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Epigenetics. 2012 Aug 1; 7(8): 858–867.
PMCID: PMC3427281

DNA methylation analyses of urothelial carcinoma reveal distinct epigenetic subtypes and an association between gene copy number and methylation status


We assessed DNA methylation and copy number status of 27,000 CpGs in 149 urothelial carcinomas and integrated the findings with gene expression and mutation data. Methylation was associated with gene expression for 1,332 CpGs, of which 26% showed positive correlation with expression, i.e., high methylation and high gene expression levels. These positively correlated CpGs were part of specific transcription factor binding sites, such as sites for MYC and CREBP1, or located in gene bodies. Furthermore, we found genes with copy number gains, low expression and high methylation levels, revealing an association between methylation and copy number levels. This phenomenon was typically observed for developmental genes, such as HOX genes, and tumor suppressor genes. In contrast, we also identified genes with copy number gains, high expression and low methylation levels. This was for instance observed for some keratin genes. Tumor cases could be grouped into four subgroups, termed epitypes, by their DNA methylation profiles. One epitype was influenced by the presence of infiltrating immune cells, two epitypes were mainly composed of non-muscle invasive tumors, and the remaining epitype of muscle invasive tumors. The polycomb complex protein EZH2 that blocks differentiation in embryonic stem cells showed increased expression both at the mRNA and protein levels in the muscle invasive epitype, together with methylation of polycomb target genes and HOX genes. Our data highlights HOX gene silencing and EZH2 expression as mechanisms to promote a more undifferentiated and aggressive state in UC.

Keywords: DNA Methylation, Polycomb, bladder cancer, copy number changes, gene expression, molecular subtypes, transcription factor


Urothelial carcinoma (UC) of the bladder is one of the most common cancer forms in the Western world and has one of the highest lifetime costs per patient.1 The major clinical distinction is between the non-muscle invasive Ta and T1 and the muscle invasive (MI) ≥ T2 tumors. A significant effort to characterize bladder cancer at the molecular level has been undertaken during recent years. Mutations in FGFR3, PIK3CA and TP53 are frequently observed2 and several recurrent copy number aberrations have been identified including frequent losses of chromosome arms 2q, 8p, 9p and 9q, as well as gains of 1q and 6p.3 In addition, gene expression profiling has greatly contributed to our understanding of bladder tumor biology4-6 and gene expression analysis in combination with array-CGH and gene mutation analyses has revealed the presence of two distinct molecular subtypes of urothelial carcinoma.7 Epigenetic changes by DNA methylation at CG dinucleotide sites (CpGs) are frequent events in tumor development.8,9 Initially, DNA methylation studies were limited to 5′ promoter regions of a small number of genes.10 Microarray technology has recently allowed investigating the cancer methylome at a larger scale and made it possible to study DNA methylation from a genomic perspective.11 Apart from revealing specific epigenetic tumor subtypes with distinct DNA methylation signatures in, e.g., breast cancer,12-14 colon cancer15 and hematological neoplasms,16 DNA methylation has also been shown to exhibit recurring local topologies.17,18 High-throughput DNA methylation analyses have also made it possible to identify promising epigenetic biomarkers.10,19-21 So far, most differential DNA methylation has been attributed to genes that are essential for developmental processes, often Polycomb Repressive Complex 2 (PRC2) regulated genes.22 The present study aims to identify stable tumor subgroups based on DNA methylation, and to refine our understanding of bladder cancer by integrating the findings with gene mutations, copy number alterations, and gene expression data.


DNA methylation in bladder cancer

Characteristics of DNA methylation in UC

We measured the methylation status of individual CpGs of 149 tumors using β-values that range from 0 (unmethylated) to 1 (fully methylated). In tumors, CpG-Island CpGs (Island CpGs) showed significantly lower methylation (β-values) levels than non-CpG-Island CpGs (non-Island CpGs). Non-Island CpGs showed high β-value variation across the data (Fig. S1) and several regions with hyper-variable DNA methylation, including the HOXA, HOXB and HOXD gene clusters, as well as CDH8 and CDH11, were identified (Fig. S2). Hyper-variable regions were enriched for membrane-related genes, e.g., the GO term ‘integral to plasma membrane’ (fdr = 0.01) when excluding HOX genes from the analysis.

For comparison between tumor and normal samples (n = 6) we used an average difference in β-value ∆β ≥ 0.25 and ∆β ≤ -0.25 as thresholds. A larger fraction was found to be hypomethylated than hypermethylated in tumors, 1,567 and 386 CpGs, respectively (Table S1). Of 1,567 hypomethylated CpGs, 1,161 (75%) were non-Island CpGs and of 386 hypermethylated CpGs, 367 (95%) were Island CpGs, a significant enrichment (p < 2x10−16, Fisher test) in the respective class of methylation states. Hence, CpGs hypomethylated in tumors were preferentially located in CpG poor regions, whereas hypermethylated CpGs were preferentially located in CpG rich regions (Fig. 1). The differentially methylated genes identified in the comparison showed a large overlap with previously published data using the same platform.21 Hypomethylated CpGs were enriched for immune-related genes (GO term analysis, fdr < 10−7); hypermethylated CpGs showed strong enrichment for developmental process (fdr < 10−11) and homeobox-containing genes (INTERPRO, fdr < 10−12).

figure epi-7-858-g1
Figure 1. Hypo- and hypermethylated CpGs in tumors. Hypomethylation, blue; hypermethylation, yellow. Vertical blue and yellow lines indicate median CpG density of hypo- and hypermethylated CpGs, respectively. X-axis, CpG density defined as fraction ...

