci, as well as
the ratio of hyper- to hypomethylated loci in IDH mutant tumors
vs IDH wild-type tumors (mutant = 7.8 vs wild-type = 0.22)
(Figure 3, A). We used the statistically significantly differentially
hypermethylated and hypomethylated CpG loci in IDH mutant
tumors to conduct an enrichment analysis of cellular pathways. We
found that cellular signaling pathways were hypermethylated,
whereas metabolism and biosynthesis pathways that included
starch and sucrose metabolism and pentose and glucuronate inter-
conversion pathways were hypomethylated in IDH mutant tumors
(Supplementary Table 8, available online).
Methylation profiling with RPMM of the 95 gliomas with both
methylation data and IDH mutation status resulted in nine meth-
ylation classes (Figure 3, B). Methylation classes were statistically
significantly associated with patient age (permutation P = 3.0 ×
1024), histology (P < 2.2 × 10216), and grade (P = 6.0 × 1029)
(Supplementary Table 9, available online). IDH mutation was
also strongly associated with methylation class (P = 3.0 × 10216)
(Figure 3, C), and this association remained statistically significant
when controlling for age and histology (likelihood ratio P < .0001).
Only two methylation classes had IDH mutant tumors (class L and
class RLLR), and greater than 98% of the tumors (all but one) in
these two classes had an IDH mutation (Figure 3, C). Furthermore,
methylation classes L and RLLR were both more highly methyl-
ated than the other methylation classes (Figure 3, B).
Last, we examined the potential association between IDH mu-
tation and patient survival among cases with available mutation
data (n = 95) because previous studies reported increased survival
among glioma patients with IDH mutation (3,5). In a multivariable
Cox proportional hazards model controlling for age at diagnosis,
sex, and grade-specific histology, we observed that patients whose
tumors harbored IDH mutation showed statistically significantly
better survival compared with patients (n = 38) whose tumors har-
bored wild-type IDH (hazard ratio of death = 0.27, 95% confi-
dence interval = 0.10 to 0.72) (Figure 3, D, and Table 3).
In this study, we demonstrate a distinct pattern of methylation
across histological subtypes of glioma that is associated with
genetic mutation in IDH gene loci. The two methylation classes
associated with mutant IDH tumors had a homogeneous hyper-
Figure 2. Differential methylation and the ratio of hyper- to hypo-
methylated loci in gliomas. Differential methylation values (Db) were
calculated by subtracting tumor average b value from the mean
b value of the nontumor brain samples (n = 7) for each CpG locus.
A) The number of statistically significantly differentially hyper- and
hypomethylated loci (Q < .05 and |Db| > 0.2) are plotted by
grade-specific glioma histology. GBM2 = secondary glioblastoma
multiforme; GBM = primary glioblastoma multi forme; AS3 = grade 3
astrocytoma; AS2 = grade 2 astrocytoma; OA3 = grade 3 oligoastro-
cytoma; OA2 = grade 2 oligoastrocytoma; OD2 = grade 2 oligodendro-
glioma; EP = ependymoma; PA = pilocytic astrocytoma. B) Db values
for all tumors (n = 131) were subjected to unsupervised hierarchical
clustering based on Euclidean distance metric and Ward linkage.
Each row represents a sample and each column represents a CpG
locus (all 1413 autosomal loci). The scale bar at the top shows the
range of Db values (21 to 1). Tissue histology and grade are defined
in color keys next to the heatmap on the left. In the heatmap, blue
designates differentially hypermethylated CpG loci in tumors (Db = 1),
and yellow designates differentially hypomethylated CpG loci in
tumors (Db = 21).
150 Articles | JNCI Vol. 103, Issue 2 | January 19, 2011
methylation-rich character compared with the methylation classes
for tumors with wild-type IDH. Additionally, the tumors with
wild-type IDH belonged to several distinct methylation classes.
