Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer.
ABSTRACT Polycomb group proteins (PCGs) are involved in repression of genes that are required for stem cell differentiation. Recently, it was shown that promoters of PCG target genes (PCGTs) are 12-fold more likely to be methylated in cancer than non-PCGTs. Age is the most important demographic risk factor for cancer, and we hypothesized that its carcinogenic potential may be referred by irreversibly stabilizing stem cell features. To test this, we analyzed the methylation status of over 27,000 CpGs mapping to promoters of approximately 14,000 genes in whole blood samples from 261 postmenopausal women. We demonstrate that stem cell PCGTs are far more likely to become methylated with age than non-targets (odds ratio = 5.3 [3.8-7.4], P < 10(-10)), independently of sex, tissue type, disease state, and methylation platform. We identified a specific subset of 69 PCGT CpGs that undergo hypermethylation with age and validated this methylation signature in seven independent data sets encompassing over 900 samples, including normal and cancer solid tissues and a population of bone marrow mesenchymal stem/stromal cells (P < 10(-5)). We find that the age-PCGT methylation signature is present in preneoplastic conditions and may drive gene expression changes associated with carcinogenesis. These findings shed substantial novel insights into the epigenetic effects of aging and support the view that age may predispose to malignant transformation by irreversibly stabilizing stem cell features.
- SourceAvailable from: Corey T Watson[show abstract] [hide abstract]
ABSTRACT: Recent associations between age-related differentially methylated sites and bivalently marked chromatin domains have implicated a role for these genomic regions in aging and age-related diseases. However, the overlap between such epigenetic modifications has so far only been identified with respect to age-associated hyper-methylated sites in blood. In this study, we observed that age-associated differentially methylated sites characterized in the human brain were also highly enriched in bivalent domains. Analysis of hyper-vs. hypo-methylated sites partitioned by age (fetal, child, and adult) revealed that enrichment was significant for hyper-methylated sites identified in children and adults (child, fold difference = 2.28, P = 0.0016; adult, fold difference = 4.73, P = 4.00610 25); this trend was markedly more pronounced in adults when only the top 100 most significantly hypo-and hyper-methylated sites were considered (adult, fold difference = 10.7, P = 2.00610 25). Interestingly, we found that bivalently marked genes overlapped by age-associated hyper-methylation in the adult brain had strong involvement in biological functions related to developmental processes, including neuronal differentiation. Our findings provide evidence that the accumulation of methylation in bivalent gene regions with age is likely to be a common process that occurs across tissue types. Furthermore, particularly with respect to the aging brain, this accumulation might be targeted to loci with important roles in cell differentiation and development, and the closing off of these developmental pathways. Further study of these genes is warranted to assess their potential impact upon the development of age-related neurological disorders. Competing Interests: The authors have the following interests. Dr. Giovannoni serves on scientific advisory boards for Merck Serono and Biogen Idec and Vertex Pharmaceuticals; has served on the editorial board of Multiple Sclerosis; has received speaker honoraria from Bayer the speakers bureau for Merck Serono; and has received research support from Bayer Schering Pharma, Biogen Idec, Merck Serono, Novartis, UCB, Merz Pharmaceuticals, LLC, Teva Pharmaceutical Industries Ltd.–sanofiaventis, GW Pharma, and Ironwood. There are no patents, products in development, or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.PLoS ONE 09/2012; · 3.73 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: BACKGROUND: Inter-individual epigenetic variation, due to genetic, environmental or random influences, is observed in many eukaryotic species. In mammals however, the molecular nature of epiallelic variation has been poorly defined, partly due to the restricted focus on DNA methylation. Here we report the first genome-scale investigation of mammalian epialleles that integrates genomic, methylomic, transcriptomic and histone state information. RESULTS: First, in a small sample set, we demonstrate that non-genetically determined inter-individual differentially methylated regions (iiDMRs) can be temporally stable over at least two years. Then, we show that iiDMRS are associated with changes in chromatin state as measured by inter-individual differences in histone variant H2A.Z levels. However, the correlation of promoter iiDMRs with gene expression is negligible and not improved by integrating H2A.Z information. We find that most promoter epialleles, whether genetically or non-genetically determined, are associated with low levels of transcriptional activity, depleted for housekeeping genes, and either depleted for H3K4me3/enriched for H3K27me3 or lacking both these marks in human embryonic stem cells. The preferential enrichment of iiDMRs at regions of relative transcriptional inactivity validates in a larger independent cohort, and is reminiscent of observations previously made for promoters that undergo hypermethylation in various cancers, in vitro cell culture and aging. CONCLUSIONS: Our work identifies potential key features of epiallelic variation in humans, including temporal stability of non-genetically determined epialleles, and concomitant perturbations of chromatin state. Furthermore, our work suggests a novel mechanistic link among inter-individual epialleles observed in the context of normal variation, cancer and aging.Genome biology 05/2013; 14(5):R43. · 10.30 Impact Factor
Dataset: Nowsheen et. al. 2012
Age-dependent DNA methylation of genes that
are suppressed in stem cells is a hallmark of cancer
Andrew E. Teschendorff,1Usha Menon,2Aleksandra Gentry-Maharaj,2
Susan J. Ramus,2Daniel J. Weisenberger,3Hui Shen,3Mihaela Campan,3
Houtan Noushmehr,3Christopher G. Bell,1A. Peter Maxwell,4David A. Savage,4
Elisabeth Mueller-Holzner,5Christian Marth,5Gabrijela Kocjan,6Simon A. Gayther,2
Allison Jones,2Stephan Beck,1Wolfgang Wagner,7Peter W. Laird,3Ian J. Jacobs,2
and Martin Widschwendter2,8
1Medical Genomics Group, UCL Cancer Institute, University College London, London WC1E 6BT, United Kingdom;2Department of
Gynecological Oncology, UCL Elizabeth Garrett Anderson Institute for Women’s Health, University College London, London W1T 7DN,
United Kingdom;3USC Epigenome Center, University of Southern California, Keck School of Medicine, Los Angeles, California
90089-9601, USA;4Nephrology Research Group, Centre for Public Health, Queen’s University Belfast, Belfast BT9 7AB, Northern
Ireland;5Department of Obstetrics and Gynaecology, Innsbruck Medical University, Innsbruck 6020, Austria;6Department of
Histopathology, University College London, London WC1E 6JJ, United Kingdom;7Helmholtz Institute for Biomedical Engineering–Cell
Biology, Aachen University Medical School, 52074 Aachen, Germany
Polycomb group proteins (PCGs) are involved in repression of genes that are required for stem cell differentiation.