DNA methylation and gene expression

Positively correlated CpGs show enrichment of specific transcription factor motifs

A total of 1,332 CpGs (8.4%) showed significant correlation between methylation and gene expression (fdr < 0.05, Spearman correlation) (Table S1). Of the 1,332 CpGs, 45% were non-Island CpGs, a significant overrepresentation of non-Island CpGs (p < 1x10−16) considering the proportions in the assessed CpGs. Among the CpGs correlated with gene expression, methylation was associated with reduced gene expression for 981/1,332 CpGs (74%). Genes with a strong negative association included members of the HOX genes and cytokeratins, such as HOXB2 (r = -0.70), KRT20 (r = -0.71) and KRT13 (r = -0.69). Notably, a substantial number of CpGs show positive correlation between methylation and gene expression. This group of 351 CpGs was enriched for genes located to the extracellular region (fdr = 3x10−10), e.g., COL18A1 (r = 0.52) and COL8A2 (r = 0.51), and involved in defense response (fdr = 4x10−7), e.g., CCL2 (r = 0.51) and LILRB3 (r = 0.51). CpGs associated with increased expression showed a trend (p < 0.025, Fisher exact test) to be located in non-Island CpGs when compared with negatively associated CpGs. To investigate whether the observed CpGs were located within a specific sequence context, we performed a search for motives overlapping the CpG using the MEME algorithm. This analysis identified a highly enriched motif with E = 2x10−20, compared with E = 10−4 obtained in a random sample of the same size, with the consensus sequence ACGTGATA (Fig. S3). Further analyses revealed significant enrichment for several specific transcription factor binding sites covering the methylated CpGs (Table 1), most of which were located in promoter regions (Fig. 2). Binding sites for E2F (E2F1) were, on the other hand, significantly under-represented (fdr < 6 × 10−5). By re-annotating all CpGs on the platform into promoter and gene body CpGs we observed a significant enrichment (p < 10−8) for CpGs located within gene bodies among the positively correlated CpGs. No transcription factor binding site enrichment was observed among the CpGs showing negative association with methylation. Hence, methylation of CpGs located within both gene bodies and transcription factor binding sites is associated with increased gene expression.

Table thumbnail
Table 1. Enriched TF motifs covering CpGs with positive correlation to gene expression
figure epi-7-858-g2
Figure 2. Transcription factor binding motifs contain CpGs that show a positive association between methylation and gene expression levels. UCSC genome browser view of MYCMAX_01/02 and CREBP1_01/Q2 transcription factor motifs. Each panel contains ...

DNA methylation and copy number alterations

We sought to investigate the correlation between gene copy changes and methylation status for individual CpGs. We developed a method to obtain copy number information from the Illumina Methylation27K BeadChip array (see Methods and Supplemental Material). We validated the obtained copy number profiles using array CGH data, which was available for 68 of the 149 cases. Copy number data derived from the Illumina platform was highly similar to array CGH data (Supplemental Material).

DNA methylation levels are associated with copy number changes

With concurrent information of DNA methylation and copy number for each CpG the relationship between methylation and copy number levels could be analyzed in detail. To find robust associations we limited the investigation to CpGs that show variation in methylation levels (variance > 0.02; standard deviation > 0.14). The CpGs were divided into frequently deleted and frequently gained sites. The observed correlations were then compared with correlations obtained by reshuffling of the genomic positions. For deleted CpGs no significant correlations were observed whereas 103 gained CpGs showed significant correlation between methylation and gene copy status: 77 positively correlated and 26 negatively correlated (Table S1). The positively correlated events were seen in tumor suppressor genes, e.g., RASSF1 and MLH1, and developmental genes, e.g., HOX and MSX1. Almost all positively correlated CpGs were located in CpG Islands (73/77, p < 10−16, Fisher’s exact test). Typically, CpGs with positive correlation were unmethylated when present in normal copy numbers and showed increased methylation in tumors with gains, as demonstrated by, e.g., RASSF1 and HOXB4 (Fig. 3A). The negatively correlated events were enriched for keratin genes (fdr = 0.003), with no preference regarding Island or non-Island CpGs (p = 0.56). We then set out to determine if the correlated CpGs have an impact on gene expression. As seen in Figure 3B, cases with concomitant gain and demethylation of KRT20 are among the high expressers, demonstrating a combined effect of copy number gain and demethylation on gene expression. Compared with gained sites not associated with methylation, the correlation of copy number with gene expression was statistically higher for genes that showed demethylation and gains (p = 0.028, t-test). The correlation with gene expression was lower for genes showing methylation and gains compared with non-associated sites (p = 0.001, t-test; Figure 3C), i.e., the expression of genes with this methylation pattern is associated to gene copy numbers to a lesser extent.

figure epi-7-858-g3
Figure 3. Methylation of CpG sites is associated to copy number levels. (A) Methylation in cases with gains. RASSF1 (two CpGs), ZMYND10, MSX1, TERT, and HOXB4 CpG sites show methylation in samples with gains. X-axis, DNA methylation as β-values. ...

Copy number changes have a larger impact on gene expression than DNA methylation levels

In contrast to the low fraction of genes influenced by CpG methylation (8.4%), 44% of the same genes showed significant correlation between copy numbers and gene expression using the same criteria for significance. The association was positive in > 99% of cases, i.e., upregulation was seen when genes were gained and downregulation when deleted. Thus, our data indicate that the tumor phenotype (i.e., the gene expression profile) is to a larger extent affected by gene copy numbers than by gene promoter CpG methylation.

