Correlation of an epigenetic mitotic clock with cancer risk

Yang, Zhen; Wong, Andrew; Kuh, Diana; Paul, Dirk S.; Rakyan, Vardhman K.; Leslie, R. David; Zheng, Shijie C.; Widschwendter, Martin; Beck, Stephan; Teschendorff, Andrew E.

doi:10.1186/s13059-016-1064-3

Research
Open Access
Published: 03 October 2016

Correlation of an epigenetic mitotic clock with cancer risk

Zhen Yang¹,
Andrew Wong²,
Diana Kuh²,
Dirk S. Paul³,
Vardhman K. Rakyan⁴,
R. David Leslie⁴,
Shijie C. Zheng¹,
Martin Widschwendter⁵,
Stephan Beck³ &
Andrew E. Teschendorff^1,5,6

Genome Biology volume 17, Article number: 205 (2016) Cite this article

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Abstract

Background

Variation in cancer risk among somatic tissues has been attributed to variations in the underlying rate of stem cell division. For a given tissue type, variable cancer risk between individuals is thought to be influenced by extrinsic factors which modulate this rate of stem cell division. To date, no molecular mitotic clock has been developed to approximate the number of stem cell divisions in a tissue of an individual and which is correlated with cancer risk.

Results

Here, we integrate mathematical modeling with prior biological knowledge to construct a DNA methylation-based age-correlative model which approximates a mitotic clock in both normal and cancer tissue. By focusing on promoter CpG sites that localize to Polycomb group target genes that are unmethylated in 11 different fetal tissue types, we show that increases in DNA methylation at these sites defines a tick rate which correlates with the estimated rate of stem cell division in normal tissues. Using matched DNA methylation and RNA-seq data, we further show that it correlates with an expression-based mitotic index in cancer tissue. We demonstrate that this mitotic-like clock is universally accelerated in cancer, including pre-cancerous lesions, and that it is also accelerated in normal epithelial cells exposed to a major carcinogen.

Conclusions

Unlike other epigenetic and mutational clocks or the telomere clock, the epigenetic clock proposed here provides a concrete example of a mitotic-like clock which is universally accelerated in cancer and precancerous lesions.

Background

Estimating the relative rate of stem cell divisions of a given tissue type between individuals may allow their stratification according to their prospective risk of cancer [1, 2]. It is therefore of interest to construct molecular mitotic-like clocks, which may provide an approximate estimate of the relative stem cell division rate of a tissue in an individual [3–5]. While telomere shortening represents a mitotic clock [6] and has been associated with increased cancer risk [7], these associations have, however, been largely inconsistent and only obtained in surrogate tissues such as blood [8]. A recently identified mutational clock-like signature [5] may also approximate a mitotic clock but has not yet been applied to cancer risk prediction.

Errors in the maintenance of DNA methylation (DNAm) arising during cell division may accumulate in the stem cell population of a tissue in line with the stem cell division rate and chronological age and have been proposed as molecular marks for a mitotic clock [3, 4, 9]. In addition, an increased rate of mitosis in the stem cell pool, possibly associated with cancer risk factors such as inflammation or viral infection, has been suggested to fuel epigenetic cellular heterogeneity and to lead to an increased epigenetic clonal mosaicism which may predispose the tissue to future neoplastic transformation [10–15]. Indeed, clonal genetic and copy number variation mosaicism has already been associated with the future risk of hematological cancers [16–19], and DNAm variability in normal cervical cells has been shown to predict the prospective risk of cervical cancer [15]. Given that many cancer risk factors have been associated with DNAm changes in normal cells [12, 15, 20–22], and preferentially at the same sites that undergo DNAm changes with age in healthy tissue [23, 24], we posited that a DNAm based mitotic-like clock could serve as a tool to predict cancer risk.

Here we report substantial progress towards the construction of such an epigenetic mitotic-like clock. Using a novel approach, based on an underlying mathematical model, we build a DNAm-based age-correlative model called “epiTOC” (Epigenetic Timer Of Cancer). A key feature underlying the construction of epiTOC is the focus on Polycomb group target (PCGT) promoter CpGs which are unmethylated in many different fetal tissue types, thus allowing us to define a proper ground state from which to then assess deviations in DNAm in aged tissue. By correlating the tick rate predictions of this model to the rate of stem cell divisions in normal tissue, as well as to an mRNA expression-based mitotic index in cancer tissue, we demonstrate that our model approximates a mitotic-like clock. Importantly, unlike Horvath’s epigenetic clock [25], the tick rate of epiTOC is universally accelerated in cancer, in preinvasive lesions, in normal epithelial cells at risk of neoplastic transformation, and in normal epithelial cells exposed to smoke carcinogens.

Results

Construction of the epiTOC model

By virtue of it being a highly accurate multi-tissue age predictor, Horvath’s clock cannot be a mitotic clock [14, 25]. Thus, in order to construct an age-correlative model which also reflects a mitotic clock-like process, we devised an alternative strategy, integrating mathematical modeling with previous biological knowledge (“Methods”). We reasoned that using only one tissue type from a large cohort of healthy individuals and focusing on CpG sites which, based on previous biological knowledge [26, 27], would more likely capture mitotic effects, relevant CpGs could be identified by correlation with chronological age (“Methods”; Fig. 1a). Specifically, we focused on CpGs satisfying the following criteria (justification in “Methods”): (1) CpGs that are constitutively unmethylated in fetal tissue encompassing many different tissue types [27]; (2) CpGs that map to gene promoters marked by the PRC2 polycomb repressive complex (also known as Polycomb group targets (PCGTs)) in human embryonic stem cells (hESCs) [26]; and (3) CpGs whose DNAm levels increase with chronological age [23]. Briefly, requirement 1 facilitates the construction of a mitotic-like clock since these CpGs all have comparable DNAm levels in a ground state of age zero, ensuring that deviations from this ground state are therefore also comparable between CpGs. Requirements 2 and 3 are justified based on prior biological knowledge that PCGT promoters undergo DNAm increases during hematopoietic ontogeny [26] and that they define age-associated signatures which are valid across different normal tissue types [23], including purified blood [24] and stem cell populations [23].

Using one of the largest Illumina 450 k DNAm datasets encompassing over 650 whole blood samples from healthy individuals spanning an age range of over 80 years [28] and correcting for changes in blood cell subtype composition (“Methods”; Additional file 1), we identified a subset of 385 PCGT promoter CpGs satisfying all required properties, including being unmethylated across 11 different fetal tissue types and exhibiting age-associated hypermethylation (false discovery rate <0.05) (“Methods”; Additional file 2). For each sample, epiTOC yields a score, denoted “pcgtAge”, as the average DNAm over these 385 CpG sites, representing the age-cumulative increase in DNAm at these sites due to putative cell-replication errors (Figure S1a in Additional file 3). Given that hypomethylation is also commonly observed in aging and cancer [29, 30], a separate age-correlative model based on promoter CpGs that are partially methylated in fetal tissue and which become hypomethylated with age was also derived (“Methods”; Figure S1b in Additional file 3). We validated both age-correlative models in an independent Illumina 450 k data set, encompassing over 300 whole blood samples from healthy individuals [31] (Figure S2 in Additional file 3). However, only epiTOC correlated with age in other normal tissue types (Figure S3 in Additional file 3), with the age-associated hypomethylation model showing inconsistent patterns (Figure S4 in Additional file 3).

epiTOC correlates with age in purified cell and stem cell populations

Although epiTOC was constructed by correcting for cellular heterogeneity in blood, age-associated DNAm changes could be non-linear and therefore linear correction for cell type composition may not effectively remove the effect of this confounder [32]. Hence, we sought to reconfirm that the identification of our 385 PCGT promoter CpGs was not affected by age-associated changes in blood cell type composition. To this end, we analyzed Illumina 450 k data from a cohort of healthy individuals spanning a wide age range of over 70 years, obtained from purified cells sorted using FACS and representing three different blood cell subtypes (CD4+ T cells, CD14+/CD16− monocytes, and CD19+ B cells) and encompassing 151 independent samples (“Methods”). In all three cell subtypes, the pcgtAge score correlated very significantly with chronological age (Fig. 2a; linear regression P = 0.0001 for B cells, P = 1e-9 for CD4+ T cells, and P = 2e-6 for monocytes). Despite the relatively small sample size of each purified sample set (n ~ 50), a relatively large fraction of the 385 PCGT CpGs were significantly hypermethylated with age in each set, with 91 % of the 385 PCGT CpGs correlating with age in at least one of these purified sample subsets (Fig. 2b). Further attesting that epiTOC correlates with chronological age independently of changes in cell type composition, we observed that pcgtAge also increased significantly with age in two additional purified cell 450 k sets profiling a larger set of samples (214 CD4+ T cell and 1202 monocyte samples) but spanning a much lower age range of ~40 years [33] (linear regression P < 1e-5 for T cells and P < 1e-9 for monocytes) (Figure S5 in Additional file 3).

We also performed a gene set enrichment analysis on the 385 PCGT CpGs that make up epiTOC to see if there is any evidence for these CpGs mapping to immune/blood cell subtype markers. To identify relevant blood cell or immune cell type terms, we first conducted the gene set enrichment analysis on top ranked CpGs in the Hannum et al. [28] data without correction for cellular heterogeneity, which, as expected, revealed strong enrichment of promoter CpGs mapping to genes underexpressed in lymphocytes and genes overexpressed in myeloid cells (Fig. 2c), consistent with the known increased myeloid–lymphoid ratio with age [34]. In contrast, these same biological terms were conspicuously absent and underenriched among the 385 PCGT epiTOC CpGs (Fig. 2c; Additional file 4).