Recently, it was shown that promoters of PCG target genes (PCGTs) are 12-fold more likely to be methylated in cancer
than non-PCGTs. Age is the most important demographic risk factor for cancer, and we hypothesized that its carcinogenic
potential may be referred by irreversibly stabilizing stem cell features. To test this, we analyzed the methylation status of
over 27,000 CpGs mapping to promoters of ;14,000 genes in whole blood samples from 261 postmenopausal women. We
demonstrate that stem cell PCGTs are far more likely to become methylated with age than non-targets (odds ratio = 5.3
[3.8–7.4], P < 10?10), independently of sex, tissue type, disease state, and methylation platform. We identified a specific
subset of 69 PCGT CpGs that undergo hypermethylation with age and validated this methylation signature in seven
independent data sets encompassing over 900 samples, including normal and cancer solid tissues and a population of bone
marrow mesenchymal stem/stromal cells (P < 10?5). We find that the age-PCGT methylation signature is present in
preneoplastic conditions and may drive gene expression changes associated with carcinogenesis. These findings shed
substantial novel insights into the epigenetic effects of aging and support the view that age may predispose to malignant
transformation by irreversibly stabilizing stem cell features.
[Supplemental material is available online at http:/ /www.genome.org. The microarray data from this study have been
submitted to the NCBI Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) under accession nos. GSE19711,
GSE20067, and GSE20080.]
Targets of polycomb group proteins (PCGTs) are repressed in
human embryonic and adult stem cells (Lee et al. 2006). The re-
pression mechanism involves chromatin modifications and is re-
versible, allowing stem cells and multipotent progenitors to dif-
ferentiate into committed cell lineages through expression of
specific PCGTs. Recently, we and others have demonstrated that
stem cell PCGTs in human embryonic stem cells (hESC) are far
more likely to undergo cancer-specific promoter DNA hyper-
methylation than non-targets, suggesting a stem-cell origin model
of cancer. In this model, PCGTs in stem cells would gradually un-
dergo de novo methylation, irreversibly locking cells in an un-
differentiated state of self-renewal and thereby predisposing them
to subsequent malignant transformation (Ohm et al. 2007;
Schlesinger et al. 2007; Widschwendter et al. 2007). However, the
mechanisms and factors contributing to this de novo methyla-
tion are not yet known.
Age is by far the strongest demographic risk factor for cancer.
Besides time-dependent DNA damage (Hoeijmakers 2009), there
is now also substantial evidence that aging affects DNA methyl-
ation (DNAm) of specific loci, including cancer-related genes
(Issa et al. 1994, 1996; Ahuja et al. 1998; Nakagawa et al. 2001; So
et al. 2006; Fraga and Esteller 2007; Fraga et al. 2007; Bjornsson
et al. 2008; Christensen et al. 2009). Based on these observations,
we hypothesized that age may induce DNAm of PCGTs, and
thereby predispose to cancer. Although blood and epithelial cells
originate from different germ layers, we speculated that genes
that are mandatory for the differentiation of epithelial cells are
more likely to become methylated with increasing age in non-
epithelial tissue such as blood. Hence, in order to identify age-
dependent CpGs that may be important in the biology of epi-
thelial cancers, we first retrieved an age-dependent signature
from peripheral blood cells, then validated the age signature in
independent blood samples and normal epithelial tissues, and
Article published online before print. Article and publication date are at
20:440–446 ? 2010 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/10; www.genome.org
finally tested the biological relevance of this signature in epi-
Age-dependent hypermethylation of PCGTs is independent
of cell type
We first performed DNAm profiling (Illumina Infinium 27k)
(Weisenberger et al. 2008) of peripheral blood samples drawn from
261 postmenopausal women spanning a 30-yr age range (Supple-
mental Fig. 1; Supplemental Tables 1, 2). A stringent quality con-
trol and interarray normalization procedure resulted in a normal-
ized data matrix of methylation scores (b-values, 0 < b < 1) across
261 blood samples (148 from healthy women [Set-1], 113 from
ovarian cancer cases [Set-2]) and 25,642 CpG sites (Table 1; see
Methods, Supplemental material). Unsupervised analysis using
singular value decomposition (SVD) revealed significant compo-
nents of variation associated with age (Supplemental Fig. 2). Next,
see if this signature would be dependent on disease status, this
analysis was performed separately for cases and controls. We ob-
served that the age-associated DNAm signature was very similar
regardless of disease status (Fig. 1A). We thus combined the sam-
ples (n = 261) to derive a core DNA methylation signature for aging
(589 CpGs passed a false discovery rate [FDR] threshold of 0.05;
Fig. 1A, Supplemental Table 3). While the majority of CpGs were
hypomethylated with age, we observed that CpGs mapping to
promoters of PCGTs (defined by single occupancy of SUZ12, EED,
or H3K27me3 in human embryonic stem cells [hESC] [Lee et al.