The contrast between a homogenous hypermethylated profile and
several heterogeneous hypomethylated profiles (associated with
distinct histological types) strongly suggests that IDH mutation
“drives” the observed hypermethylated phenotype, irrespective of
tumor histology. In support of this, we note that IDH mutation is
more robustly associated with methylation class compared with the
classical glioma tumor genetic markers like TP53 mutation and
IDH mutations are heterozygous and allow the enzyme nor-
mally responsible for conversion of isocitrate to a-ketoglutarate to
convert a-ketoglutarate to 2-hydroxyglutarate in an NADPH-
dependent manner and results in accumulation of 2-hydroxyglutarate
(7,8). Despite the observed hypermethylated profile of IDH
mutant tumors, analysis of cellular pathways showed hypomethyl-
ation of several metabolic pathways, potentially to compensate for
mutation-related metabolic stress. Because the methylation profile
of IDH mutant tumors is generally homogenous, it is possible that
the hypermethylation phenotype is either selected for, or driven
by, the hypomethylation of compensatory metabolic pathways,
thus directly linking and temporally situating these events. The
level of a-ketoglutarate has been shown to be slightly lower in
IDH1 mutant gliomas, though this decrease was not statistically
significant (8). However, IDH1 localizes to the cytosol and
peroxisomes, whereas IDH2 localizes to mitochondria; and
because most IDH mutations in gliomas are in IDH1, pan-cellular
a-ketoglutarate levels may not represent available cytosolic
a-ketoglutarate levels. Furthermore, IDH1 R132 mutation has
been shown to favor an active conformation of the enzyme,
increase its affinity for NADPH, and favor reduction of a-ketoglu-
tarate to 2-hydroxyglutarate over the conversion of isocitrate to
a-ketoglutarate, which may reduce the availability of cytosolic
a-ketoglutarate and NADPH (8). Hence, a potential mechanism
responsible for the strong association between epigenetic profile
and IDH mutation is related to potentially altered availability of
a-ketoglutarate in these tumors. The Jumonji domain–containing
histone demethylases require a-ketoglutarate as a substrate for
their enzymatic activity (29), and altered activity of these histone
demethylases could lead to aberrantly remodeled chromatin, po-
tentially resulting in epigenetic alterations at the DNA level as
well. However, studies that are beyond the scope of this article
would be necessary to disentangle the complex networks of chro-
Table 2. Patient age, grade-specific glioma histology, grade, TP53 mutation, and EGFR amplification stratified by IDH mutation status*
Patient age and tumor characteristic
Age at diagnosis, y
Median age (range)
Mean age (SD)
Tumor histology,§ No. (%)
Grade 2 astrocytoma
Grade 3 astrocytoma
Secondary glioblastoma (P = .005)¶
Grade 2 oligoastrocytoma
Grade 3 oligoastrocytoma
Grade 2 oligodendroglioma
Tumor grade, No. (%)
TP53 mutation, No. (%)
EGFR amplification, No. (%)
P = 9.0 × 1024‡
P = 6.4 × 1029║
P = .01#
P = .06**
P = .10††
* Analysis of patient age and tumor characteristics vs isocitrate dehydrogenase (IDH) gene mutation status. TP53 = tumor protein 53; EGFR = epidermal growth
† IDH gene mutation was assessed by sequencing tumor DNA.
‡ Association between age and IDH mutation was assessed using two-sided Student t test.
§ Tumors were previously reviewed by neuropathologists at the University of California San Francisco to assign histological subtypes and grades according to the
World Health Organization classification.
║ Association between grade-specific histology and IDH mutation was assessed using two-sided Fisher exact test.
¶ Association between primary vs secondary glioblastoma and IDH mutation was assessed using two-sided x2 test.
# Association between tumor grade and IDH mutation was assessed using two-sided Fisher exact test.
** Association between TP53 mutation and IDH mutation was assessed using two-sided x2 test.
†† Association between EGFR amplification and IDH mutation was assessed using two-sided x2 test.
JNCI | Articles 151
matin remodeling enzymes, their targets, and their responses to
altered levels of enzymatic substrate. Alternatively (or perhaps in
conjunction), lower concentrations of NADPH associated with
mutant IDH1 (30) may result in a decreased capacity for reductive
processes in defense against reactive oxygen species. Furthermore,
a-ketoglutarate itself is a potent antioxidant (6) and its decreased
availability in IDH mutant cells alone, or together with lower
NADPH levels, could drive the selection of cells with compensa-
tory metabolic gene expression profiles mediated by altered epige-
netic patterns including chromatin configuration and DNA
methylation. Consistent with the suggestion that gene expression
profiles are altered in association with DNA methylation related to
IDH mutation, an analysis of glioblastoma gene expression sub-
types showed that IDH mutation occurred almost exclusively in
proneural glioblastomas (31).
More broadly, and similar to the hypermethylation phenotype
we describe here, hypermethylator phenotypes have previously
been associated with other cancers. This phenotype was first
described in colon cancer and is commonly referred to as CpG
Island Methylator Phenotype (CIMP) (32). Specifically, colorectal
cancers can be divided in CIMP-high, CIMP-low, and non-CIMP
based on the methylation of five to eight specific gene promoters
Figure 3. Association between IDH mutation and methylation pheno-
type in gliomas. A) The number of statistically significantly differentially
hyper- and hypomethylated loci (Q < .05 and |Db| > 0.2), are plotted by
tumor IDH mutation status. B) Recursively partitioned mixture model
(RPMM) of glioma samples with both methylation and mutation data
(n = 95). Methylation profile classes are stacked in rows separated by
red lines, class height corresponds to the number of samples in each
class. Class methylation at each CpG locus (columns) is the mean meth-
ylation for all samples in a class where blue designates methylated CpG
loci (average b = 1), and yellow designates unmethylated CpG loci (av-
erage b = 0). To the right of the RPMM is the clustering dendrogram.
C) Methylation-class-specific IDH mutation status (Fisher P = 3.0 × 10216).
D) Kaplan–Meier survival probability strata for IDH mutant (red, n = 57)
and IDH wild-type (black, n = 38) tumors, tick marks are censored
observations and banding patterns represent 95% confidence
152 Articles | JNCI Vol. 103, Issue 2 | January 19, 2011
(33,34). Similar to IDH in glioma, CIMP status in colon tumors
has been associated with specific mutations; CIMP-high with
BRAF and CIMP-low and non-CIMP with KRAS (35). Recently,
Noushmehr et al. (36) described a CIMP in glioblastomas, termed
G-CIMP, which they found to be tightly associated with IDH1
mutation. In a number of lower-grade gliomas, Noushmehr et al.
performed methylation profiling of eight markers of G-CIMP and
confirmed that IDH1 mutation is associated with G-CIMP in low-
grade tumors, which is consistent with our array-based findings.