Finally, we also assessed epiTOC in stem cell populations in order to support our underlying assumption that DNAm alterations at the epiTOC PCGT loci can accrue with age in a stem cell pool. We obtained Illumina Infinium 27 k DNA methylation data for a total of eight bone marrow-derived mesenchymal stem cell (MSC) populations of low passage number, representing a wide donor age range (20–80 years) [35], as well as for 12 CD34+ hematopoietic progenitor cell (HPC) populations derived from cord blood and adult peripheral blood [36]. In both studies, and despite the small sample sizes, the pcgtAge score correlated positively with donor age (linear regression P = 0.015 for MSCs and Wilcoxon rank sum test P = 0.037 for HPCs; Fig. 2d).

epiTOC approximates a mitotic clock

To demonstrate that epiTOC approximates a mitotic-like clock we computed the pcgtAge score in 288 normal samples from nine different tissue types collected from TCGA consortium [37] and for which independent estimates of the intrinsic stem cell division rates were available [2, 38] (Fig. 1b). Using the chronological age of the sample and the intrinsic cell division rate of the tissue, we obtained estimates of the cumulative total number of divisions incurred per stem cell in each sample (TNSC). Plotting these TNSC estimates on a log scale showed that samples spread mainly according to tissue type and secondly by age (Fig. 3a). On the natural unlogged scale, it revealed that the 288 normal samples clustered into three groups, characterized by a low, intermediate, and high cellular turnover (Fig. 3b). Fitting a linear regression, adjusted for chronological age, between the predicted pcgtAge from our model and the total number of stem cell divisions per stem cell in the sample revealed a strong positive correlation (P < 1e-26, R² = 0.45; Fig. 3b). Differences in pcgtAge between the cellular turnover groups were also statistically significant (Fig. 3d). As a negative control, the corresponding correlation between Horvath’s measure of age acceleration [25] and TNSC was either not significant (P = 0.39, R² ~ 0; Fig. 3c) or, in the case of between group comparisons, of only marginal significance (Fig. 3e).

To further demonstrate that pcgtAge provides a correlative measure of the cell division rate in a tissue, we posited that it would correlate with an mRNA expression-based mitotic index, which we constructed from the expression levels of genes that have been highly validated as being cell proliferation markers (“Methods”). This expression-based mitotic index was increased in all 15 TCGA cancer types compared to their corresponding normal tissue (Figures S6 and S7 in Additional file 3) and also correlated strongly with the TNSC estimates across normal tissues (P < 1e-21; Figure S8 in Additional file 3). Focusing on only the cancer samples from each of these cancer types, we obtained Pearson correlations between their pcgtAge and their expression-based mitotic index. Correlations were generally positive and much higher than those obtained using Horvath’s age-acceleration measure (Fig. 3f; paired Wilcoxon rank sum test P < 0.0001). For pcgtAge we observed a significant (P < 0.05) positive correlation in 7/15 cancer types, whilst for Horvath’s clock only one cancer type (KIRP) exhibited such an association (Fig. 3g). All these data support the view that epiTOC represents an approximate mitotic-like clock. In contrast, the model based on age-associated hypomethylation did not correlate well with the expression-based mitotic index in cancer tissue, although we did observe an excellent correlation with cellular turnover rates in normal tissue (Figure S9 in Additional file 3).

epiTOC predicts universal age acceleration in cancer and is further increased in cancer cell lines

Because cell proliferation is a cancer hallmark, we reasoned that epiTOC would predict an accelerated tick rate in all cancer types (Fig. 1c). We confirmed this using all age-matched normal–tumor pairs from 15 TCGA cancer types (Fig. 4). In contrast, Horvath’s clock and the age-hypomethylated CpG-based model did not consistently predict age acceleration in cancer (Figures S10 and S11 in Additional file 3). Of note, the pcgtAge score also outperformed the mRNA-based mitotic index, as a discriminator of normal/cancer status, in 9/15 cancer types (Figure S12 in Additional file 3).

Next, we asked if the pcgtAge score is also increased in cancer cell lines compared to cell lines of normal karyotype. Using Illumina 450 k DNAm data from 24 cancer and 29 normal cell lines, all profiled as part of ENCODE [39], we observed a significantly higher pcgtAge score in the cancer cell lines (Wilcoxon rank sum test P = 6e-14; Figure S13a in Additional file 3). Cancer cell lines generally also exhibited higher pcgtAge scores than those observed in cancer tissue. To confirm this, we compared the pcgtAge scores for 11 cancer types against the pcgtAge score of a corresponding representative cancer cell line (“Methods”). In all 11 cancer types, the cancer cell line exhibited a higher score than the average over corresponding cancer tissue samples (Wilcoxon paired rank sum test P = 0.005; Figure S13b in Additional file 3).

Increased epiTOC tick rate in pre-invasive cancer lesions

We further reasoned that pcgtAge might also be increased in pre-cancerous tissue owing to a marginal increase in cell proliferation. We computed pcgtAge in an independent Illumina 450 k dataset encompassing 21 normal lung tissue samples and 35 lung carcinoma in situ (LCIS) samples, of which 22 progressed to an invasive lung cancer [20]. This revealed a gradual increase in pcgtAge from normal lung, to LCIS, and to LCIS which progressed to invasive lung cancer (ILC), a result which was independent of chronological age (Fig. 5a). pcgtAge could discriminate normal lung from LCIS with an AUC of 0.88 (95 % confidence interval (CI) 0.79–0.97; Fig. 5a), as well as discriminating the LCIS which progressed to ILC from those which did not (AUC = 0.79, 95 % CI 0.63–0.94; Figure S14a in Additional file 3). In contrast to epiTOC, Horvath’s clock did not predict age acceleration in the LCIS samples and LCIS samples which progressed to ILC even exhibited substantial age deceleration (Fig. 5b). The age-hypomethylated model could neither discriminate normal from LCIS (Figure S15 in Additional file 3) nor predict cancer risk (Figure S14b in Additional file 3).

Most of these results were replicated in an Illumina 450 k dataset encompassing 14 normal breast tissues (from reduction surgery), 28 age-matched ductal carcinomas in situ (DCIS), and a further 13 age-matched DCIS samples from women who later developed an invasive breast cancer (IBC) [40] (Fig. 5c). Specifically, we observed an increased pcgtAge score in DCIS compared to normal breast (AUC = 1, P < 0.05; Fig. 5c). In contrast, neither Horvath’s measure of age acceleration nor the age-associated hypomethylation model could discriminate normal breast from DCIS samples (Fig. 5d; Figure S16 in Additional file 3). In another independent Illumina 450 k set which profiled 50 normal breast samples, 42 age-matched normal samples adjacent to a breast cancer and an additional 305 breast cancers [41], pcgtAge also showed a gradual age-independent increase between normal breast tissue and normal-adjacent breast tissue and breast cancer and was able to discriminate normal breast from normal-adjacent tissue (AUC = 0.66, 95 % CI 0.55–0.77; Fig. 5e). Again, this was not the case for Horvath’s age-acceleration measure nor for the analogous age-associated hypomethylation signature (Fig. 5f; Figure S17 in Additional file 3).

To translate the discrimination accuracies above into an estimate of the difference in incurred stem cell divisions, we integrated the estimate of the intrinsic rate of stem cell division in lung tissue [2, 38] with the epiTOC model, using the normal lung samples from TCGA to estimate the intercept and slope in our regression model (“Methods”). We obtained an estimate of 8.45 stem cell divisions per stem cell in the normal tissue compared to 13.33 divisions per stem cell in LCIS which did not progress to ILC and 22.02 divisions in the LCIS samples which progressed to ILC, representing an approximate threefold increase compared to normal samples. In the case of breast tissue, the underlying rate of stem cell division is unknown, but we could nevertheless estimate that DCIS samples had a 3.5-fold higher number of total stem cell divisions per stem cell compared to age-matched normal breast, a ratio similar to that of LCIS to normal lung tissue.

Increased epiTOC tickrate in normal buccal tissue of smokers

Next, we asked if epiTOC’s tick rate is increased in normal cells exposed to a major carcinogen. Given that smoking is a well-established major cancer risk factor for epithelial cancers like lung cancer [42, 43] and that smoking has been shown to be strongly associated with DNAm changes in epithelial cells, specifically in buccal tissue [20], we hypothesized that epiTOC would exhibit an accelerated rate in the buccal tissue of smokers compared to non-smokers. We estimated pcgtAge in a large cohort of 790 buccal samples from women all aged 53 years at the time of sampling and for which DNAm data with Illumina 450 k arrays had been generated and smoking pack-year (SPY) information was available for 647 of these women [20]. We focused on SPY as opposed to smoking status at the time of sampling given that SPY better reflects the smoking history of women [20]. Consistent with a model in which smoke carcinogens cause inflammation [44] and in turn an increased mitotic rate [12], the pcgtAge score significantly correlated with SPY (linear regression P < 1e-6; Fig. 6a). However, substantial variation in the pcgtAge scores unrelated to smoking exposure was also evident (Fig. 6a). In contrast, Horvath’s measure of age acceleration did not correlate with SPY (Fig. 6b), although the age-hypomethylated model did show a strong association (Figure S18 in Additional file 3).