2006]) were preferentially hypermethylated (Fig. 1A). Specifically,
we identified 69 CpGs mapping to 64 unique PCGT loci (Supple-
mental Table 4), which was significantly more than the 20 unique
estimated that PCGT loci were approximately fivefold (odds ratio
[OR]) more likely (median unbiased mid-p test, P < 10?12, Fig. 1B)
to be hypermethylated with age than non-PCGTs, defined by
genes that lack occupancy of SUZ12, EED, or H3K27me3 marks in
hESC (Lee et al. 2006). Similarly, we observed a fivefold OR en-
richment of H3K27me3 marks (Fig. 1C) in hematopoietic stem
cells (HSC) (Cui et al. 2009). In contrast, only 11 PCGTs were
hypomethylated with age, which was somewhat less than ex-
pected by chance (Fig. 1A,B). We verified that PCGT enrichment
among hypermethylated CpGs was not due to an overrep-
resentation of PCGT CpGs within CpG islands, by showing that
the enrichment remained when restricting the comparison to
those CpGs located within CpG islands (OR = 4.2 [3.0–5.7], P <
10?10). The 69 PCGT CpGs displayed an average methylation
profile that increased monotonically over an age range spanning
>25 yr (50–80 yr) (Supplemental Fig. 3).
To investigate the generality of this epigenetic phenomenon,
we next applied the same linear regression approach to derive
DNAm signatures for aging in two independent data sets (Table 1,
Set-5 and Set-6): whole blood (WB) samples from 188 patients (95
women and 93 men) with type 1 diabetes (T1D), and tumor tissue
samples from 177 women with ovarian cancer (OvC). Using the
same FDR cutoff of 0.05, we observed many age-associated CpGs
in WB and OvC tissue (Supplemental Tables 5, 6), with a highly
significant OR enrichment of PCGTs among CpGs undergoing
hypermethylation with age, but not so among CpGs undergoing
hypomethylation (Fig. 1D,E). For the WB T1D samples, we verified
In addition, using data generated on a different platform,
with a different set of CpGs (Goldengate assay; Christensen et al.
2009), we confirmed that PCGTs undergo preferential hyper-
methylation with age in normal tissues other than blood, in-
cluding normal pleura and lung samples (Supplemental Fig. 5).
Given the common enrichment of PCGTs across multiple
tissue types, we next asked if this result could be due to a specific
‘‘core’’ subset of PCGTs, or if instead the age-PCGT signature is
methylation with age in the validation data sets (Table 1). We
found that the average methylation profile of the 69 CpGs corre-
lated significantly with age in blood samples from 108 healthy
Fig. 3, Set-4), 188 patients with T1D (Fig. 2B, Set-5), and in ovarian
cancer tissue from 177 women (Fig. 2C, Set-6). Moreover, we ob-
served that the 69 PCGT CpGs exhibited a significant skew toward
hypermethylation with age in all validation sets examined (Fig.
2E–G; Supplemental Fig. 3; Supplemental Table 4). The skew to-
ward hypermethylation remained significant relative to random
choices of 69 CpGs. Furthermore, we observed that the 69 PCGT
Main methylation data sets used in this study
size Cell type
Song et al. 2009
Song et al. 2009
Song et al. 2009
Song et al. 2009
Widschwendter et al. 2004
Bork et al. 2010
Ovarian cancer before treatment
Ovarian cancer after treatment
Type-1 diabetics (93 male + 95 female)
Women with ovarian cancer
Bone marrow from healthy donors
Set-8110Normal tissue from healthy
donors + cancer patients
Normal adjacent + lung cancer
tissue from 23 patients
GG 1.5k v2ValidationChristensen et al. 2009
Set-946Lung GG 1.5k v1 ValidationBibikova et al. 2006
Set-1048Cervix26–43 Infin. 27kValidation—
aIllumina Infinium 27k or GoldenGate-GG 1.5k v1.
(WB) Whole blood, (NA) not available.
Age-dependent DNA methylation and cancer
CpGs exhibited higher levels of methylation than non-age-asso-
ciated PCGT CpGsand that the differencein methylation between
(Supplemental Fig. 6).
reflected in multipotent progenitor and stem cell pools. To this
end, we investigated the DNA methylation profiles of cultured
mesenchymal stromal/stem cells (MSC) derived from the bone
marrow of eight healthy individuals spanning a wide age range
(21–85 yr; Set-7) (Bork et al. 2010). Despite the small sample size,
the average methylation profile exhibited a significant linear in-
crease with age (t-test for linear trend, P = 0.003, Fig. 2D), with 59
of the 69 age-hypermethylated CpGs
demonstrating corresponding increases
in methylation, while 14 of the 20 age-
hypomethylated CpGs demonstrated co-
ordinate decreases (Fisher’s exact test, P =
4 3 10?6; Fig. 2H; Supplemental Table 4).