Epigenetic subtypes of bladder cancer

We used hierarchical clustering analyses (HCA) of bootstrapped data sets based on the CpGs that showed highest variation in DNA methylation levels (Supplemental Material) to identify four major subtypes of bladder cancer: Epitypes A, B, C, and D (Fig. 4; Supplemental Material). The available data indicated a possible further division of Epitypes A and C, A1 and A2, and C1 and C2. We tested the robustness of the results by repeating the procedure with and without background-correction, using the 15% top varying CpGs, resulting in highly similar epitypes. The robustness was further confirmed using M-values, i.e., converted β-values.23

figure epi-7-858-g4
Figure 4. Epitypes in their genomic and histopathologcial context. (A) Heat map of 25% most varying CpGs. Left bar, green, Island CpGs; gray, non-Island CpGs. Heat map color code, blue, low methylation levels to yellow, high methylation levels. ...

Biological context of epitypes

Non-Island CpGs were overrepresented among the CpGs that defined epitypes (Fig. 4A). However, some epitype splits were dependent on Island/non-Island categories (Table S2), e.g., the splits between A1 and A2, and C1 and C2. Gene Ontology analysis assigned biological processes to all epitypes, supporting their biological relevance (Table S2). Both gene expression data and pathological evaluation showed that Epitype D is infiltrated by immune cells. The average tumor cell content of Epitype D is low (66%) compared with Epitypes A/B/C (81%, p = 4 × 10−6), which is mainly attributed to the presence of cells positively stained for CD3 (T-cells), CD68 (myeloid cells), and α smooth muscle actin (fibroblasts) (Table S3). In particular, the CD3 data indicates a stronger presence of T-cells in Epitype D (p = 2x10−7). Furthermore, a multidimensional scaling analysis of various tissue types revealed Epitype D to show high similarity to blood leukocytes (Fig. S4A). A common methylation pattern among cell types from external studies, such as B-cells, T-cells, monocytes, mesenchymal cells and embryonic stem cells, can be observed (Fig. S5). This pattern is to some extent evident in Epitype D, but not in Epitypes A/B/C (Fig. S5). Epitype D shows demethylation of immune gene CpGs. Notably, the age-related methylation signature defined in blood cells24 only shows correlation with age within Epitype D (r = 0.4, p = 0.008, Fig. S4B). This further suggests that Epitype D is infiltrated by hematological cells.

A subsequent MDS analysis revealed Epitype B to differ the most from normal urothelial samples (Fig. 5). The epitypes differ with respect to stage, with Epitypes A and B dominated by non-muscle invasive (NMI) tumors and C and D by muscle-invasive (MI) tumors (p = 2 × 10−4). With respect to pathological grade, A and B were of lower grade (G1 and G2) and C and D of higher grade (p = 2 × 10−5) (Fig. 4B). Epitypes also differ in disease specific survival as determined by Kaplan-Meier analysis (p = 0.035, logrank test), with Epitype A having good prognosis, B intermediate, and C poor prognosis (Fig. S6). Epitype D was left out from the analysis as the tumor-infiltrating leukocytes/lymphocytes dominated the epitype. At the genomic level, FGFR3 mutations were more frequent in Epitypes A and B (p = 2 × 10−7). Loss of 9q was enriched in Epitypes A and B (p = 5 × 10−4) whereas amplification of 6p and the number of focal genomic amplifications (FGA), an indicator of genomic instability, were more frequent in Epitype C (p = 2 × 10−3 and p = 3 × 10−7, respectively) (Fig. 4B). TP53 and PIK3CA mutation frequencies did not differ significantly between epitypes. Epitypes A and B were strongly associated to molecular subtype 1 (MS1), and Epitype C and D to molecular subtype 2 (MS2) (p < 10−13), as defined by Lindgren et al.7 (Fig. 4B). Hence, the identified UC epitypes are associated with specific genomic and gene alterations, as well as gene expression subtypes, known to be important for UC classification and development.

figure epi-7-858-g5
Figure 5. MDS analysis. A two-dimensional MDS representation of the similarities among tumors. Green, Epitype A samples; blue, Epitype B samples; red, Epitype C samples; black, normal urothelium. Epitype D samples were omitted from the analysis. ...

EZH2 expression and HOX gene methylation in aggressive tumors

Expression of the Polycomb gene EZH2, the catalytic subunit of the PRC2 complex that keeps embryonic stem cells in an undifferentiated state,25 was assessed both by gene expression and by IHC (Supplemental Material). High EZH2 expression was observed in Epitype C and associated with methylation of PRC2 target genes identified in embryonic stem cells25 (r = 0.43, p = 5 × 10−8) (Fig. 4C; Fig. S7). EZH2 expression was also correlated with methylation of the remaining genes not targeted by PRC2 (r = 0.37, p = 4 × 10−6); however, PRC2 target methylation showed additional association with EZH2 expression in a multivariate analysis (p = 0.004). PRC2 target methylation overlaps with methylation of HOX gene family members and with genes defined by the GO term ‘developmental processes’, all of them methylated in Epitype C (Fig. 4C). These findings suggest that developmental processes are repressed by DNA methylation in Epitype C, partly regulated by EZH2. In contrast, MBD2, a gene involved in maintaining an unmethylated state, is expressed at higher levels in Epitype A (p = 0.002) (Fig. 4C), in line with the observation that Epitype A shows lower levels of methylation and Epitype C shows higher levels of methylation.