For a subset of 152 women for which there was matched blood–buccal tissue, we found that the pcgtAge score in the two tissues did not correlate with each other (Fig. 6c), consistent with the fact that all women were of the same age and that the effect of inflammation (and hence cancer risk) is tissue-specific. In line with this, the association of the pcgtAge score with SPY was stronger in buccal tissue than in blood (Fig. 6d). In contrast, for Horvath’s age-acceleration measure, which in this cohort of identically aged women equals the predicted DNAm age, we observed a weak yet significant correlation across the two different tissues (Fig. 6e). Correspondingly, no association between Horvath’s age-acceleration measure and SPY was observed, irrespective of tissue type (Fig. 6f).

Discussion

Unlike telomere shortening [6–8], or the recently identified mutational clock-like signature [5], epiTOC provides a concrete example of a molecular mitotic-like clock which predicts universal acceleration in cancer. Although we acknowledge that current estimates of stem cell division rates in normal tissues are only very rough approximations, the observed correlation between epiTOC and the number of cell divisions per stem cell in over 288 normal samples was obtained by broadly categorizing tissues into groups of low, medium, and high cellular turnover, a classification which is likely to be robust (i.e., it is well accepted that colon has one of the highest cellular turnover rates of any tissue). Importantly, the pcgtAge score from epiTOC further correlated with an expression-based mitotic index in as many as seven different cancer types. In those cancer types where we did not see an association with the expression-based mitotic index, this could be due to small differences in proliferation rates between the actual tumors. Supporting this, the pcgtAge score was universally increased in cancer, as assessed in 15 tumor types encompassing over 5000 samples, in line with an increased mitotic rate being a universal cancer hallmark. The pcgtAge score was also accelerated in preinvasive cancer lesions, including LCIS and breast DCIS samples, allowing highly accurate discrimination of such lesions (AUC ~ 0.9–1). In addition, epiTOC was able to predict risk of an ILC, providing highly accurate identification (AUC ~ 0.8) of LCIS which progressed to an ILC (Figure S14a in Additional file 3). The estimated epiTOC tick rate in normal breast tissue adjacent to breast cancer was also accelerated, albeit at a much lower level, consistent with most of the cells in these samples being of normal cytology. Of note, we recently demonstrated the existence of DNAm field defects in the same normal-adjacent breast tissue samples [41]. That the pcgtAge score could discriminate normal tissue containing such field defects from the normal tissue of age-matched cancer-free women (AUC of 0.66, 95 % CI 0.55–0.77) suggests that epiTOC may serve to assess the risk of neoplastic transformation of normal tissue. Large prospective studies will, however, be required to demonstrate utility of epiTOC in a clinical setting.

Of note, the pcgtAge score also correlated with SPY in normal buccal tissue, highlighting the potential of epiTOC to capture putative increased cellular proliferation of epithelial cells that have been exposed to a major carcinogen. Although we did not measure inflammatory or cell proliferation markers in our buccal samples, previous studies have noted increased inflammation and proliferative activity in the oral epithelium of smokers [44, 45]. Although it is also plausible that the DNAm changes seen in the buccal epithelium of smokers reflects active changes associated with smoking-induced DNA damage, there is another observation supporting the view that inflammation and increased proliferation underlie most of the observed changes: smoking-associated DNAm changes seen in the buccal epithelium are generally very similar to those seen in healthy aging [20, 46]—for instance, as seen in blood cells from pediatric populations [47, 48]—or similar to those DNAm changes seen in inflammatory diseases [12, 49]. Indeed, a recent review highlighted enrichment of PCGT CpGs as a common DNAm signature which is seen in healthy aging of normal cells (which are presumably free of DNA damage), as well as in normal cells of the same chronological age but which have been exposed to a variety of different cancer risk factors [14]. Hence, the more likely explanation for the observed increased pcgtAge score in the buccal epithelial cells of smokers compared to non-smokers of the same age is a marginally increased proliferative activity, possibly associated with an inflammatory response to smoke toxins.

Assessment of epiTOC in cancer cell lines was less conclusive. Although cancer cell lines always exhibited a higher pcgtAge score than the average value of the corresponding cancer tissue, consistent with our epiTOC model and with reports that cancer cell lines exhibit higher levels of promoter methylation than cancer tissue [50], comparisons between cell lines and cancer tissue is difficult for a number of reasons. First, studies have shown that the artificial microenvironment and selection pressures of cell culture conditions can modify the genomic, DNAm, and expression patterns of the parental cells [35, 51–53]. Therefore, it is unclear whether the mechanism underlying DNAm changes upon cell division in tumors is also operative in cell lines. Second, a cell line derives from a parent cell which may grossly underrepresent the level of epigenetic heterogeneity in the stem cell pool of the primary cancer tissue, rendering comparisons between cell lines and independent cancer tissue samples problematic. Third, many cell lines would be needed to render meaningful comparisons with cancer tissue [54]. Notwithstanding these limitations, our data are broadly supportive of epiTOC, assuming that, once cells are cultured in vitro, the pcgtAge score ceases to reflect the mitotic age of the cells while still reflecting the mitotic age of the parent tissue.

The data presented here further confirm that Horvath’s clock is not a mitotic clock and that it does not exhibit a consistent universal acceleration in cancer or precancerous lesions. Indeed, by its very nature, Horvath’s clock was designed to predict chronological age independently of tissue type and must, therefore, reflect a biological process unrelated to cell division (since cell division rate is highly variable between tissues). While it is not yet clear what this biological process may be, the data presented here indicate that it is not a process that is uniformly altered in cancer, nor a process which appears accelerated in response to a major cancer risk factor such as smoking. In comparing epiTOC to Horvath’s clock, it is also important to point out that epiTOC does not attempt to predict chronological age of samples in absolute terms. This is not a limitation but, in fact, a key advantage of our approach, since any optimization procedure aimed at predicting chronological age as accurately as possible would not allow us to reliably identify the CpGs which reflect an underlying mitotic process.

Importantly, our work has further exposed a subtle difference between promoter CpGs that undergo age-associated hypermethylation from those that undergo hypomethylation, with the analogous model based on age-hypomethylated sites not correlating with the mitotic index in cancer tissue and correspondingly not exhibiting a consistent acceleration in cancer. This is consistent with a recent report by Lin and Wagner [55]. In fact, whereas the model based on hypomethylation also correlated with the number of stem cell divisions per stem cell in normal tissues, as well as with exposure to smoking in normal buccal tissue, it is intriguing that no consistent associations were found in cancer or preinvasive cancer lesions. This suggests that DNA hypermethylation errors associated with cell replication might be similar in cancer and normal stem cells but not so for the corresponding hypomethylation aberrations. It will be important for future studies to try to understand this deep and subtle asymmetry between age-associated hyper- and hypomethylation in relation to the changes seen in cancer.

Interestingly, the mRNA expression-based mitotic index also correlated with the estimated number of stem cell divisions, with the strength of correlation comparable to that obtained by epiTOC. Both mitotic indices were also comparable predictors of normal/cancer status (Figure S12 in Additional file 3). However, only the DNAm-based mitotic index correlates with chronological age. Indeed, it has been shown that mRNA expression signatures correlating with age are not strongly consistent or reproducible across independent studies and that the only consistent genes map to immune system and metabolic pathways and not to cell proliferation [56]. Thus, epiTOC has a higher sensitivity to detect differences in cell division numbers between differently aged samples of the same tissue type. Moreover, epiTOC could discriminate normal breast tissue adjacent to breast cancer from the corresponding normal tissue of age-matched healthy women (Fig. 5e). Although matched mRNA data were not available to assess this for an expression-based mitotic index, it is unclear whether expression based assays can offer the stability and reliability of a DNA-based assay to detect such subtle differences [57].

Although epiTOC correlates with stem cell division rate estimates in normal tissues and correctly predicts an increased tick rate in precancerous and cancer lesions, it is important to point out several limitations underlying our model. First, epiTOC assumes that the underlying DNAm errors are occurring in the underlying stem cell pool of a tissue and ignores active DNAm changes which may occur post-mitotically in response to exposure to various risk factors. As such, the epiTOC model may only capture one part of a more general cancer risk-predictive DNAm signature. However, by computing the pcgtAge score in populations of mesenchymal stem cells and hematopoietic progenitor cells, we have seen that epiTOC does predict an increased mitotic age in older stem/progenitor cell populations, supporting the view that it may measure mitotic age of the stem cell pool in epithelial tissues. A second limitation of our model is that it largely ignores absolute stem cell numbers in the tissue of interest, which may vary substantially between individuals and even within an individual as a function of current exposure to environmental factors. The absolute number of stem cells in a tissue is expected to be an important determinant of epigenetic stem cell heterogeneity and clonal mosaicism and, therefore, of cancer risk, as proposed by a number of studies [10, 13, 58]. This is therefore an important caveat, since our model does not distinguish between a tissue with a lower number of stem cells carrying lots of DNAm changes from another tissue with a large number of stem cells, each carrying only a few yet unique DNAm alterations. Nevertheless, assuming similar stem cell numbers, our pcgtAge score would be expected to increase in line with the level of epigenetic heterogeneity in the stem cell pool and thus be informative of cancer risk. It is also worth stressing that the observed correlation of the pcgtAge score with the tissue’s cellular turnover rate does not depend on any estimates of the actual number of stem cells in the tissue of interest. Indeed, the pcgtAge score is an intensive variable, defined as an average over cells and a specific set of loci, and, as such, correlation can only be assessed meaningfully relative to another intensive variable. This other intensive variable is the cumulative number of cell divisions per stem cell in the tissue of interest and not the extensive variable defined as the total number of stem cell divisions in the tissue. For this reason, our model does not make, and cannot make, any predictions of cancer risk between different tissues.