All these results demonstrate that
although the magnitude of methylation
changes differed between studies and tis-
sues (Supplemental Table 4), the 69 PCGT
CpGs (henceforth ‘‘age-PCGT’’ CpGs) de-
fined a robust age-related DNAm signa-
tissue, and cell type.
The age-PCGT signature discriminates
normal from preinvasive and invasive
methylation levels of age-PCGT CpGs
were higher than those of PCGT CpGs
not associated with age (Supplemental
Fig. 7). This suggested to us that the im-
plicated genes could be contributing
to carcinogenesis. We therefore hypothe-
sized that this age-PCGT signature could
be present in preinvasive lesions. As there
is still debate over the cell of origin, and
there is no well-defined preneoplastic
lesion for ovarian cancer, we used the
uterine cervix as a model to test this hy-
pothesis. We performed DNAm profiling
of48 age-matchedcervicalsmear samples
from premenopausal women (Table 1,
and -negative) and smears exhibiting dys-
plasia (all HPV-positive; Supplemental
material). We verified that the age of
the normal smears (Wilcoxon test, P =
0.86). Despite the relatively small sample
size and narrow age range of this pre-
menopausal sample set, we found that
PCGTs and our 69 age-PCGT CpG sub-
set were preferentially hypermethylated
with age (Supplemental Fig. 8). In addi-
tion, we observed that the 69 age-PCGT
CpGs were more highly methylated in
the HPV-positive samples exhibiting dysplasia compared with
HPV-positive and -negative normal samples (Fig. 3A). In contrast,
DNAm of PCGT CpGs that underwent hypomethylation in whole
blood did not correlate with progression (Fig. 3B), and non-
age-associated PCGT CpGs also did not exhibit methylation dif-
ferences between dysplasia and normal conditions (P = 0.47, Fig.
3C). In only 0.5% of 10,000 random choices of other 69 PCGTs
CpGs did we observe an association as strong as the one provided
by the age-PCGTs (P < 0.01, Fig. 3C). Clustering the 48 samples
over the 69 CpG methylation profiles also demonstrated that
inferred clusters correlated significantly with dysplasia (Fisher’s
exact test, P < 0.001, Fig. 3D).
vation of the ‘‘core’’ DNA methylation signature for aging. First, the supervised analysis was performed
separately for the blood samples from 148 healthy and 113 ovarian cancer cases. This yielded 293 CpGs
and 420 CpGs passing aFDR (q)cut-off of0.3. There was a strong overlap between these two signatures
(Fisher’s exact test, P = 10?30) with >80% concordance. Healthy and pretreatment samples were thus
combined and supervised analysis was performed on this larger set to identify with more confidence
a DNA methylation signature for aging. This gave 589 age-associated CpGs (q < 0.05), termed the
demonstrated a skew toward hypomethylation (binomial test, P = 6 3 10?9). Among the 226 hyper-
methylated CpGs, 69 mapped to polycomb group targets (PCGTs) (64 unique gene loci), while among
the 363 hypomethylated CpGs this number was only 20 (11 unique gene loci). Thus, relative to the
‘‘core’’ aging signature, PCGTs were preferentially hypermethylated (69 vs. 20 compared with 226 vs.
363, Fisher’sexact test, P < 4 3 10?12). Here, PCGTs were defined by promoter occupancy of any one of
SUZ12, EED, or H3K27me3 in human embryonic stem cells (Lee et al. 2006). (B,C) Enrichment odds
ratios with 95% confidence intervals for PCGTs (B) and for H3K27me3 marks (C), among the 226 age-
hypermethylated and 363 age-hypomethylated CpGs. H3K27me3 marks were defined by trimethylation
of H3K27 within gene body, promoter, and gene body + promoter regions in CD133+ hematopoietic
stem cells (HSC) (Cui et al. 2009). (D,E) Independent validation: enrichment odds ratios with 95% con-
fidence intervals for PCGTs among CpGs undergoing significant hyper- and hypomethylation with age in
188 blood samples from patients with type-1 diabetes (D) and 177 ovarian cancer samples (E). (Dashed
line) Line of unit odds ratio. Two-tailed P-values of enrichment (i.e., deviation from this line) are given.
DNAm signatures for aging and enrichment of PCGTs. (A) Flowchart depicting the deri-
Teschendorff et al.
Of the 64 age-PCGT genes, many have been reported to un-
dergo hypermethylation in cancer (Ongenaert et al. 2008). No-
tably, TP73 and SFRP1 have been reported to undergo hyper-
methylation in not less than 10 different cancers (Supplemental
Table 7). In line with this, we also observed that methylation levels
of age-PCGTs discriminated other cancers from their normal
counterparts (Supplemental Fig. 9; Bibikova et al. 2006).
frequent underexpression in cancer (Ben-Porath et al. 2008). We
therefore compared gene expression profiles in ovarian and cer-
vical cancer samples with their respective normal tissues (Scotto
et al. 2008; Mok et al. 2009). In both cases, we observed that age-
PCGTs exhibited average expression profiles that were signifi-
cantly lower in cancercompared with normal tissue (Fig. 3E,F). We
also observed that age-PCGTs were generally better discriminators
of ovarian cancer than 1000 random choices of other 64 PCGTs
(P = 0.06). Clustering over age-PCGTs further confirmed their
power to discriminate ovarian and cervical cancer from their re-
spective normal tissues (Supplemental Fig. 10). Interestingly, age-
PCGT mRNA expression also showed a gradual decrease with
cancer progression in a data set including preneoplastic lesions
(Supplemental Fig. 11; Wurmbach et al. 2007).