We performed DNA methylation profiling of urothelial carcinomas with the major aim to detect DNA methylation subtypes, i.e., UC epitypes. The cases were selected from a larger cohort for which gene expression and mutation data, as well as array CGH data for a subset, were available. As array CGH was not available for the whole set we devised a method to obtain relative CpG copy numbers from Illumina’s Methylation27K BeadChip platform. We further observed that copy number alterations modified the β-values by shifting the proportion of true signal to background signal (Supplemental Material). This produces β-value changes and spurious correlations of the β-value to copy number and, consequently, to gene expression, as there is a general correlation between gene copy number and gene expression.26 We corrected for this by subtracting the background signal for each individual probe. In uncorrected data, 13.1% of CpGs were correlated with expression. Background correction resulted in a reduction of associated CpGs to 8.4%. Hence, not properly corrected β-values in data obtained from tumors with genomic alterations may result in overoptimistic correlations.

After correction, we observed biologically meaningful associations between gene copy numbers and methylation states for specific CpGs. In genomic regions frequently gained in UC, we detected CpGs preferentially demethylated or methylated. The biological outcome of these two processes is that gene expression of developmental genes is less sensitive to copy number gains, whereas DNA demethylation for some keratin genes results in increased correlation between gene copy numbers and expression. The extent of these associations may be underestimated in the present data as the applied platform only contains a limited number of CpGs for each gene. Further studies are needed to determine whether the methylation change occurs at the gained allele and whether the change in DNA methylation or the gain in copy number occurs first. Irrespectively, the findings emphasize the dynamic character of DNA methylation and point to a process in which parts of the methylome may interact with changes in the cancer genome.

As expected, when significant association between CpG methylation and expression was seen, the associations were negative, i.e., gene downregulation when methylated, in the majority of the cases. However, for a substantial fraction of the cases CpG methylation was coupled with increased gene expression. Positively associated CpGs were predominantly located in CpG-poor promoters as well as in gene bodies. The association of gene body methylation with transcription is consistent with previous reports.27 In addition, the positively associated CpGs were enriched for being part of specific transcription factor binding sites, of which MYC/MAX/EBOX-related, USF, and CREBBP1 binding sites were among the most prominent. Specific CpG methylation has been found to affect the binding of the cognate transcriptions factors, e.g., MYC/MAX and MAD/MAX,28 NMYC,29 USF,30,31 CREB32 and HIF1.33 How this may induce increased expression is far from clear. The simplest mechanism would be that motif methylation inhibits the binding of a transcriptional repressor.10 In this context, it is notable that MYC may act as a transcriptional repressor by recruiting DNA methyltransferases as co-repressors.34 It is thus conceivable that inactivation of MYC binding could prevent promoter methylation and gene silencing for a subset of genes. In case of the CREB-binding site, it has been shown that the methylated version is bound by C/EBPα whereas the unmethylated version is bound by the CREB protein, resulting in tissue specific gene expression programs.32 Hence, the positive association between CpG methylation and gene expression may be the outcome of several different mechanisms, of which modification of transcription factor binding sites is one. The scale of this effect, in terms of number of affected genes, may at present not be fully appreciated.

We used a bootstrap approach to arrive at four robust Epitypes: A, B, C, and D. Non-Island CpGs were enriched among the epitype-discriminating CpGs, reminiscent of what is seen for human normal tissues that are predominantly discriminated by CpGs on CpG Island shores.35 Epitype D tumors showed a methylation pattern similar to immune cells and were infiltrated by immune cells. On the other hand, non-tumoral cell content is of minor relevance for Epitypes A/B/C. Consequently, Epitype D was not considered a true epitype of UC cells. The epitypes adhere to the two major molecular subtypes of UC.7 Apart from showing distinct methylation patterns, the epitypes differed at the genomic level for alterations common in UC: Epitypes A and B showed frequent FGFR3 mutations and losses of 9q whereas Epitype C showed signs of genomic instability and frequent 6p amplifications. The expression of EZH2, a regulator of polycomb targets and a major inhibitor of differentiation, showed increased expression in Epitype C, as determined both by mRNA and protein expression. EZH2 expression was strongly associated with DNA methylation of polycomb target genes, PRC2 targets, and with genes described by the GO term “development.” This suggests that an epigenetic program has been activated, or preserved, that promotes a more undifferentiated state in Epitype C cells. This is line with the finding that Epitype C is associated with bad prognosis.

The presence of possible urothelial cancer epitypes has been suggested in previous investigations. Wolff et al. associated specific DNA methylation profiles to bladder cancer tumor stage.19 On the same platform Wilhelm-Bernartzi et al. could delineate four DNA methylation subtypes, of which two were associated with invasive cases.20 However, these investigations were limited to a small number of CpG positions by the use of the Illumina Golden Gate platform. Also, the results of Kandimalla et al., using a CpG Island based platform, point to the possible presence of several DNA methylation subtypes.36 Our investigation differs from the previous ones in that: first, we used a more comprehensive CpG platform that includes both Island and non-Island CpGs; second, we set out to define DNA methylation subtypes, epitypes, independent of pathological classification and; third, we integrate the epigenetic data with gene expression and gene copy number data. Both Wolff et al. and Kandimalla et al. show that genes methylated in tumor samples compared with normal samples are predominantly polycomb target genes. We were able to expand these results and show that, in fact, polycomb target methylation is a feature of the most aggressive epitype.