Furthermore, more than 83% of G-CIMP-positive glioblastomas
with IDH1 mutation were of the proneural glioblastoma gene ex-
pression subtype (36), additional evidence supporting an associa-
tion between distinct, IDH-related methylation in our data (from
diverse glioma histological subtypes), and a specific gene expression
phenotype. In addition, MGMT methylation is often investigated
in glioma because it has been associated with increased sensitivity
to alklyating agents such as temozolomide and can affect response
to therapy (37). In fact, increased MGMT methylation can also
distinguish CIMP-high and CIMP-low from non-CIMP in colon
cancer (38). Our results, consistent with previous work (9), demon-
strate an association between increased MGMT methylation and
IDH mutation. Finally, some studies have reported CIMP-positive
colon cancers to have a relatively better prognosis (39), and from
both the work of Noushmehr et al. and ours, this appears to be
consistent with the pattern of survival observed in CIMP gliomas.
The association between IDH mutation and a homogenous
methylation profile across several histological subtypes suggests
that genetic and epigenetic alterations are not independent. This
observation also has profound implications for the development of
new therapies for glioma. Although pharmacological inhibition of
2-hydroxyglutarate has been suggested as a possible approach to
treating IDH mutant gliomas (40) such drugs do not yet exist.
However, DNA methylation is a modifiable therapeutic target;
DNA methyltransferase inhibitors and histone deacetylase inhibi-
tors are in clinical trials and showing some promise for the treat-
ment of hematopoietic malignancies (41–43). Our work suggests
that a simple diagnostic test for DNA methylation (or mutation)
can identify a class of tumors for which the modification of DNA
methylation may have therapeutic efficacy. This class of tumors is
not discernable by any of the classic histopathologic or tumor
markers for glioma. The recognition that IDH mutation has value
as a clinical prognostic marker and is associated with a broad DNA
methylation phenotype suggests that glioma therapeutic protocols
that reverse DNA methylation should be pursued.
Our study has a few limitations. Although we studied 131 his-
tologically diverse tumors, we did not have IDH mutation, TP53
mutation, and EGFR amplification data on all subjects and had
somewhat limited statistical power to explore the relationships
between IDH mutation and these alterations. Future investiga-
tions that include larger numbers of histologically diverse samples
and higher-resolution methylation array techniques, along with
measurements of other somatic alterations (IDH mutation, mRNA
expression, and copy number), will afford a more comprehensive
understanding of the molecular and chromosomal characteristics
that distinguish glioma subtypes. Understanding whether these
glioma molecular and chromosomal subtypes are differentially
associated with glioma risk loci (44) also will help to understand
the etiology and possibly outcomes of this often catastrophic
In summary, our work demonstrates a clear relationship
between genetic and epigenetic events in human gliomas by asso-
ciating IDH mutations with a homogenous methylation profile,
and demonstrates that profiles of methylation differ by histological
subtype of disease. Additionally, and consistent with previous work,
we also showed that patients with IDH mutation have significantly
improved survival. Advances in therapy for glioma may be realized
by targeting DNA methylation. Much attention has recently been
given to the utility of MGMT methylation in predicting response
to therapy, and our data further suggest that other DNA methyla-
tion markers may improve clinical assessment, guide therapies, and
potentially uncover novel therapeutic avenues altogether.
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National Institute of Health (R01CA52689 to M.R.W.; P50CA097257
to M.R.W. and J.K.W; R01CA078609, R01CA121147, R01CA126939,
and R01CA100679 to K.T.K.; R01ES06717 and R01CA126831 to J.K.W.;
P30CA077598 to H.H.N.); Tobacco-Related Diseases Research Program
(18CA-0127 to J.L.W.).
B. C. Christensen and A. A. Smith contributed equally to the work.
M. R. Wrensch, K. T. Kelsey, and J. K. Wiencke are joint lead investigators.
The funders did not have any role in the study design, collection of data,
interpretation of the results, preparation of the article, or the decision to submit
the article for publication.
Affiliations of authors: Department of Pathology and Laboratory Medicine
(BCC, AAS, CJM, KTK) and Department of Community Health (BCC, DCK,
EAH, KTK), Brown University, Providence, RI; Department of Neurological
Surgery, Helen Diller Family Cancer Center (SZ, MRW, JKW) and Department
of Epidemiology and Biostatistics (JLW), University of California San Francisco,
San Francisco, CA; Department of Biostatistics, Harvard School of Public
Health, Boston, MA (EAH); Masonic Cancer Center, Division of Epidemiology
and Community Health, University of Minnesota, Minneapolis, MN (HHN);
Section of Biostatistics and Epidemiology, Department of Community and
Family Medicine, Dartmouth Medical School, Lebanon, NH (MRK).