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Recurrent patterns of DNA methylation in the ZNF154, CASP8, and VHL promoters across a wide spectrum of human solid epithelial tumors and cancer cell lines a

a

a

a

Francisco Sánchez-Vega , Valer Gotea , Hanna M Petrykowska , Gennady Margolin , Thomas b

c

d

a

C Krivak , Julie A DeLoia , Daphne W Bell & Laura Elnitski a

Genome Technology Branch; National Human Genome Research Institute; National Institutes of Health; Bethesda, MD USA b

Department of Obstetrics, Gynecology and Reproductive Sciences; University of Pittsburgh Medical School; Pittsburgh, PA USA c

School of Public Health and Health Services; The George Washington University; Washington DC, USA d

Cancer Genetics Branch; National Human Genome Research Institute; National Institutes of Health; Bethesda, MD USA Published online: 22 Oct 2013.

To cite this article: Francisco Sánchez-Vega, Valer Gotea, Hanna M Petrykowska, Gennady Margolin, Thomas C Krivak, Julie A DeLoia, Daphne W Bell & Laura Elnitski (2013) Recurrent patterns of DNA methylation in the ZNF154, CASP8, and VHL promoters across a wide spectrum of human solid epithelial tumors and cancer cell lines, Epigenetics, 8:12, 1355-1372, DOI: 10.4161/epi.26701 To link to this article: http://dx.doi.org/10.4161/epi.26701

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Research Paper

Research Paper

Epigenetics 8:12, 1355–1372; December 2013; © 2013 Landes Bioscience

Recurrent patterns of DNA methylation in the ZNF154, CASP8, and VHL promoters across a wide spectrum of human solid epithelial tumors and cancer cell lines Francisco Sánchez-Vega1, Valer Gotea1, Hanna M Petrykowska1, Gennady Margolin1, Thomas C Krivak2, Julie A DeLoia3, Daphne W Bell4, and Laura Elnitski1,* Genome Technology Branch; National Human Genome Research Institute; National Institutes of Health; Bethesda, MD USA; 2Department of Obstetrics, Gynecology and Reproductive Sciences; University of Pittsburgh Medical School; Pittsburgh, PA USA; 3School of Public Health and Health Services; The George Washington University; Washington DC, USA; 4Cancer Genetics Branch; National Human Genome Research Institute; National Institutes of Health; Bethesda, MD USA

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Keywords: DNA methylation, cancer, pan-cancer, ZNF154, CASP8, VHL, epigenetics, chromatin, serous ovarian cancer, endometrioid ovarian cancer, endometrioid endometrial cancer, ovarian papillary serous tumors of low malignant potential Abbreviations: ENCODE, Encyclopedia of DNA Elements; TCGA, The Cancer Genome Atlas

The study of aberrant DNA methylation in cancer holds the key to the discovery of novel biological markers for diagnostics and can help to delineate important mechanisms of disease. We have identified 12 loci that are differentially methylated in serous ovarian cancers and endometrioid ovarian and endometrial cancers with respect to normal control samples. The strongest signal showed hypermethylation in tumors at a CpG island within the ZNF154 promoter. We show that hypermethylation of this locus is recurrent across solid human epithelial tumor samples for 15 of 16 distinct cancer types from TCGA. Furthermore, ZNF154 hypermethylation is strikingly present across a diverse panel of ENCODE cell lines, but only in those derived from tumor cells. By extending our analysis from the Illumina 27K Infinium platform to the 450K platform, to sequencing of PCR amplicons from bisulfite treated DNA, we demonstrate that hypermethylation extends across the breadth of the ZNF154 CpG island. We have also identified recurrent hypomethylation in two genomic regions associated with CASP8 and VHL. These three genes exhibit significant negative correlation between methylation and gene expression across many cancer types, as well as patterns of DNaseI hypersensitivity and histone marks that reflect different chromatin accessibility in cancer vs. normal cell lines. Our findings emphasize hypermethylation of ZNF154 as a biological marker of relevance for tumor identification. Epigenetic modifications affecting the promoters of ZNF154, CASP8, and VHL are shared across a vast array of tumor types and may therefore be important for understanding the genomic landscape of cancer.