In spite of these limitations, epiTOC may allow, in principle, prediction of stem cell division numbers if absolute stem cell numbers remain constant, if the intrinsic division rate in a tissue is known and if a large DNAm data set of that tissue from healthy individuals is available. In the case of lung tissue, for which estimates of the intrinsic rate of stem cell division are available [2, 38], and using normal-adjacent samples from the TCGA to train the model, we found that LCIS samples exhibited a total of about 13 to 22 divisions per stem cell. This is significantly larger than the approximate eight to nine divisions per stem cell for the normal lung samples, which in turn is only marginally higher than the life-time number of divisions per stem cell in lung tissue (approximately six divisions per stem cell) as estimated by Tomasetti and Vogelstein [2]. Given that it has been estimated that lung tissue contains about one billion stem cells, and assuming that a 2 cm sized LCIS contains 0.5 % of these stem cells, this translates into approximately (22/8) × 0.005 × 10⁹ ~ 14 million additional cell divisions in LCIS compared to a normal sample. We acknowledge that these estimates are only very rough approximations, yet they are also not necessary for the purpose of cancer risk prediction where only the tick rate relative to that of a normal reference tissue is required.

Finally, it is of interest to discuss the relation of epiTOC to a recently proposed “Big Bang” model of early cancer evolution as proposed by Sottoriva et al. [59]. This Big Bang model describes the clonal expansion of a tumor and can accurately explain the observed patterns of spatial genetic heterogeneity within the tumor. In contrast, epiTOC provides an estimate of the relative number of stem cell divisions per stem cell in a tissue of interest, which may approximate the level of epigenetic stem cell heterogeneity within the normal tissue before the cancer clone emerges. Once the cancer clone has emerged, the increased cellular proliferation of this clone would be expected to generate a “large wave” of further DNAm changes that may affect most of the genome. Thus, when applying epiTOC to cancer tissue, the pcgtAge score is expected to correlate strongly with the level of proliferation of the tumor, and we have indeed demonstrated this in as many as 15 TCGA cancer types. Moreover, we recently demonstrated that the pattern of aberrant DNAm of one tumor sample can explain, on average, about 60 % (R² ~ 0.6) of the aberrant DNAm variation of another tumor, even if from a different cancer type [60], suggesting that a common process underlies most of the DNAm variation in the cancer genome. This common universal process is likely to be an increased cell division rate. The epigenetic epiTOC and genetic Big Bang model are therefore mainly aimed at describing different phases of the carcinogenic process, with epiTOC providing an approximate measure of cell division numbers per stem cell before the first cancer clone emerges and with the Big Bang model providing a description of subsequent cancer evolution. Both models together are highly consistent with an overarching “phase transition” model of oncogenesis, in which epigenetic clonal mosaicism is maximal before cancer emerges [61].

Conclusions

The epigenetic mitotic clock-like signature presented here exhibits a consistent universal pattern of acceleration in cancer, in precancerous epithelial lesions, and in normal epithelial cells exposed to a major carcinogen. We propose that DNAm-based models such as epiTOC may constitute informative biomarkers of cancer risk if evaluated in the cell of origin or in a relevant surrogate tissue.

Methods

Construction of an epigenetic mitotic clock (epiTOC): a mathematical model

In order to provide the rationale for the procedure of constructing epiTOC, as described in the next subsection, we here first present the salient features of the underlying mathematical model. We assume that cancer risk of a tissue type t in an individual s, which we denote as CR(s,t), is a monotonically increasing function f of the total number of stem cell divisions (per stem cell) incurred in the tissue, i.e., we assume that:

$$ CR\;\left(s,t\right)=f\left[ TNSC\left(s,t\right)\right] $$

where TNSC is the total number of stem cell divisions, which will depend on tissue type t and individual s. We assume further that TNSC can be approximated as:

$$ TNSC\left(s,t\right)=A(s)\left[IR(t) + E(s)ER(t)\right]=A(s)IR(t) + A(s)E(s)ER(t) $$

(1)

where A(s) denotes the chronological age of the individual s, IR(t) denotes the intrinsic rate of stem cell divisions per stem cell in tissue type t, E(s) is a complex non-linear (generally unknown) positively valued function representing the exposure of individual s to a cancer risk factor, and where ER(t) denotes an extrinsic rate of stem cell division associated with exposure to the cancer risk factor and which we assume may depend on tissue type t. Note that we assume that the intrinsic rate IR only depends on tissue type and that it is, therefore, independent of chronological age and individual s, so we are ignoring genetic factors which may influence the rate of stem cell division.

Motivated by previous work [4], we further assume that specific CpG sites in the genome acquire stochastic DNAm errors during cell replication and that the cumulative number of DNAm errors is a linear function of the total number of stem cell divisions per stem cell. We denote the cumulative amount of DNAm errors as “pcgtAge” in anticipation of how the corresponding CpGs will be identified (see the “Construction of an epigenetic mitotic-like clock: CpG selection” section below). Hence, we can also write a linear model of the form:

$$ pcgtAge\left(s,t\right) = \alpha (t) + \xi (t)\ TNSC\left(s,t\right) + \varepsilon $$

(2)

However, we can’t train a DNAm-based model with TNSC, since the latter depends on the exposures which are generally unknown. Instead, we could link Eqs. 1 and 2 above to train a DNAm-based model. Since IR is dependent on the tissue type, and since E(s) and ER(t) are generally unknown quantities, it is best to focus on one tissue type only and to consider a healthy population of individuals. This then allows us to assume that E(s) ≈ 0 and that IR(t) = constant, so, to a first approximation:

$$ \begin{array}{c} pcgtAge\left(s,t\right) = \alpha (t) + \xi (t)\ A(s)IR(t) + \varepsilon \\ {} = \alpha (t) + \gamma (t)\ A(s) + \varepsilon \end{array} $$

where we have absorbed the term IR into a new slope coefficient. Thus, to identify CpGs whose DNAm levels correlate with TNSC, it is justified to correlate DNAm to chronological age, as long as we use one tissue type and focus on healthy individuals. However, since many biological processes may be associated with distinct age-associated DNAm changes, it is necessary to make a selection of CpGs which are more likely to capture the DNAm aberrations caused by cell division.

Construction of an epigenetic mitotic-like clock: CpG selection

Motivated by the above mathematical model, we posited that we could identify relevant CpGs as follows: (i) identify CpG sites undergoing age-associated DNAm changes in a large DNAm dataset of healthy individuals, encompassing one tissue type only and correcting for potential changes in cell type composition; (ii) identify a subset of these that map to PCGT promoters, i.e., marked by the PRC2 complex, and which are constitutively unmethylated in a ground state of age zero (e.g., fetal tissue).

To justify (i), we reasoned that using multiple tissues, which would naturally differ in their mitotic tick rates, would only hamper or confound derivation of a mitotic clock (see above mathematical model). Correction for underlying changes in cell type composition is, however, an important potential confounder if we are to use only one tissue type. For these reasons, we used the dataset of 656 whole blood samples from Hannum et al. [28], representing one of the largest cohorts of healthy individuals which have been profiled with Illumina 450 k DNAm beadarrays, and a tissue type (blood) for which accurate correction for changes in blood cell type composition is possible [62]. We justify (ii) on grounds that a recent study has shown that DNAm changes occurring during hematopoietic ontogeny involve preferentially DNAm increases at PCGT promoters, i.e., sites marked by the PRC2 complex [26]. Thus, we reasoned that focusing on a subset of such promoter CpGs which are also constitutively unmethylated in a large set of fetal tissues [63] would provide us with the right markers to measure the rate of cell division.

In detail, using Hannum’s whole blood samples, we ran linear regressions of chronological age versus DNA methylation beta profiles adjusted for plate, sex, and estimates of blood cell subtypes. Estimates of blood cell subtypes were obtained using quadratic programming [64] and a novel blood cell subtype DNAm reference matrix (Additional file 1) constructed by integrating the Illumina 450 k DNAm data from Reinius et al. [65] with blood cell subtype-specific DNase hypersensitive site data from the NIH Epigenomics Roadmap (Teschendorff A et al: A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies, submitted). Age-associated CpGs were selected at a false discovery rate threshold of <0.05. Subsequently, these age-CpGs were filtered for those mapping unambiguously to within 200 bp of a transcription start site (TSS200 probes). We note that this restriction to TSS200 probes was done in order to minimize differences in the ground state (i.e., at age zero) methylation levels between probes, which facilitates the later construction of the pcgtAge score as an average of the probes. With this restriction to TSS200 probes as well as the inherent restricted coverage of the 450 k beadarrays, we nevertheless still covered 72 % of all PCGT promoters as defined in Lee et al. [66]. The age-associated TSS200 CpGs were then divided into age-hypermethylated CpGs and age-hypomethylated ones. Age-hypermethylated CpGs were filtered further, selecting only those with absent or low (beta <0.2) methylation across 52 fetal tissue samples encompassing 11 tissue types (cord blood (GSE72867), stomach, heart, tongue, kidney, liver, brain, thymus, spleen, lung, adrenal gland [63]). These unmethylated promoter CpGs were divided further into those marked by PRC2 in human embryonic stem cells (hESCs) and those that are not, according to the annotation provided [66]. This resulted in an age-hypermethylated set of 385 CpGs, which we denote “pcgtAge”. In the case of the age-hypomethylated promoter CpGs, we selected those with a methylation level of at least 0.3 across all 52 fetal tissue samples in order to guarantee that the observed hypomethylation at these sites is genuine and of potential biological significance. This resulted in a second set of 656 CpGs, denoted “hypoAge”.