The importance of DNA methylation for HOX gene regulation became obvious from several of the analyses performed. The HOXA, -B, and –D clusters were among the hyper-variable regions detected in our data. The same regions have been described by Vallot et al.18 as regions with coordinated gene expression in absence of genomic alterations, regulated by chromatin modification. The distinct behavior of the three HOX gene families emphasizes their importance in bladder cancer development. The link between Epitype C and cellular differentiation was also seen in the behavior of several HOX genes that showed increased methylation associated with decreased gene expression. Furthermore, members from these HOX gene clusters showed an on-state in Epitypes A and B, and an off-state in Epitype C. The downregulation of HOX genes in Epitype C implies that HOX gene expression may suppress aggressive growth. HOXB2 has, in line with this, been reported to be hypermethylated in aggressive UC tumors.37 A similar association between HOX gene methylation inactivation and aggressive growth has been reported for breast cancer,38 ovarian cancer39 and leukemias.40 A further indication of the suggested tumor suppressor like activity of the HOX genes was the observed methylation of HOXB4, -B5 and -B8 genes when gained. Notably, HOX genes are repressed in pluripotent cells and activated upon differentiation.41 Repression of HOX genes in Epitype C may thus facilitate tumor evolution toward a more stem cell-like behavior. Taken together, our findings highlight HOX gene silencing and EZH2 expression as important mechanisms to maintain UC in an undifferentiated and more aggressive state.

In conclusion, we have highlighted two aspects of DNA methylation. First, the association between gene copy numbers and DNA methylation and, second, the possible activation of genes through site specific methylation of transcription factor bindings sites. These findings emphasize the dynamic behavior of the cancer methylome. In addition, we show that UC consists of distinct DNA methylation subtypes, epitypes, that are related to pathological staging and grading as well as molecular subtypes.7 We believe that detailed understanding of the UC methylome dynamics and epitypes combined with data on the transcriptomic and genomic levels will enable the precise classification of UC into tumor subtypes of importance for clinical behavior and treatment response.

Materials and Methods


We assayed 149 tumors from 149 patients, 53 Ta, 45 T1 and 51 MI tumors, for genome-wide methylation analysis using the Illumina Infinium HumanMethylation27K BeadChip. As controls, we used 3 technical replicates of one tumor sample and 6 samples from normal bladder urothelium without bladder cancer history. Informed consent was obtained from all patients; the study has been approved by the local ethics committee. Tumor cell content for 133 of 149 samples was obtained from adjacent 1mm tissue microarray cores. The proportion of tumor cells for Epitypes A, B, C and D is on average 85%, 85%, 76% and 66% respectively (Table S3). For details on sample preparation and hybridization see Supplemental Material. Raw intensities for methylated and unmethylated probes of each CpG site were extracted from Illumina’s GenomeStudio, available at the Gene Expression Omnibus (GSE33510). Genome wide expression data were available for all samples (GSE32894). Array CGH data were available for 68 tumor samples (GSE32549).

Preprocessing of methylation data

In line with a previous report, we find β-values of low-varying CpGs to be influenced by copy numbers (Supplemental Material).42 This is, however, caused by probe-specific background rather than Illumina’s constant offset of 100 as suggested previously,42 and has an impact on β-values on a large scale. Consequently, we aimed to subtract the probe-specific background. Probe-specific background was estimated using the unmethylated channel from hybridizations of SssI methylated control DNAs, and then subtracted from the data to obtain M' (methylated) and U' (unmethylated) signals (Supplemental Material). Values below zero were set to zero and 2,524 sites with (mean (M'+U') < 3,000 were removed from the data. Beta values were calculated as β = M'/(M'+U'), not using Illumina’s offset. Missing values were imputed using k-nearest neighbor imputation (k = 10).43 Batch effects induced by bisulfite-treatment were adjusted (Supplemental Material). CpGs located in chromosomes X and Y were removed from the subsequent analyses to obtain the final β-value matrix. For the definition of epitypes, the top 25% CpGs in β-value variation across tumors were used, and for integration with copy number data, CpGs with β-value variance > 0.02 were considered. Chromosome X and Y CpGs were included for copy number analysis.

Copy number data from Methylation27K BeadChip

The overall intensity for each CpG site reflects the copy number level described by I = U+M. To account for dye specific signal intensity the data was quantile-normalized with respect to the dye-channels.44 The sites were calibrated using samples with normal copy numbers (Supplemental Material) and then adjusted for batch effects (Supplemental Material). For each sample the data was segmented using the CBS algorithm,45 and a sample-adaptive threshold (SAT) was calculated to obtain a sample-specific cut-off for calling of amplifications and deletions.46

Definition of CpG Islands, hyper-variable regions and epitypes

We used Illumina’s CpG Island definition that is based on NCBIs relaxed Island definition.47 For identification of hyper-variable regions only the CpG with highest β-value variation for each gene was included. To call significant hyper-variable regions, we calculated the 0.995 and 0.005 quantiles for each chromosome, obtained from smoothing values of 500 data sets with reshuffled genome positions; i.e., values above and below these quantiles are of p value < 0.01. The epitypes were identified using hierarchical clustering analyses (HCA) of 2000 bootstrapped data sets as previously described by Lindgren et al.7 (Supplemental Material).

Gene signatures

CpGs with age-related methylation changes were obtained from Teschendorff et al.24 PRC2 target gene signature was obtained from Lee et al.25 Genes with GO term “developmental processes” were retrieved48 and genes with the GO term “cell cycle” removed prior to analysis.

Identification of transcription factor binding sites

Correlation analysis of methylation to gene expression levels was compared with permuted data sets to obtain false discovery rates (Supplemental Material). We used MEME for motif discovery in positively correlated CpGs, with the respective CpG sites +/− 5 bp as input (MEME settings 4 to 8 bp motif).49 To further characterize the genomic CpG context we re-annotated the CpGs in respect to promoters (transcription start site -1,500 to +500) and gene bodies (transcriptions start site +501 to transcription termination site -500). We used the SMART algorithm to characterize transcription factor binding sites overlapping the CpGs.50 We extracted the flanking genomic sequence (+/−25 bp) for each CpG and analyzed both strands for the presence of high scoring transcription factor (TF) binding motifs in at least one strand (SMART settings CSS = 1.0, MSS ≥ 0.9). We filtered the hits in order to identify core motifs overlapping the Illumina CpG-site by at least one base. In this analysis no distinctions were made between different position weight matrices for the same transcription factor, e.g., MYC_01, MYC_02, and MYC_03. As background list for binding site enrichment analysis we employed the genes used for correlation analysis between methylation and expression levels.