Introduction DNA methylation is an epigenetic modification that plays an important role in development and disease.1-3 The study of patterns of aberrant methylation in cancer has attracted a significant amount of attention during the past decade.4-9 As a result, a wide variety of data acquisition technologies and computational methods for the analysis and interpretation of DNA methylation data are currently available.10 Several studies have explored the biological relevance of differential methylation at CpG islands and shores,11 the functional implications of promoter vs. gene body methylation12 and the interplay between histone marks and DNA methylation.13-15 Considerable effort has also been devoted to investigate mutational events that could explain cancer-related

aberrant methylation through their effect on the epigenetic machinery.16,17 Recently, the development of clinically relevant diagnostic tests for cancer based on methylation biomarkers measured in blood, urine, and other body fluids18-22 have provided a glimpse into the biomedical applications that may become mainstream in the near future.23 DNA methylation experiments performed by individual laboratories provide evidence for the identification of shared signatures of differential methylation that can separate tumors from normal controls.21,24,25 Even though these studies are typically restricted to relatively small numbers of samples due to technical and clinical constraints, they also provide certain methodological advantages such as a homogeneous workflow that can help to reduce biases across samples associated with technological

*Correspondence to: Laura Elnitski; Email: [email protected] Submitted: 08/09/2013; Revised: 09/30/2013; Accepted: 10/03/2013 http://dx.doi.org/10.4161/epi.26701 www.landesbioscience.com Epigenetics 1355

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variation in platforms, locations, and times of analysis.26,27 Smallscale studies also make it easier for researchers to evaluate specific samples in greater detail, possibly going back to the bench to carry out additional experiments or to acquire complementary types of biological information from those samples. However, attempts to infer and validate reproducible methylation patterns of even moderate complexity frequently require large-scale studies over hundreds or thousands of samples, which involve collaborations between multiple research groups, often including international consortia.28-34 Exploiting the benefits of in-depth analyses of small samples while satisfying the need for statistical significance and reproducibility associated with large sample sizes requires the adoption of integrative strategies that can fill the gap between these two common approaches. Working under this premise, we searched for DNA methylation patterns that occur in multiple cancer types and may therefore be involved in common pathways responsible for the development and progression of cancer. We have found that some differential methylation signatures shared by a set of ovarian and endometrial cancer samples collected and analyzed in our laboratory are recurrent across (1) cancer cell lines from the Encyclopedia of DNA Elements (ENCODE)35 and (2) a compendium of tissue samples from 16 different cancer types collected by The Cancer Genome Atlas (TCGA). We have observed cancer-related hypermethylation of a CpG island at the promoter of ZNF154 and hypomethylation of two genomic regions near the CASP8 and VHL promoters. Methylation at these locations is correlated with gene expression, as well as DNaseI hypersensitivity and histone marks that correspond to closed chromatin in heavily methylated loci. Taken as a whole, our analyses point to the existence of shared epigenetic signatures among different cancer types that may be relevant for developing diagnostic markers and delineating mechanisms of tumorigenesis. Studies involving smaller numbers of samples and cancer types in complementary publications are congruent with this idea.36-38

Results Initial experiments were targeted at identifying DNA methylation patterns that discriminated a set of ovarian and endometrial tumor samples from normal control samples. We then compared our results with publicly available DNA methylation data from TCGA and ENCODE to address whether those patterns—which include differential methylation and negative correlation between methylation and gene expression—were shared across data sets derived from multiple cancer types and diverse cell lines. Patterns of differential methylation shared by serous ovarian cancers and endometrioid ovarian and endometrial cancers In previous work,25 we identified genomic locations of differential methylation in high-grade serous ovarian tumors vs. normal control samples. Additionally, we identified a larger set of loci whose levels of methylation could be used to differentiate tissue samples originating as ovarian high-grade serous cancer, ovarian endometrioid cancer, and endometrial endometrioid cancer from normal controls based on hierarchical clustering results.