Age-correlative DNAm deviation scores, pcgtAge and hypoAge, were then calculated as the average DNAm over the respective CpG sites. Mathematically, pcgtAge for sample s in tissue type t, is calculated as:

$$ pcgtAge\left(s,t\right)=\frac{1}{n}{\displaystyle {\sum}_{c=1}^n{\beta}_{cst}} $$

By construction, this score should correlate with chronological age, and we can estimate parameters α’ and γ’ from fitting a linear model:

$$ pcgtAge\left(s,t\right)\kern0.5em =\kern0.5em \alpha (t)\kern0.5em +\kern0.5em \gamma (t)\kern0.5em A(s)+\kern0.5em \varepsilon ' $$

From this, we can now obtain an estimate of TNSC(s,t) for a sample of the same tissue type t but which is not healthy, e.g., one exposed to a cancer risk factor:

$$ TNSC\left(s,t\right)=\left[ pcgtAge\left(s,t\right)\kern0.5em \hbox{--} \kern0.5em \alpha '(t)\right]IR(t)/\kern0.5em \gamma '(t) $$

(3)

since estimates for IR(t) are available from the literature [2]. Thus, for the same tissue type t used in the training (i.e., blood) and from which the estimates α’ and γ’ were obtained, we can derive estimates of TNSC but not so for a different tissue type (e.g., lung or breast). However, the second term in Eq. 3 above is independent of the sample s; hence, one can write for the cancer risk:

$$ CR\left(s,x\right)=f\left[ TNSC\left(s,x\right)\right]=f\left[ pcgtAge\left(s,x\right)IR(t)/\ \gamma '(t)\kern0.5em \hbox{--} \kern0.5em term(t)\right] $$

and so, if pcgtAge(s ₁,x) > pcgtAge(s ₂,x), then the cancer risk (CR) of sample s ₁ is also greater than that of s ₂: CR(s ₁,x) > CR(s _2, x). Hence, a higher pcgtAge score should be indicative of a higher mitotic tick rate and indicate a higher cancer risk, which we can formally test if data for pre-cancerous lesions are available. Similarly, a lower hypoAge score would correspond to a higher cancer risk.

From Hannum et al. [28] whole blood data set, we estimated for the pcgtAge model that α’ = 0.052 and γ’ = 0.000345. From the 81 normal breast tissue samples from TCGA, we estimated α’ = 0.053 and γ’ = 0.000165, and from the combined 73 normal lung samples from the TCGA we estimated α’ = 0.021 and γ’ = 0.000588.

We note that even in the absence of an IR(t) estimate one can still estimate the relative TNSC numbers of two samples s ₁ and s ₂ of the same tissue type since the ratio:

$$ TNSC\left({s}_1,\ t\right)\kern0.5em /\kern0.5em TNSC\left({s}_{2, }t\right)\kern0.5em =\kern0.5em \left[ pcgtAge\left({s}_1,t\right)\ \hbox{--}\ \alpha '(t)\right]\kern0.5em /\kern0.5em \left[ pcgtAge\left({s}_2,t\right)\kern0.5em \hbox{--} \kern0.5em \alpha '(t)\right] $$

does not depend on IR(t).

Validation of the age-correlative models in blood and other normal tissue types

EpiTOC and the analogous model based on age-hypomethylated sites were tested in the large independent whole blood Illumina 450 k dataset of Liu et al. [31] using only healthy controls (over 300 samples). We also tested these two models in other normal tissue types. For this purpose, we used normal-adjacent tissue from TCGA, focusing on tissues for which there were enough normal samples and for which there had been corresponding fetal tissue used in the derivation and selection of the CpGs making up these models. This included 38 normal colon (normal-adjacent tissue to colon adenocarcinoma (COAD)), 160 normal kidney (adjacent to kidney renal cell carcinoma (KIRC)), 47 normal liver (adjacent to liver hepatocellular carcinoma (LIHC)), and 73 normal lung samples (adjacent to lung squamous cell carcinoma (LSCC)/ lung adenocarcinoma (LUAD)).

Validation of the epigenetic mitotic clock in normal tissue

In order to demonstrate that our age-correlative models define approximate mitotic clocks, i.e., that the relation TNSC(s,t) ~ pcgtAge(s,t) holds, we estimated the age-correlative scores in the normal tissue samples of TCGA [37], for which estimates of the intrinsic stem cell division rates (IR) per stem cell and per year are available from [2]. Specifically, this included colon (IR = 73 divisions per stem cell per year), rectum (IR = 73), esophagus (IR = 17.4), head and neck (IR = 21.5), liver (IR = 0.9125), lung (IR = 0.07), pancreas (IR = 1) and thyroid (IR = 0.087), encompassing a total of 288 normal tissue samples. Cumulative total number of cell divisions per stem cell (i.e., TNSC) was estimated for each of these 288 normal “healthy” samples as the product of chronological age (tissue-independent) and the corresponding rate IR (tissue-dependent), i.e., as TNSC(s,t) = A(s)IR(t). The sample-specific scores pcgtAge, nonpcgtAge, and hypoAge were then correlated to these sample-specific cumulative cellular turnover rates using a linear regression framework adjusted for chronological age (in order to avoid the expected trivial correlation by age).

Validation of the epigenetic mitotic clock in cancer tissue: construction of an mRNA expression based-mitotic index

Because cell division rates are altered in cancer, we validated the mitotic nature of the age-correlative scores pcgtAge and hypoAge in cancer samples by comparison of these scores to an mRNA expression-based mitotic index. This mitotic index was constructed by first taking the interConstruction of the epiTOC model of genes in the cell proliferation cluster of Ben-Porath et al. [67] and those of the proliferation signature of Rhodes et al. [68]. This resulted in nine genes (CDKN3, ILF2, KDELR2, RFC4, TOP2A, MCM3, KPNA2, CKS2, and CDC2). The mitotic index was then defined as the average mRNA expression over these nine genes. This mRNA expression-based mitotic index was validated in 15 cancer types of TCGA by demonstrating that it is significantly increased in each cancer type compared to its corresponding normal tissue type. We verified that it was a more reliable mitotic index than PCNA expression (not shown).

Cancer and pre-cancerous Illumina 450 k datasets

We downloaded and processed level 3 Illumina 450 k and RNA-SeqV2 data from TCGA [37], as described by us previously [69]. In total, we considered 15 cancer types: BLCA (bladder adenocarcinoma, nN = 19, nC = 204), BRCA (breast adenocarcinoma, nN = 81, nC = 652), COAD (colon adenocarcinoma, nN = 38, nC = 272), ESCA (esophageal carcinoma, nN = 15, nC = 126), HNSC (head and neck squamous cell carcinoma, nN = 45, nC = 402), KIRC (kidney renal cell carcinoma, nN = 160, nC = 299), KIRP (kidney renal papillary carcinoma, nN = 45, nC = 196), LIHC (liver hepatocellular carcinoma, nN = 47, nC = 176), LSCC (lung squamous cell carcinoma, nN = 41, nC = 275), LUAD (lung adenomacarcinoma, nN = 32, nC = 399), PAAD (pancreatic adenoma carcinoma, nN = 10, nC = 146), PRAD (prostate ademoma carcinoma, nN = 48, nC = 278), READ (rectal adenoma carcinoma, nN = 7, nC = 95), THCA (thyroid carcinoma, nN = 53, nC = 489), and UCEC (uterine cervix endometrial carcinoma, nN = 34, nC = 374).

We used Illumina 450 k DNAm data from the three previous publications and which had profiled precursor cancer lesions or normal-adjacent tissue in addition to normal samples. Briefly, these datasets were: (i) a dataset of normal lung and lung carcinoma in situ (LCIS) samples, with a subset of these progressing to an invasive lung cancer, previously described in [20]; (ii) a dataset of normal breast tissue and ductal carcinoma in situ (DCIS) samples, with a subset of these progressing to an invasive breast cancer, previously described in [40]; (iii) a dataset of 50 normal breast tissue samples, 42 matched normal-adjacent breast tumor pairs, and an additional 263 breast cancers, previously described in [41].

Age-correlative scores pcgtAge and hypoAge were estimated as average DNAm levels over the corresponding CpG sites in all of these samples.

Buccal tissue Illumina 450 k set

Illumina 450 k DNAm profiles were generated for buccal samples from 790 women, all aged 53 at the time of sampling, as described by us previously [20]. For a subset of 152 women, there were matched buccal–blood samples. We used the normalized data as used in our previous publication.