Supplementary Material

Additional material

Additional material

Additional material

Additional material


MH would like to thank participants in the Epigenetics – medical, nutritional, social, legal and ethical consequences theme organized by the Pufendorf Institute for Advanced Studies in Lund for valuable discussions before conducting this investigation. This work was supported by The Swedish Research Council, The Swedish Cancer Foundation, The Gunnar Nilsson foundation, BioCARE, and The Maja and Hjalmar Leanders foundation.



fdrfalse discovery rate
FGAfocal genomic amplifications
GOGene Ontology
HCAhierarchical cluster analysis
MSmolecular subtype
NMInon-muscle invasive, PRC2, Polycomb Repressive Complex 2
SATsample adaptive threshold
TFtranscription factor
UCurothelial carcinoma

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest are disclosed.


1. Avritscher EBC, Cooksley CD, Grossman HB, Sabichi AL, Hamblin L, Dinney CP, et al. Clinical model of lifetime cost of treating bladder cancer and associated complications. Urology. 2006;68:549–53. doi: 10.1016/j.urology.2006.03.062. [PubMed] [Cross Ref]
2. Sjödahl G, Lauss M, Gudjonsson S, Liedberg F, Halldén C, Chebil G, et al. A systematic study of gene mutations in urothelial carcinoma; inactivating mutations in TSC2 and PIK3R1. PLoS One. 2011;6:e18583. doi: 10.1371/journal.pone.0018583. [PMC free article] [PubMed] [Cross Ref]
3. Blaveri E, Brewer JL, Roydasgupta R, Fridlyand J, DeVries S, Koppie T, et al. Bladder cancer stage and outcome by array-based comparative genomic hybridization. Clin Cancer Res. 2005;11:7012–22. doi: 10.1158/1078-0432.CCR-05-0177. [PubMed] [Cross Ref]
4. Blaveri E, Simko JP, Korkola JE, Brewer JL, Baehner F, Mehta K, et al. Bladder cancer outcome and subtype classification by gene expression. Clin Cancer Res. 2005;11:4044–55. doi: 10.1158/1078-0432.CCR-04-2409. [PubMed] [Cross Ref]
5. Sanchez-Carbayo M, Socci ND, Lozano J, Saint F, Cordon-Cardo C. Defining molecular profiles of poor outcome in patients with invasive bladder cancer using oligonucleotide microarrays. J Clin Oncol. 2006;24:778–89. doi: 10.1200/JCO.2005.03.2375. [PubMed] [Cross Ref]
6. Dyrskjøt L, Zieger K, Real FX, Malats N, Carrato A, Hurst C, et al. Gene expression signatures predict outcome in non-muscle-invasive bladder carcinoma: a multicenter validation study. Clin Cancer Res. 2007;13:3545–51. doi: 10.1158/1078-0432.CCR-06-2940. [PubMed] [Cross Ref]
7. Lindgren D, Frigyesi A, Gudjonsson S, Sjödahl G, Hallden C, Chebil G, et al. Combined gene expression and genomic profiling define two intrinsic molecular subtypes of urothelial carcinoma and gene signatures for molecular grading and outcome. Cancer Res. 2010;70:3463–72. doi: 10.1158/0008-5472.CAN-09-4213. [PubMed] [Cross Ref]
8. Esteller M. Epigenetic gene silencing in cancer: the DNA hypermethylome. Hum Mol Genet. 2007;16(Spec No 1):R50–9. doi: 10.1093/hmg/ddm018. [PubMed] [Cross Ref]
9. Hansen KD, Timp W, Bravo HC, Sabunciyan S, Langmead B, McDonald OG, et al. Increased methylation variation in epigenetic domains across cancer types. Nat Genet. 2011;43:768–75. doi: 10.1038/ng.865. [PMC free article] [PubMed] [Cross Ref]
10. Veerla S, Panagopoulos I, Jin Y, Lindgren D, Höglund M. Promoter analysis of epigenetically controlled genes in bladder cancer. Genes Chromosomes Cancer. 2008;47:368–78. doi: 10.1002/gcc.20542. [PubMed] [Cross Ref]
11. Weber M, Hellmann I, Stadler MB, Ramos L, Pääbo S, Rebhan M, et al. Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nat Genet. 2007;39:457–66. doi: 10.1038/ng1990. [PubMed] [Cross Ref]
12. Holm K, Hegardt C, Staaf J, Vallon-Christersson J, Jönsson G, Olsson H, et al. Molecular subtypes of breast cancer are associated with characteristic DNA methylation patterns. Breast Cancer Res. 2010;12:R36. doi: 10.1186/bcr2590. [PMC free article] [PubMed] [Cross Ref]
13. Van der Auwera I, Yu W, Suo L, Van Neste L, van Dam P, Van Marck EA, et al. Array-based DNA methylation profiling for breast cancer subtype discrimination. PLoS One. 2010;5:e12616. doi: 10.1371/journal.pone.0012616. [PMC free article] [PubMed] [Cross Ref]
14. Fang F, Turcan S, Rimner A, Kaufman A, Giri D, Morris LGT, et al. Breast cancer methylomes establish an epigenomic foundation for metastasis. Sci Transl Med. 2011;3:75ra25. doi: 10.1126/scitranslmed.3001875. [PMC free article] [PubMed] [Cross Ref]
15. Hinoue T, Weisenberger DJ, Lange CPE, Shen H, Byun H-M, Van Den Berg D, et al. Genome-scale analysis of aberrant DNA methylation in colorectal cancer. Genome Res. 2012;22:271–82. doi: 10.1101/gr.117523.110. [PMC free article] [PubMed] [Cross Ref]