In this paper we have described a consistent directional change of
DNAm with age, characterized by hypermethylation of PCGTs
(Fig. 1; Supplemental Figs. 4, 8). While effect andsample sizes were
not large enough for us to ascertain which genes undergo age-
associated hypermethylation in a tissue-specific manner, the fact
trend toward hypermethylation with age across multiple cell types
(blood, ovarian cancer, cervix, mesenchymal stem cells) indicates
that a component of the identified signature is largely nonspecific
(Fig. 2; Supplemental Figs. 6–8). It is also very unlikely that the
identified age-PCGTsignature is caused by age-related variation in
cell-type composition. Indeed, as demonstrated in our recent work
(Teschendorff et al. 2009), we were able to correlate the age-asso-
ciated hypomethylation signature in blood with changes in blood
cell-type composition, but not so for the age-hypermethylated
methylated PCGTs (y-axis) as a function of age (x-axis) in validation data sets. Number of samples in each age group are given above the x-axis. t-test
P-values for linear trend derived from a robust linear regression are given; (green dashed line) best linear fit. (E–H ) Validation of age-associated (69
hypermethylated and 20 hypomethylated) PCGT CpGs in test sets. (X-axis) t-statistic of the linear regression test of age vs. methylation in the training set
(blood samplesfrom 148healthy +113 pretreatment ovarian cancercases).Colorsreflect directionality: (red)hypermethylated,(green)hypomethylated.
(Y-axis) t-statistic of the linear regression test of age vs. methylation in the test set. We provide the number of CpGs displaying significant hyper/hypo-
methylation in the training set and hyper/hypomethylation in the test set, as well as the corresponding Fisher’s exact test P-value. (A,E) Test set of blood
samples from an independent set of 108 healthy individuals spanning an age range of 50–80 yr. In A, age was categorized into six age groups (50–55,
56–60, 61–65, 66–70, 71–75, >75). (B,F ) Test set of blood samples from 188 T1D patients spanning an age range of 24–74 yr. In B, age was categorized
into six age groups (#35, 36–40,41–45, 46–50, 51–60, >60). (C,G)A test set ofovarian cancer samplesfrom 177 ovarian cancer patients spanning anage
range 24–88 yr. In C, age was categorized into six age groups (#40, 41–50, 51–60, 61–70, 71–75, >75). (D,H) A test set of eight bone marrow mes-
enchymal stromal cell samples from healthy donors of the following ages: 21, 24, 25, 50, 53, 79, 85, 85 (Bork et al. 2010).
External validation of specific age-associated PCGT DNAm signature. (A–D) Average beta-methylation values over the 69 age-hyper-
Age-dependent DNA methylation and cancer
signature. Consistent with this, we were also not able to validate
the age hypomethylation signature in tissues other than blood
(Supplemental Fig. 12), or to implicate it in carcinogenesis (Fig. 3B;
signature was able to discriminate preneoplastic from normal cells
and was found to be aggravated in invasive cancer leading to re-
may also be broadly implicated in aging and carcinogenesis (Sup-
plemental Figs. 12, 13).
To obtain direct functional proof that simultaneous silencing
of age-PCGT genes predisposes a cell to become malignant is not
possible with currently available technology. In the absence of
a functional test, there are, however, other lines of evidence sup-
portingthe role of age-PCGTs in carcinogenesis: (1) 36% (24/64) of
the 64 age-PCGT genes have already been published to be aber-
rantly methylated and deregulated in cancer; (2) 34% (22/64) of
these genes are transcription factors known to be involved in
normal differentiation. For instance, FOXC1 has been shown to
play an essential role in development (Myatt and Lam 2007) and is
also implicated in cancer (Bloushtain-Qimron et al. 2008). GATA4
belongs to the family of zinc finger–containing GATA transcrip-
tion factors, which play critical roles in cell lineage specification
is expressed in human ovarian surface epithelial cells and is im-
portant for the formation and maintenance of the differentiated
state of these cells (Capo-chichi et al. 2003; Caslini et al. 2006).