Non-TCGA cancer tissue and ENCODE cell line DNAm datasets

Illumina 450 k DNAm data for 32 glioblastoma multiformes (GBM) were downloaded from the Gene Expression Omnibus (GEO; accession number GSE30338) [70] and normalized with BMIQ. Illumina 450 k data for 215 ovarian cancers was processed and normalized with BMIQ as described by us previously (GEO: GSE74845) [71]. Illumina 27 k DNAm data for a total of 49 cervical cancer epithelial samples were processed and normalized as described by us previously (GEO: GSE30759) [15]. Cell line Illumina 450 k DNAm data for 62 cell lines was obtained from ENCODE via GEO (GSE40699). These data were subsequently normalized with BMIQ.

Purified T-cell, B-cell and monocyte Illumina 450 k sets

Illumina 450 k profiling was performed on 100 purified CD19+ B-cell samples, 98 CD4+ T-cell samples, and 104 CD14+/CD16− monocytes from a total of 52 monozygotic twins discordant for type 1 diabetes, as described by us previously (Paul D et al: Increased DNA methylation variability in type 1 diabetes across three immune effector cell types, submitted) Here we only used the samples from the healthy controls, amounting to 50 B-cell, 49 T-cell, and 52 monocyte samples. Across all cell types, the mean cell purity was 90 %. The Illumina 450 k data were processed as described (Paul D et al: Increased DNA methylation variability in type 1 diabetes across three immune effector cell types, submitted) and are available from the European Genome-phenome Archive (EGA; https://www.ebi.ac.uk/ega/) under accession number EGAS00001001598.

In addition, we used Illumina 450 k data of an independent set of purified CD4+ T-cell (n = 214) and monocyte (n = 1202) samples, as generated by the MESA study [33]. These data were downloaded from the GEO (GSE56046 and GSE56581). Intra-array normalization was performed with BMIQ.

Stem cell Illumina DNAm sets

We downloaded normalized Illumina 27 k data for two sets of stem cell-like cell populations. One set had profiled eight mesenchymal stem cell (MSC) samples (all of the same low passage number of 2) collected from the bone marrow of eight donors of widely different ages [35]. Data were obtained from the GEO (GSE17448). Another set consisted of 12 CD34+ hematopoetic progenitor cell (HPC) samples collected from either cord blood (n = 7) or mobilized peripheral blood from adults (n = 5, age range 28 to 41 years) [36]. Data were obtained from EBI’s ArrayExpress repository (E-MTAB-487).

Software availability

An R script implementing epiTOC and the associated probe IDs of the CpGs making up epiTOC is available as Additional files 5 and 6.

Abbreviations

AUC:: area under the curve
CI:: confidence interval
DCIS:: ductal carcinoma in situ
DNAm:: DNA methylation
EGA:: European Genome-phenome Archive
GEO:: Gene Expression Omnibus
hESC:: human embryonic stem cell
HPC:: hematopoietic progenitor cell
IBC:: invasive breast cancer
ILC:: invasive lung cancer
LCIS:: lung carcinoma in situ
MSC:: mesenchymal stem cell
PCGT:: Polycomb group target
SPY:: smoking pack-year
TCGA:: The Cancer Genome Atlas
TNSC:: total number of stem cell divisions per stem cell.