16. Martin-Subero JI, Ammerpohl O, Bibikova M, Wickham-Garcia E, Agirre X, Alvarez S, et al. A comprehensive microarray-based DNA methylation study of 367 hematological neoplasms. PLoS One. 2009;4:e6986. doi: 10.1371/journal.pone.0006986. [PMC free article] [PubMed] [Cross Ref]
17. Stransky N, Vallot C, Reyal F, Bernard-Pierrot I, de Medina SGD, Segraves R, et al. Regional copy number-independent deregulation of transcription in cancer. Nat Genet. 2006;38:1386–96. doi: 10.1038/ng1923. [PubMed] [Cross Ref]
18. Vallot C, Stransky N, Bernard-Pierrot I, Hérault A, Zucman-Rossi J, Chapeaublanc E, et al. A novel epigenetic phenotype associated with the most aggressive pathway of bladder tumor progression. J Natl Cancer Inst. 2011;103:47–60. doi: 10.1093/jnci/djq470. [PMC free article] [PubMed] [Cross Ref]
19. Wolff EM, Chihara Y, Pan F, Weisenberger DJ, Siegmund KD, Sugano K, et al. Unique DNA methylation patterns distinguish noninvasive and invasive urothelial cancers and establish an epigenetic field defect in premalignant tissue. Cancer Res. 2010;70:8169–78. doi: 10.1158/0008-5472.CAN-10-1335. [PMC free article] [PubMed] [Cross Ref]
20. Wilhelm-Benartzi CS, Koestler DC, Houseman EA, Christensen BC, Wiencke JK, Schned AR, et al. DNA methylation profiles delineate etiologic heterogeneity and clinically important subgroups of bladder cancer. Carcinogenesis. 2010;31:1972–6. doi: 10.1093/carcin/bgq178. [PMC free article] [PubMed] [Cross Ref]
21. Reinert T, Modin C, Castano FM, Lamy P, Wojdacz TK, Hansen LL, et al. Comprehensive genome methylation analysis in bladder cancer: identification and validation of novel methylated genes and application of these as urinary tumor markers. Clin Cancer Res. 2011;17:5582–92. doi: 10.1158/1078-0432.CCR-10-2659. [PubMed] [Cross Ref]
22. Schlesinger Y, Straussman R, Keshet I, Farkash S, Hecht M, Zimmerman J, et al. Polycomb-mediated methylation on Lys27 of histone H3 pre-marks genes for de novo methylation in cancer. Nat Genet. 2007;39:232–6. doi: 10.1038/ng1950. [PubMed] [Cross Ref]
23. Du P, Zhang X, Huang C-C, Jafari N, Kibbe WA, Hou L, et al. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics. 2010;11:587. doi: 10.1186/1471-2105-11-587. [PMC free article] [PubMed] [Cross Ref]
24. Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Weisenberger DJ, Shen H, 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. doi: 10.1101/gr.103606.109. [PMC free article] [PubMed] [Cross Ref]
25. Lee TI, Jenner RG, Boyer LA, Guenther MG, Levine SS, Kumar RM, et al. Control of developmental regulators by Polycomb in human embryonic stem cells. Cell. 2006;125:301–13. doi: 10.1016/j.cell.2006.02.043. [PMC free article] [PubMed] [Cross Ref]
26. Heidenblad M, Lindgren D, Jonson T, Liedberg F, Veerla S, Chebil G, et al. Tiling resolution array CGH and high density expression profiling of urothelial carcinomas delineate genomic amplicons and candidate target genes specific for advanced tumors. BMC Med Genomics. 2008;1:3. doi: 10.1186/1755-8794-1-3. [PMC free article] [PubMed] [Cross Ref]
27. Ball MP, Li JB, Gao Y, Lee J-H, LeProust EM, Park I-H, et al. Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nat Biotechnol. 2009;27:361–8. doi: 10.1038/nbt.1533. [PMC free article] [PubMed] [Cross Ref]
28. James L, Eisenman RN. Myc and Mad bHLHZ domains possess identical DNA-binding specificities but only partially overlapping functions in vivo. Proc Natl Acad Sci U S A. 2002;99:10429–34. doi: 10.1073/pnas.162369299. [PMC free article] [PubMed] [Cross Ref]
29. Perini G, Diolaiti D, Porro A, Della Valle G. In vivo transcriptional regulation of N-Myc target genes is controlled by E-box methylation. Proc Natl Acad Sci U S A. 2005;102:12117–22. doi: 10.1073/pnas.0409097102. [PMC free article] [PubMed] [Cross Ref]
30. Fujii G, Nakamura Y, Tsukamoto D, Ito M, Shiba T, Takamatsu N. CpG methylation at the USF-binding site is important for the liver-specific transcription of the chipmunk HP-27 gene. Biochem J. 2006;395:203–9. doi: 10.1042/BJ20051802. [PMC free article] [PubMed] [Cross Ref]
31. Qiao H, May JM. CpG methylation at the USF-binding site mediates cell-specific transcription of human ascorbate transporter SVCT2 exon 1a. Biochem J. 2011;440:73–84. doi: 10.1042/BJ20110392. [PMC free article] [PubMed] [Cross Ref]