Loss of GATA4 expression precedes neoplastic transformation of
ovarian surface epithelia (Cai et al. 2009), and GATA4 is also
age-hypomethylated PCGT CpGs as a function of disease status in 48 cervical cytology samples. (HPVneg) Normal cervical sample not infected with HPV,
(HPVpos) normal cervical sample infected with HPV, (HPVpos-Dysplasia) samples infected with HPV and displaying dysplasia. Wilcoxon test P-value
between normalanddysplasticconditionis given. Numberofsamplesin eachgroup given below boxplots. (C)Histogram distribution of?log10(P-values)
from 1000 randomlyselected69non-age-associatedPCGT CpGs. P-valueswere derived fromthe Wilcoxon test.(Redline) ?log10(P-value) forthe 69age-
hypermethylated PCGT CpGs, (blue line) ?log10(P-value) for PCGT CpGs not mapping to age-PCGTs. In less than 0.5% of runs (P < 0.01) wereP-values as
extreme as the observed one, indicating that the age-PCGTs discriminate the dysplastic condition better than a random set of PCGTs. (D) Heatmap of the
48 cervival samples over the 69 age-hypermethylated PCGT CpGs. Samples were clustered using a Gaussian mixture model and three optimal clusters
were inferred using the Bayesian Information Criterion (see Supplemental material). (Orange, brown, pink) Distinct clusters. The disease status of samples
is labeled as a color bar (PROGR): (light green) HPVneg, (green) HPVpos+normal, (red) HPVpos+dysplasia. CpGs were clustered according to hierarchical
clustering with a Pearson correlation metric. Prior to sample and CpG clustering, methylation profiles of invividual CpGs were renormalized to mean zero
and unit standard deviation. Heatmap reflects, for each CpG, relative methylation levels across samples as determined by the renormalized methylation
profile. (Blue) Relative high methylation, (yellow) relative low methylation. (E,F ) Average gene expression intensity (Affymetrix) values for the 64 age-
hypermethylated PCGTs in normal ovarian (OvN) and ovarian cancer tissue (OvC) and in normal cervix (CVX-N) and cervical cancer (CVX-T). Number of
samples of each type and Wilcoxon test P-values are given.
Biological and clinical significance of age-PCGT DNAm signature. (A,B) Average methylation values of the 69 age-hypermethylated and 20
Teschendorff et al.
444 Genome Research
heavily methylated in ovarian cancer (Wakana et al. 2006). An-
other age-PCGT transcription factor is DLX5, whichis increasingly
methylated and silenced in MSC by both replicative senescence in
vitro and aging in vivo (Bork et al. 2010). As a final example, TP73
(also known as p73) shares many functional properties with the
TP53 (also known as p53) tumor suppressor and is also involved in
mediating DNA damage-induced apoptosis as well as suppressing
that age-dependent methylation and suppression of TP73 may
potentiallylead to genetic alterationsand increased predisposition
to cancer (Irwin et al. 2000; Moll and Slade 2004; Talos et al. 2007).
(3) Finally, alongside transcription factors, there are numerous
be involved in carcinogenesis, including ALOX5 (Catalano et al.
2005), SFRP1 (Wnt pathway) (Baylin and Ohm 2006), and KLF14
(TGF-beta signaling) (Truty et al. 2009).
In summary, we have found that age may contribute to car-
cinogenesis by irreversibly silencing genes that are suppressed in
stem cells. To our knowledge, this constitutes the first report of a
and carcinogenesis. Our findings may have broad implications
for cancer prevention, risk prediction, detection, prognosis, and
All DNAm data sets used in the study are summarized in Table 1.
The primary sample set consisted of 491 whole blood samples
drawn from the United Kingdom Ovarian Cancer Population
Study (UKOPS) (Table 1; Supplemental Table 1, Data Sets 1–4; Song
et al. 2009). Blood samples were taken at ages spanning a wide age
postmenopausal women (Set-1 and Set-3). The remaining samples
(n = 235) consisted of postmenopausal women diagnosed with pri-
mary epithelial ovarian cancer. About half of these (pre-treatment
[preT] cases; n = 113; Set-2) gave their blood at the time of their
diagnosis prior to treatment, and the other half (post-treatment
[posT] cases; n = 122; Set-4) gave their blood at some stage during
their follow-up visits after primary treatment (mean 2.4 6 2.7 yr
between diagnosis and blood sample taken). The distribution of all
these samples across batches is given in Supplemental Table 2. Set-5
consisted of 188 whole blood samples from patients with type 1
diabetes mellitus (CG Bell, AE Teschendorff, V Rakyan, AP Maxwell,
S Beck, and DA Savage, in prep.). Set-6 consisted of 177 ovarian
cancer tissue specimens from pre-and postmenopausal women.
Table 8. Details of the age distribution of samples per study is
shown in Supplemental Figure 1. Full experimental methods and
descriptions of other sample sets used in this study and any asso-
ciated references are available in the Supplemental material. Ethi-
cal approval has been obtained for all sample sets.
DNA methylation profiling and quality control
Methylation analysis was performed using the validated Illumina
Infinium Human Methylation27 BeadChip (Weisenberger et al.
2008). The methylation status of a specifc CpG site was calculated
from the intensity of the methylated (M) and unmethylated (U)
indicating methylation (no methylation). Quality control pro-
cedures are described in the Supplemental material. After quality
control, singular value decompositions (SVD) were used to assess
unwanted variation caused by experimental factors (variable bi-
sulfite conversion efficiency, plate and chip effects) and to test the
the Supplemental material).
All primary data used in this study are available at the NCBI
Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/)
under accession numbers GSE19711, GSE20067, and GSE20080.
Unsupervised analysis was performed using singular value de-
the number of significant components of variation and their as-
sociation with phenotypes (here, age). Supervised analyses were
performed for each CpG site separately, using a robust linear re-
gression model with age as the response and DNAm as the pre-
dictor, including covariates to model the batch, DNA input, and
bisulfite conversion efficiency effects. FDRs were evaluated ana-
lytically (q-values) (Storey and Tibshirani 2003) as well as using
between CpG sites into account. When data from potentially
confounding experimental factors were not available, we used the
2008) to perform the supervised analysis and FDR estimation.
Further details of methodology and software used are available in
the Supplemental material.