References

Wu S, Powers S, Zhu W, Hannun YA. Substantial contribution of extrinsic risk factors to cancer development. Nature. 2016;529:43–7.
CAS Article PubMed Google Scholar
Tomasetti C, Vogelstein B. Cancer etiology. Variation in cancer risk among tissues can be explained by the number of stem cell divisions. Science. 2015;347:78–81.
CAS Article PubMed PubMed Central Google Scholar
Siegmund KD, Marjoram P, Woo YJ, Tavare S, Shibata D. Inferring clonal expansion and cancer stem cell dynamics from DNA methylation patterns in colorectal cancers. Proc Natl Acad Sci U S A. 2009;106:4828–33.
CAS Article PubMed PubMed Central Google Scholar
Kim JY, Tavare S, Shibata D. Counting human somatic cell replications: methylation mirrors endometrial stem cell divisions. Proc Natl Acad Sci U S A. 2005;102:17739–44.
CAS Article PubMed PubMed Central Google Scholar
Alexandrov LB, Jones PH, Wedge DC, Sale JE, Campbell PJ, Nik-Zainal S, Stratton MR. Clock-like mutational processes in human somatic cells. Nat Genet. 2015;47:1402–7.
CAS Article PubMed PubMed Central Google Scholar
Blackburn EH, Greider CW, Szostak JW. Telomeres and telomerase: the path from maize, Tetrahymena and yeast to human cancer and aging. Nat Med. 2006;12:1133–8.
CAS Article PubMed Google Scholar
Willeit P, Willeit J, Mayr A, Weger S, Oberhollenzer F, Brandstatter A, Kronenberg F, Kiechl S. Telomere length and risk of incident cancer and cancer mortality. JAMA. 2010;304:69–75.
CAS Article PubMed Google Scholar
Zhang C, Doherty JA, Burgess S, Hung RJ, Lindstrom S, Kraft P, Gong J, Amos CI, Sellers TA, Monteiro AN, et al. Genetic determinants of telomere length and risk of common cancers: a Mendelian randomization study. Hum Mol Genet. 2015;24:5356–66.
CAS Article PubMed PubMed Central Google Scholar
Yatabe Y, Tavare S, Shibata D. Investigating stem cells in human colon by using methylation patterns. Proc Natl Acad Sci U S A. 2001;98:10839–44.
CAS Article PubMed PubMed Central Google Scholar
Issa JP. Epigenetic variation and cellular Darwinism. Nat Genet. 2011;43:724–6.
CAS Article PubMed Google Scholar
Feinberg AP, Ohlsson R, Henikoff S. The epigenetic progenitor origin of human cancer. Nat Rev Genet. 2006;7:21–33.
CAS Article PubMed Google Scholar
Issa JP. Aging and epigenetic drift: a vicious cycle. J Clin Invest. 2014;124:24–9.
CAS Article PubMed PubMed Central Google Scholar
Timp W, Feinberg AP. Cancer as a dysregulated epigenome allowing cellular growth advantage at the expense of the host. Nat Rev Cancer. 2013;13:497–510.
CAS Article PubMed PubMed Central Google Scholar
Zheng SC, Widschwendter M, Teschendorff AE. Epigenetic drift, epigenetic clocks and cancer risk. Epigenomics. 2016;8:705–19.
CAS Article PubMed Google Scholar
Teschendorff AE, Jones A, Fiegl H, Sargent A, Zhuang JJ, Kitchener HC, Widschwendter M. Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation. Genome Med. 2012;4:24.
CAS Article PubMed PubMed Central Google Scholar
Jacobs KB, Yeager M, Zhou W, Wacholder S, Wang Z, Rodriguez-Santiago B, Hutchinson A, Deng X, Liu C, Horner MJ, et al. Detectable clonal mosaicism and its relationship to aging and cancer. Nat Genet. 2012;44:651–8.
CAS Article PubMed PubMed Central Google Scholar
Laurie CC, Laurie CA, Rice K, Doheny KF, Zelnick LR, McHugh CP, Ling H, Hetrick KN, Pugh EW, Amos C, et al. Detectable clonal mosaicism from birth to old age and its relationship to cancer. Nat Genet. 2012;44:642–50.
CAS Article PubMed PubMed Central Google Scholar
Genovese G, Kahler AK, Handsaker RE, Lindberg J, Rose SA, Bakhoum SF, Chambert K, Mick E, Neale BM, Fromer M, et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N Engl J Med. 2014;371:2477–87.
Article PubMed PubMed Central Google Scholar
Jaiswal S, Fontanillas P, Flannick J, Manning A, Grauman PV, Mar BG, Lindsley RC, Mermel CH, Burtt N, Chavez A, et al. Age-related clonal hematopoiesis associated with adverse outcomes. N Engl J Med. 2014;371:2488–98.
Article PubMed PubMed Central Google Scholar
Teschendorff AE, Yang Z, Wong A, Pipinikas CP, Jiao Y, Jones A, Anjum S, Hardy R, Salvesen HB, Thirlwell C, et al. Correlation of smoking-associated DNA methylation changes in buccal cells with DNA methylation changes in epithelial cancer. JAMA Oncol. 2015;1:476–85.
Article PubMed Google Scholar
Feinberg AP, Irizarry RA, Fradin D, Aryee MJ, Murakami P, Aspelund T, Eiriksdottir G, Harris TB, Launer L, Gudnason V, Fallin MD. Personalized epigenomic signatures that are stable over time and covary with body mass index. Sci Transl Med. 2010;2:49ra67.
Article PubMed PubMed Central Google Scholar
Vandiver AR, Irizarry RA, Hansen KD, Garza LA, Runarsson A, Li X, Chien AL, Wang TS, Leung SG, Kang S, Feinberg AP. Age and sun exposure-related widespread genomic blocks of hypomethylation in nonmalignant skin. Genome Biol. 2015;16:80.
Article PubMed PubMed Central Google Scholar
Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Weisenberger DJ, Shen H, Campan M, Noushmehr H, Bell CG, Maxwell AP, et al. Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res. 2010;20:440–6.
CAS Article PubMed PubMed Central Google Scholar
Rakyan VK, Down TA, Maslau S, Andrew T, Yang TP, Beyan H, Whittaker P, McCann OT, Finer S, Valdes AM, et al. Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domains. Genome Res. 2010;20:434–9.
CAS Article PubMed PubMed Central Google Scholar
Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:R115.
Article PubMed PubMed Central Google Scholar
Beerman I, Bock C, Garrison BS, Smith ZD, Gu H, Meissner A, Rossi DJ. Proliferation-dependent alterations of the DNA methylation landscape underlie hematopoietic stem cell aging. Cell Stem Cell. 2013;12:413–25.
CAS Article PubMed Google Scholar
Nejman D, Straussman R, Steinfeld I, Ruvolo M, Roberts D, Yakhini Z, Cedar H. Molecular rules governing de novo methylation in cancer. Cancer Res. 2014;74:1475–83.
CAS Article PubMed Google Scholar
Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49:359–67.
CAS Article PubMed Google Scholar
Timp W, Bravo HC, McDonald OG, Goggins M, Umbricht C, Zeiger M, Feinberg AP, Irizarry RA. Large hypomethylated blocks as a universal defining epigenetic alteration in human solid tumors. Genome Med. 2014;6(8):61.
Article PubMed PubMed Central Google Scholar
Heyn H, Li N, Ferreira HJ, Moran S, Pisano DG, Gomez A, Diez J, Sanchez-Mut JV, Setien F, Carmona FJ, et al. Distinct DNA methylomes of newborns and centenarians. Proc Natl Acad Sci U S A. 2012;109:10522–7.
CAS Article PubMed PubMed Central Google Scholar
Liu Y, Aryee MJ, Padyukov L, Fallin MD, Hesselberg E, Runarsson A, Reinius L, Acevedo N, Taub M, Ronninger M, et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol. 2013;31:142–7.
CAS Article PubMed PubMed Central Google Scholar
Jaffe AE, Irizarry RA. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol. 2014;15:R31.
Article PubMed PubMed Central Google Scholar
Reynolds LM, Taylor JR, Ding J, Lohman K, Johnson C, Siscovick D, Burke G, Post W, Shea S, Jacobs Jr DR, et al. Age-related variations in the methylome associated with gene expression in human monocytes and T cells. Nat Commun. 2014;5:5366.
Article PubMed PubMed Central Google Scholar
Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Gayther SA, Apostolidou S, Jones A, Lechner M, Beck S, Jacobs IJ, Widschwendter M. An epigenetic signature in peripheral blood predicts active ovarian cancer. PLoS One. 2009;4, e8274.
Article PubMed PubMed Central Google Scholar
Bork S, Pfister S, Witt H, Horn P, Korn B, Ho AD, Wagner W. DNA methylation pattern changes upon long-term culture and aging of human mesenchymal stromal cells. Aging Cell. 2010;9:54–63.
CAS Article PubMed PubMed Central Google Scholar
Bocker MT, Hellwig I, Breiling A, Eckstein V, Ho AD, Lyko F. Genome-wide promoter DNA methylation dynamics of human hematopoietic progenitor cells during differentiation and aging. Blood. 2011;117:e182–9.
CAS Article PubMed Google Scholar
Cancer Genome Atlas Research N, Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013;45:1113–20.
Article Google Scholar
Teixeira VH, Nadarajan P, Graham TA, Pipinikas CP, Brown JM, Falzon M, Nye E, Poulsom R, Lawrence D, Wright NA, et al. Stochastic homeostasis in human airway epithelium is achieved by neutral competition of basal cell progenitors. Elife. 2013;2, e00966.
Article PubMed PubMed Central Google Scholar
Gerstein M. ENCODE leads the way on big data. Nature. 2012;489:208.
CAS Article PubMed Google Scholar
Johnson KC, Koestler DC, Fleischer T, Chen P, Jenson EG, Marotti JD, Onega T, Kristensen VN, Christensen BC. DNA methylation in ductal carcinoma in situ related with future development of invasive breast cancer. Clin Epigenetics. 2015;7:75.
Article PubMed PubMed Central Google Scholar
Teschendorff AE, Gao Y, Jones A, Ruebner M, Beckmann MW, Wachter DL, Fasching PA, Widschwendter M. DNA methylation outliers in normal breast tissue identify field defects that are enriched in cancer. Nat Commun. 2016;7:10478.
CAS Article PubMed PubMed Central Google Scholar
Thun MJ, Carter BD, Feskanich D, Freedman ND, Prentice R, Lopez AD, Hartge P, Gapstur SM. 50-year trends in smoking-related mortality in the United States. N Engl J Med. 2013;368:351–64.
CAS Article PubMed PubMed Central Google Scholar
Jha P, Ramasundarahettige C, Landsman V, Rostron B, Thun M, Anderson RN, McAfee T, Peto R. 21st-century hazards of smoking and benefits of cessation in the United States. N Engl J Med. 2013;368:341–50.
CAS Article PubMed Google Scholar
Seifi S, Feizi F, Mehdizadeh M, Khafri S, Ahmadi B. Evaluation of cytological alterations of oral mucosa in smokers and waterpipe users. Cell J. 2014;15:302–9.
PubMed Google Scholar
Gedoz L, Lauxen Ida S, Sant'Ana MF, Rados PV. Proliferative activity in clinically healthy oral mucosa exposed to tobacco smoking and alcohol: a longitudinal study using the agNOR staining technique. Anal Quant Cytol Histol. 2007;29:231–8.
PubMed Google Scholar
Yuan T, Jiao Y, de Jong S, Ophoff RA, Beck S, Teschendorff AE. An integrative multi-scale analysis of the dynamic DNA methylation landscape in aging. PLoS Genet. 2015;11, e1004996.
Article PubMed PubMed Central Google Scholar
Martino D, Loke YJ, Gordon L, Ollikainen M, Cruickshank MN, Saffery R, Craig JM. Longitudinal, genome-scale analysis of DNA methylation in twins from birth to 18 months of age reveals rapid epigenetic change in early life and pair-specific effects of discordance. Genome Biol. 2013;14:R42.
Article PubMed PubMed Central Google Scholar
Alisch RS, Barwick BG, Chopra P, Myrick LK, Satten GA, Conneely KN, Warren ST. Age-associated DNA methylation in pediatric populations. Genome Res. 2012;22:623–32.
CAS Article PubMed PubMed Central Google Scholar
Hahn MA, Hahn T, Lee DH, Esworthy RS, Kim BW, Riggs AD, Chu FF, Pfeifer GP. Methylation of polycomb target genes in intestinal cancer is mediated by inflammation. Cancer Res. 2008;68:10280–9.
CAS Article PubMed PubMed Central Google Scholar
Smiraglia DJ, Rush LJ, Fruhwald MC, Dai Z, Held WA, Costello JF, Lang JC, Eng C, Li B, Wright FA, et al. Excessive CpG island hypermethylation in cancer cell lines versus primary human malignancies. Hum Mol Genet. 2001;10:1413–9.
CAS Article PubMed Google Scholar
Grafodatskaya D, Choufani S, Ferreira JC, Butcher DT, Lou Y, Zhao C, Scherer SW, Weksberg R. EBV transformation and cell culturing destabilizes DNA methylation in human lymphoblastoid cell lines. Genomics. 2010;95:73–83.
CAS Article PubMed Google Scholar
Fu X, Deng H, Zhao L, Li J, Zhou Y, Zhang Y. Distinct expression patterns of hedgehog ligands between cultured and primary colorectal cancers are associated with aberrant methylation of their promoters. Mol Cell Biochem. 2010;337:185–92.
CAS Article PubMed Google Scholar
Domcke S, Sinha R, Levine DA, Sander C, Schultz N. Evaluating cell lines as tumour models by comparison of genomic profiles. Nat Commun. 2013;4:2126.
Article PubMed PubMed Central Google Scholar
Mouradov D, Sloggett C, Jorissen RN, Love CG, Li S, Burgess AW, Arango D, Strausberg RL, Buchanan D, Wormald S, et al. Colorectal cancer cell lines are representative models of the main molecular subtypes of primary cancer. Cancer Res. 2014;74:3238–47.
CAS Article PubMed Google Scholar
Lin Q, Wagner W. Epigenetic aging signatures are coherently modified in cancer. PLoS Genet. 2015;11, e1005334.
Article PubMed PubMed Central Google Scholar
de Magalhaes JP, Curado J, Church GM. Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics. 2009;25:875–81.
Article PubMed PubMed Central Google Scholar
Beck S. Taking the measure of the methylome. Nat Biotechnol. 2010;28:1026–8.
CAS Article PubMed Google Scholar
Swanton C, Beck S. Epigenetic noise fuels cancer evolution. Cancer Cell. 2014;26:775–6.
CAS Article PubMed Google Scholar
Sottoriva A, Kang H, Ma Z, Graham TA, Salomon MP, Zhao J, Marjoram P, Siegmund K, Press MF, Shibata D, Curtis C. A Big Bang model of human colorectal tumor growth. Nat Genet. 2015;47:209–16.
CAS Article PubMed PubMed Central Google Scholar
Chen Y, Breeze CE, Zhen S, Beck S, Teschendorff AE. Tissue-independent and tissue-specific patterns of DNA methylation alteration in cancer. Epigenetics Chromatin. 2016;9:10.
Article PubMed PubMed Central Google Scholar
Teschendorff AE, Liu X, Caren H, Pollard SM, Beck S, Widschwendter M, Chen L. The dynamics of DNA methylation covariation patterns in carcinogenesis. PLoS Comput Biol. 2014;10, e1003709.
Article PubMed PubMed Central Google Scholar
Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, Wiencke JK, Kelsey KT. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:86.
Article PubMed PubMed Central Google Scholar
Nazor KL, Altun G, Lynch C, Tran H, Harness JV, Slavin I, Garitaonandia I, Muller FJ, Wang YC, Boscolo FS, et al. Recurrent variations in DNA methylation in human pluripotent stem cells and their differentiated derivatives. Cell Stem Cell. 2012;10:620–34.
CAS Article PubMed PubMed Central Google Scholar
Accomando WP, Wiencke JK, Houseman EA, Nelson HH, Kelsey KT. Quantitative reconstruction of leukocyte subsets using DNA methylation. Genome Biol. 2014;15:R50.
Article PubMed PubMed Central Google Scholar
Reinius LE, Acevedo N, Joerink M, Pershagen G, Dahlen SE, Greco D, Soderhall C, Scheynius A, Kere J. Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PLoS One. 2012;7, e41361.
CAS Article PubMed PubMed Central Google Scholar
Lee TI, Jenner RG, Boyer LA, Guenther MG, Levine SS, Kumar RM, Chevalier B, Johnstone SE, Cole MF, Isono K, et al. Control of developmental regulators by Polycomb in human embryonic stem cells. Cell. 2006;125:301–13.
CAS Article PubMed PubMed Central Google Scholar
Ben-Porath I, Thomson MW, Carey VJ, Ge R, Bell GW, Regev A, Weinberg RA. An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors. Nat Genet. 2008;40:499–507.
CAS Article PubMed PubMed Central Google Scholar
Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A, Chinnaiyan AM. Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci U S A. 2004;101:9309–14.
CAS Article PubMed PubMed Central Google Scholar
Yang Z, Jones A, Widschwendter M, Teschendorff AE. An integrative pan-cancer-wide analysis of epigenetic enzymes reveals universal patterns of epigenomic deregulation in cancer. Genome Biol. 2015;16:140.
Article PubMed PubMed Central Google Scholar
Turcan S, Rohle D, Goenka A, Walsh LA, Fang F, Yilmaz E, Campos C, Fabius AW, Lu C, Ward PS, et al. IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature. 2012;483:479–83.
CAS Article PubMed PubMed Central Google Scholar
Teschendorff AE, Lee SH, Jones A, Fiegl H, Kalwa M, Wagner W, Chindera K, Evans I, Dubeau L, Orjalo A, et al. HOTAIR and its surrogate DNA methylation signature indicate carboplatin resistance in ovarian cancer. Genome Med. 2015;7:108.
Article PubMed PubMed Central Google Scholar