32. Rishi V, Bhattacharya P, Chatterjee R, Rozenberg J, Zhao J, Glass K, et al. CpG methylation of half-CRE sequences creates C/EBPalpha binding sites that activate some tissue-specific genes. Proc Natl Acad Sci U S A. 2010;107:20311–6. doi: 10.1073/pnas.1008688107. [PMC free article] [PubMed] [Cross Ref]
33. Rössler J, Stolze I, Frede S, Freitag P, Schweigerer L, Havers W, et al. Hypoxia-induced erythropoietin expression in human neuroblastoma requires a methylation free HIF-1 binding site. J Cell Biochem. 2004;93:153–61. doi: 10.1002/jcb.20133. [PubMed] [Cross Ref]
34. Brenner C, Deplus R, Didelot C, Loriot A, Viré E, De Smet C, et al. Myc represses transcription through recruitment of DNA methyltransferase corepressor. EMBO J. 2005;24:336–46. doi: 10.1038/sj.emboj.7600509. [PMC free article] [PubMed] [Cross Ref]
35. Irizarry RA, Ladd-Acosta C, Wen B, Wu Z, Montano C, Onyango P, et al. The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat Genet. 2009;41:178–86. doi: 10.1038/ng.298. [PMC free article] [PubMed] [Cross Ref]
36. Kandimalla R, van Tilborg AAG, Kompier LC, Stumpel DJPM, Stam RW, Bangma CH, et al. Genome-wide Analysis of CpG Island Methylation in Bladder Cancer Identified TBX2, TBX3, GATA2, and ZIC4 as pTa-Specific Prognostic Markers. Eur Urol. 2012;61:1245–56. doi: 10.1016/j.eururo.2012.01.011. [PubMed] [Cross Ref]
37. Marsit CJ, Houseman EA, Christensen BC, Gagne L, Wrensch MR, Nelson HH, et al. Identification of methylated genes associated with aggressive bladder cancer. PLoS One. 2010;5:e12334. doi: 10.1371/journal.pone.0012334. [PMC free article] [PubMed] [Cross Ref]
38. Fackler MJ, Umbricht CB, Williams D, Argani P, Cruz L-A, Merino VF, et al. Genome-wide methylation analysis identifies genes specific to breast cancer hormone receptor status and risk of recurrence. Cancer Res. 2011;71:6195–207. doi: 10.1158/0008-5472.CAN-11-1630. [PMC free article] [PubMed] [Cross Ref]
39. Fiegl H, Windbichler G, Mueller-Holzner E, Goebel G, Lechner M, Jacobs IJ, et al. HOXA11 DNA methylation--a novel prognostic biomarker in ovarian cancer. Int J Cancer. 2008;123:725–9. doi: 10.1002/ijc.23563. [PubMed] [Cross Ref]
40. Strathdee G, Holyoake TL, Sim A, Parker A, Oscier DG, Melo JV, et al. Inactivation of HOXA genes by hypermethylation in myeloid and lymphoid malignancy is frequent and associated with poor prognosis. Clin Cancer Res. 2007;13:5048–55. doi: 10.1158/1078-0432.CCR-07-0919. [PubMed] [Cross Ref]
41. Atkinson SP, Koch CM, Clelland GK, Willcox S, Fowler JC, Stewart R, et al. Epigenetic marking prepares the human HOXA cluster for activation during differentiation of pluripotent cells. Stem Cells. 2008;26:1174–85. doi: 10.1634/stemcells.2007-0497. [PubMed] [Cross Ref]
42. Houseman EA, Christensen BC, Karagas MR, Wrensch MR, Nelson HH, Wiemels JL, et al. Copy number variation has little impact on bead-array-based measures of DNA methylation. Bioinformatics. 2009;25:1999–2005. doi: 10.1093/bioinformatics/btp364. [PMC free article] [PubMed] [Cross Ref]
43. Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, et al. Missing value estimation methods for DNA microarrays. Bioinformatics. 2001;17:520–5. doi: 10.1093/bioinformatics/17.6.520. [PubMed] [Cross Ref]
44. Smyth GK Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004; 3:Article3. [PubMed]
45. Venkatraman ES, Olshen AB. A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics. 2007;23:657–63. doi: 10.1093/bioinformatics/btl646. [PubMed] [Cross Ref]
46. Staaf J, Jönsson G, Ringnér M, Vallon-Christersson J. Normalization of array-CGH data: influence of copy number imbalances. BMC Genomics. 2007;8:382. doi: 10.1186/1471-2164-8-382. [PMC free article] [PubMed] [Cross Ref]
47. Bibikova M, Le J, Barnes B, Saedinia-Melnyk S, Zhou L, Shen R, et al. Genome-wide DNA methylation profiling using Infinium® assay. Epigenomics. 2009;1:177–200. doi: 10.2217/epi.09.14. [PubMed] [Cross Ref]
48. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. The Gene Ontology Consortium Gene ontology: tool for the unification of biology. Nat Genet. 2000;25:25–9. doi: 10.1038/75556. [PMC free article] [PubMed] [Cross Ref]
49. Bailey TL, Boden M, Buske FA, Frith M, Grant CE, Clementi L, et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 2009;37(Web Server issue):W202-8. doi: 10.1093/nar/gkp335. [PMC free article] [PubMed] [Cross Ref]
50. Veerla S, Ringnér M, Höglund M. Genome-wide transcription factor binding site/promoter databases for the analysis of gene sets and co-occurrence of transcription factor binding motifs. BMC Genomics. 2010;11:145. doi: 10.1186/1471-2164-11-145. [PMC free article] [PubMed] [Cross Ref]

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