We thankall theindividualswhotookpartin thisstudyandallthe
researchers, clinicians, and administrative staff who have enabled
the many studies contributing to thiswork. In particular, we thank
Health NIHR Biomedical Research Centres funding scheme. A.E.T.
was supported by a Heller Research Fellowship. S.B. was supported
by the Wellcome Trust. D.S and P.M acknowledge the support of
the Renal Unit Fund, Belfast Health and Social Care Trust. This
work was supported in part by the Ovarian Cancer Research Fund
(OCRF)andNIH/NCIgrantR01-CA096958 (P.W.L.). We also thank
Keji Zhao and Chongzhi Zang for sending us processed ChIP-seq
Ahuja N, Li Q, Mohan AL, Baylin SB, Issa JP. 1998. Aging and DNA
methylation in colorectal mucosa and cancer. Cancer Res 58:
for early oncogenic pathway addiction? Nat Rev Cancer 6: 107–116.
Ben-Porath I, Thomson MW, Carey VJ, Ge R, Bell GW, Regev A, Weinberg
RA. 2008. An embryonic stem cell-like gene expression signature in
poorly differentiated aggressive human tumors. Nat Genet 40: 499–507.
Bibikova M, Lin Z, Zhou L, Chudin E, Garcia EW, Wu B, Doucet D, Thomas
NJ, Wang Y, Vollmer E, et al. 2006. High-throughput DNA methylation
profiling using universal bead arrays. Genome Res 16: 383–393.
Bjornsson HT, Sigurdsson MI, Fallin MD, Irizarry RA, Aspelund T, Cui H, Yu
W, Rongione MA, Ekstroem TJ, Harris TB, et al. 2008. Intra-individual
change over time in DNA methylation with familial clustering. JAMA
Bloushtain-QimronN, Yao J, Snyder EL, Shipitsin M, Campbell LL, Mani SA,
Hu M, Chen H, Ustyasnky V, Antosiewicz JE, et al. 2008. Cell type-
specific DNA methylation patterns in the human breast. Proc Natl Acad
Sci 105: 14076–14081.
Bork S, Pfister S, Witt H, Horn P, Korn B, Ho AD, Wagner W. 2010. DNA
methylation pattern changes upon long-term culture and aging of
human mesenchymal stromal cells. Aging Cell 9: 54–63.
Age-dependent DNA methylation and cancer
Cai KQ, Caslini C, Capo-chichi CD, Slater C, Smith ER, Wu H, Klein-Szanto
AJ, Godwin AK, Xu XX. 2009. Loss of GATA4 and GATA6 expression
specifies ovarian cancer histological subtypes and precedes neoplastic
transformation of ovarian surface epithelia. PLoS One 4: e6454. doi:
Capo-chichi CD, Roland IH, Vanderveer L, Bao R, Yamagata T, Hirai H,
Cohen C, Hamilton TC, Godwin AK, Xu XX. 2003. Anomalous
expression of epithelial differentiation-determining GATA factors in
ovarian tumorigenesis. Cancer Res 63: 4967–4977.
Caslini C, Capo-chichi CD, Roland IH, Nicolas E, Yeung AT, Xu XX. 2006.
Histone modifications silence the GATA transcription factor genes in
ovarian cancer. Oncogene 25: 5446–5461.
Catalano A, Rodilossi S, Caprari P, Coppola V, Procopio A. 2005.
5-Lipoxygenase regulates senescence-like growth arrest by promoting
ROS-dependent p53 activation. EMBO J 24: 170–179.
Christensen BC, Houseman EA, Marsit CJ, Zheng S, Wrensch MR, Wiemels
JL, Nelson HH, Karagas MR, Padbury JF, Bueno R, et al. 2009. Aging
and environmental exposures alter tissue-specific DNA methylation
dependent upon CpG island context. PLoS Genet 5: e1000602.
Cui K, Zang C, Roh TY, Schones DE, Childs RW, Peng W, Zhao K. 2009.
Chromatin signatures in multipotent human hematopoietic stem cells
indicate the fate of bivalent genes during differentiation. Cell Stem
Cell 4: 80–93.
Trends Genet 23: 413–418.
Fraga MF, Agrelo R, Esteller M. 2007. Cross-talk between aging and cancer:
The epigenetic language. Ann N YAcad Sci 1100: 60–74.
Hoeijmakers JH. 2009. DNA damage, aging, and cancer. N Engl J Med 361:
Irwin M, Marin MC, Phillips AC, Seelan RS, Smith DI, Liu W, Flores ER, Tsai
KY, Jacks T, Vousden KH, et al. 2000. Role for the p53 homologue p73 in
E2F-1-induced apoptosis. Nature 407: 645–648.
Issa JP, Ottaviano YL, Celano P, Hamilton SR, Davidson NE, Baylin SB. 1994.
Methylation of the oestrogen receptor CpG island links ageing and
neoplasia in human colon. Nat Genet 7: 536–540.
Issa JP, Vertino PM, Boehm CD, Newsham IF, Baylin SB. 1996. Switch from
monoallelic to biallelic human IGF2 promoter methylation during
aging and carcinogenesis. Proc Natl Acad Sci 93: 11757–11762.
Lee TI, Jenner RG, Boyer LA, Guenther MG, Levine SS, Kumar RM, Chevalier
B, Johnstone SE,ColeMF,Isono K,etal. 2006. Control ofdevelopmental
regulators by polycomb in human embryonic stem cells. Cell 125:
LeekJT,Storey JD.2007. Capturingheterogeneity ingene expression studies
by surrogate variable analysis. PLoS Genet 3: 1724–1735.