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Funding

The authors wish to thank the Chinese Academy of Sciences, Shanghai Institute for Biological Sciences and the Max-Planck Society for financial support. This work was also supported by NSFC (National Science Foundation of China) grants, grant numbers 31571359 and 31401120, and by a Royal Society Newton Advanced Fellowship (NAF project number 522438, NAF award number 164914). The results shown here are in part based upon data generated by TCGA Research Network (http://cancergenome.nih.gov/), to which we are very grateful. This project has also received funding from the European Union’s FP7 programme under grant agreement number 316758 (Project EpiTrain), the European Union’s Horizon 2020 research and innovation programme under grant agreement number 634570, and The Eve Appeal and the Medical Research Council (MC_UU_12019/1).

Availability of data and materials

Data: The main Illumina DNA methylation datasets used here are freely available from public repositories, including GEO (http://www.ncbi.nlm.nih.gov/geo/), ArrayExpress (http://www.ebi.ac.uk/arrayexpress/) and EGA (https://www.ebi.ac.uk/ega/home) (see also “Methods” for relevant references). Details: whole blood discovery (450 k, n = 656, GEO: GSE40279); whole blood validation (450 k, n = 335, GEO: GSE42861); glioblastoma multiforme (450 k, n = 32, GEO: GSE30338); ovarian cancer (450 k, n = 215, GEO:GSE74845); cervical cancer (27 k, n = 49, GEO: GSE30759). Cell lines (450 k, n = 62, GEO:GSE40699); purified blood cells (450 k, 50 purified CD19+ B-cell samples, 49 CD4+ T-cell samples, and 52 CD14+/CD16− monocytes from a total of 52 healthy monozygotic twins, EGA: EGAS00001001598); MESA (450 k, 214 purified CD4+ T-cell and 1202 monocyte samples, GEO: GSE56046 and GSE56581). Mesenchymal stem cells (27 k, n = 8, GEO:GSE17448). Hematopoietic progenitor cells (27 k, n = 12, ArrayExpress: E-MTAB-487).

Software: An R script implementing epiTOC and the associated probe IDs of the CpGs making up epiTOC is available as Additional files 5 and 6

Authors’ contributions

Statistical analyses were done by AET. YZ contributed analyses on TCGA data. The study was conceived and designed by AET. The manuscript was written by AET. SB and MW contributed useful feedback. AW, DK, SCZ, VR, and DL contributed data. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Ethics approval and consent to participate

This study only analyses existing publicly available data.

Author information

Authors and Affiliations

CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, 320 Yue Yang Road, Shanghai, 200031, China
Zhen Yang, Shijie C. Zheng & Andrew E. Teschendorff
MRC Unit for Lifelong Health and Ageing at University College London, London, UK
Andrew Wong & Diana Kuh
Medical Genomics, UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6BT, UK
Dirk S. Paul & Stephan Beck
The Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, E1 2AT, UK
Vardhman K. Rakyan & R. David Leslie
Department of Women’s Cancer, University College London, 74 Huntley Street, London, WC1E 6AU, UK
Martin Widschwendter & Andrew E. Teschendorff
Statistical Cancer Genomics, Paul O’Gorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London, WC1E 6BT, UK
Andrew E. Teschendorff

Authors

Zhen Yang
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Andrew Wong
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Diana Kuh
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Dirk S. Paul
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Vardhman K. Rakyan
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R. David Leslie
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Shijie C. Zheng
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Martin Widschwendter
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Stephan Beck
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Andrew E. Teschendorff
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Corresponding author

Correspondence to Andrew E. Teschendorff.

Additional files

Additional file 1:

Blood reference DNA methylation data matrix in Excel format. Detailed legend in file. (XLS 85 kb)

Additional file 2:

The 385 PCGT CpGs that make up epiTOC. Detailed legend in file. (XLS 205 kb)

Additional file 3:

Supplementary information document with all supplementary figures and their legends. (PDF 1902 kb)

Additional file 4:

Gene set enrichment analysis results (in Excel table format) of the 385 PCGT CpGs that make up epiTOC. Columns label the biological term, number of genes present in the term, number and corresponding fraction of genes present on the bead array, the number in the overlap between term and the genes mapped by the 385 PCGT CpGs, the odds ratio (OR), the Fisher test one-tailed P value of enrichment, the adjusted P value (Benjamini–Hochberg adjusted) and the overlapping gene symbols. (XLS 1403 kb)

Additional file 5:

epiTOCcpgs.RData. An R object data file containing the 450 k probe IDs of the 385 PCGT CpGs that make up epiTOC. (RDATA 1 kb)

Additional file 6:

EstEpiTOC.R. An executable R script function to allow estimation of the pcgtAge score in independent samples. (R 1 kb)

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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Yang, Z., Wong, A., Kuh, D. et al. Correlation of an epigenetic mitotic clock with cancer risk. Genome Biol 17, 205 (2016). https://doi.org/10.1186/s13059-016-1064-3

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Published: 03 October 2016
DOI: https://doi.org/10.1186/s13059-016-1064-3

Keywords

DNA methylation
Epigenetic clock
Cancer
Mitotic
Stem cells
Ageing

Correlation of an epigenetic mitotic clock with cancer risk

Abstract

Background

Results

Conclusions

Background

Results

Construction of the epiTOC model

epiTOC correlates with age in purified cell and stem cell populations

epiTOC approximates a mitotic clock

epiTOC predicts universal age acceleration in cancer and is further increased in cancer cell lines

Increased epiTOC tick rate in pre-invasive cancer lesions

Increased epiTOC tickrate in normal buccal tissue of smokers

Discussion

Conclusions

Methods

Construction of an epigenetic mitotic clock (epiTOC): a mathematical model

Construction of an epigenetic mitotic-like clock: CpG selection

Validation of the age-correlative models in blood and other normal tissue types

Validation of the epigenetic mitotic clock in normal tissue

Validation of the epigenetic mitotic clock in cancer tissue: construction of an mRNA expression based-mitotic index

Cancer and pre-cancerous Illumina 450 k datasets

Buccal tissue Illumina 450 k set

Non-TCGA cancer tissue and ENCODE cell line DNAm datasets

Purified T-cell, B-cell and monocyte Illumina 450 k sets

Stem cell Illumina DNAm sets

Software availability

Abbreviations

References

Funding

Availability of data and materials

Authors’ contributions

Competing interests

Ethics approval and consent to participate

Author information

Authors and Affiliations

Corresponding author

Additional files

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About this article

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Keywords

Genome Biology

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