Leek JT, Storey JD. 2008. A general framework for multiple testing
dependence. Proc Natl Acad Sci 105: 18718–18723.
Mok SC, Bonome T, Vathipadiekal V, Bell A, Johnson ME, Wong KK, Park
for outcome in advanced ovarian cancer identifies a novel survival
factor: Microfibril-associated glycoprotein 2. Cancer Cell 16: 521–532.
Moll UM, Slade N. 2004. p63 and p73: Roles in development and tumor
formation. Mol Cancer Res 2: 371–386.
Myatt SS, Lam EW. 2007. The emerging roles of forkhead box (Fox) proteins
in cancer. Nat Rev Cancer 7: 847–859.
Nakagawa H, Nuovo GJ, Zervos EE, Martin EW Jr, Salovaara R, Aaltonen LA,
de la Chapelle A. 2001. Age-related hypermethylation of the 59 region
of MLH1 in normal colonic mucosa is associated with microsatellite-
unstable colorectal cancer development. Cancer Res 61: 6991–6995.
Ohm JE, McGarvey KM, Yu X, Cheng L, Schuebel KE, Cope L, Mohammad
HP, Chen W, Daniel VC, Yu W, et al. 2007. A stem cell-like chromatin
pattern may predispose tumor suppressor genes to DNA
hypermethylation and heritable silencing. Nat Genet 39: 237–242.
Ongenaert M, Van Neste L, De Meyer T, Menschaert G, Bekaert S, van
text-mining and expert annotation. Nucleic Acids Res 36: D842–D846.
Schlesinger Y, Straussman R, Keshet I, Farkash S, Hecht M, Zimmerman J,
Eden E, Yakhini Z, Ben-Shushan E, Reubinoff BE, et al. 2007. Polycomb-
mediated methylation on Lys27 of histone H3 pre-marks genes
for de novo methylation in cancer. Nat Genet 39: 232–236.
Scotto L, Narayan G, Nandula SV, Arias-Pulido H, Subramaniyam S,
Schneider A, Kaufmann AM, Wright JD, Pothuri B, Mansukhani M, et al.
2008. Identification of copy number gain and overexpressed genes
on chromosome arm 20q by an integrative genomic approach in
cervical cancer: Potential role in progression. Genes Chromosomes Cancer
So K, Tamura G, Honda T, Homma N, Waki T, Togawa N, Nishizuka S,
Motoyama T. 2006. Multiple tumor suppressor genes are increasingly
methylated with age in non-neoplastic gastric epithelia. Cancer Sci 97:
Song H, Ramus SJ, Tyrer J, Bolton KL, Gentry-Maharaj A, Wozniak E,
Anton-Culver H, Chang-Claude J, Cramer DW, DiCioccio R, et al. 2009.
A genome-wide association study identifies a new ovarian cancer
susceptibility locus on 9p22.2. Nat Genet 41: 996–1000.
Storey JD, Tibshirani R. 2003. Statistical significance for genomewide
studies. Proc Natl Acad Sci 100: 9440–9445.
Talos F, Nemajerova A, Flores ER, Petrenko O, Moll UM. 2007. p73
suppresses polyploidy and aneuploidy in the absence of functional p53.
Mol Cell 27: 647–659.
Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Gayther SA,
Apostolidou S, Jones A, Lechner M, Beck S, Jacobs I, et al. 2009. An
epigenetic signature in peripheral blood predicts active ovarian cancer.
PLoS One 4: e8274. doi: 10.1371/journal.pone.0008274.
Truty MJ, Lomberk G, Fernandez-Zapico ME, Urrutia R. 2009. Silencing of
the transforming growth factor-b (TGFb) receptor II by Kruppel-like
factor 14 underscores the importance ofanegativefeedbackmechanism
in TGFb signaling. J Biol Chem 284: 6291–6300.
Wakana K, Akiyama Y, Aso T, Yuasa Y. 2006. Involvement of GATA-4/-5
transcription factors in ovarian carcinogenesis. Cancer Lett 241:
Weisenberger DJ, den Berg DV, Pan F, Berman BP, Laird PW. 2008.
Comprehensive DNA methylation analysis on the Illumina Infinium assay
platform. Technicalreport.Illumina,Inc., SanDiego.http://www.illumina.
Widschwendter M, Jiang G, Woods C, Mu ¨ller HM, Fiegl H, Goebel G, Marth
C, Mueller-Holzner E, Zeimet AG, Laird PW, et al. 2004. DNA
hypomethylation and ovarian cancer biology. Cancer Res 64:
Widschwendter M, Fiegl H, Egle D, Mueller-Holzner E, Spizzo G, Marth C,
Weisenberger DJ, Campan M, Young J, Jacobs I, et al. 2007. Epigenetic
stem cell signature in cancer. Nat Genet 39: 157–158.
Wurmbach E, Chen YB, Khitrov G, Zhang W, Roayaie S, Schwartz M, Fiel I,
Thung S, Mazzaferro V, Bruix J, et al. 2007. Genome-wide molecular
profiles of HCV-induced dysplasia and hepatocellular carcinoma.
Hepatology 45: 938–947.
Received November 26, 2009; accepted in revised form February 11, 2010.
Teschendorff et al.