Using a larger sample collection that comprises 164 samples and a new methodological approach, we have identified a set of 12 loci that are differentially methylated at significant levels both in high-grade serous ovarian cancers (83 tumors vs. 16 controls) and in ovarian and endometrial cancers of the endometrioid histopathological subtype (53 tumors vs. 12 controls). All 83 serous tumors that we analyzed were located in ovary, whereas the endometrioid samples contained a mixture of 27 ovarian endometrioid tumors and 26 endometrial endometrioid tumors. Of the 12 differentially methylated locations that we identified, one locus was consistently hypermethylated in tumors, while the other 11 loci exhibited lower levels of methylation in tumors than in controls (Table 1; Fig. 1). The signal identified as hypermethylation in tumors with respect to controls was discovered at the locus interrogated by Illumina probe cg21790626, based on a very stringent selection criterion that combines statistical significance and magnitude of effect (see “Materials and Methods”). This position has the strongest signal in terms of differential methylation among any of the significantly hyper- or hypomethylated loci and is located within a CpG island (chr19:58 220 189–58 220 517) immediately downstream of the transcription start site of ZNF154. Methylation at cg21790626 was correlated with increased, widespread methylation of CpG islands across the genomes of ovarian and endometrial endometrioid tumor samples, as shown by analysis of a reference set of 380 probes located at CpG islands (Fig. 2A and B; Spearman correlation = 0.730, P < 2.2e-16). Correlation was also significant for more than 32% of a million sets of 380 probes that were randomly chosen at CpG islands across the entire genome in endometrioid samples, whereas only 5% of those sets reached statistical significance in the serous cases (Fig. 2C). This agrees with our previous report of a CpG island methylator phenotype (CIMP) for the endometrioid, but not serous histopathological subtype.25 Nevertheless, methylation of the cg21790626 locus remains a good discriminative feature to separate the majority of serous tumors from controls. Figure 2 also shows methylation levels for four ovarian papillary serous tumors of low malignant potential (LMP) that were not among the 164 samples used in our original analysis of differential methylation. Three of these samples have low levels of methylation at ZNF154 and low levels of overall methylation in the reference set of CpG islands, while the fourth exhibits intermediate levels for both indicators. Although most LMP tumors are indolent, some show rare progression to aggressive tumors.39 The lack of high levels of ZNF154 methylation in all four LMP samples is consistent with relatively normal DNA methylation patterns when examined at the top 500 most discriminative sites (Fig. 2A). A small number of tumor samples (19 of 136, 14%) show relatively low levels of methylation at the cg21790626 location (Fig. 1; Fig. S1). Their median methylation level is 14.4% (9.5% SD), compared with controls at 5.6% (9.9% SD), and the rest of tumors at 83.8% (15.1% SD). The majority of these 19 tumor samples also score incongruently at the 11 loci selected for significant hypomethylation in cancer (Fig. S1) and 15 out of the 19 exhibit patterns that closely resemble the ones observed in normal

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Table 1. Differentially methylated loci in ovarian and endometrial cancer Serous vs. controls

Endometrioid vs. controls

Probe

Gene

CpG locus (hg19)

N

T

P val

N

T

P val

cg21790626

ZNF154

chr19:58 220 494

0.04

0.82

3.93e-05

0.15

0.80

2.70e-02

cg07908874

ZNF511;TUBGCP2

chr10:135 123 006

0.90

0.53

4.03e-04

0.85

0.35

1.77e-02

cg12334759

C19orf19

chr19:475 154

0.83

0.46

5.60e-04

0.84

0.44

1.92e-02

cg25391023

BTNL2

chr6:32 374 754

0.76

0.38

1.25e-03

0.73

0.29

7.38e-03

cg07014174

KRTAP11-1

chr21:32 253 760

0.90

0.50

1.19e-03

0.83

0.37

1.36e-02

cg04947157

TMC6; TMC8

chr17:76 128 481

0.59

0.19

4.75e-04

0.78

0.27

3.77e-02

cg20312687

DEFB118

chr20:29 956 585

0.77

0.34

2.61e-03

0.75

0.28

8.81e-03

cg13897627

FLJ44674

chr16:49 378 497

0.80

0.37

3.82e-04

0.79

0.37

1.92e-02

cg14992108

SNTB1

chr8:121 825 470

0.88

0.37

1.05e-04

0.81

0.35

1.25e-02

cg16869108

VHL

chr3:10 184 319

0.85

0.32

5.02e-04

0.78

0.29

1.05e-02

cg21032583

LMLN; IQCG

chr3:197 685 803

0.95

0.39

3.82e-04

0.86

0.42

2.48e-02

cg26799474

CASP8

chr2:202 098 951

0.87

0.24

4.42e-05

0.74

0.27

1.15e-02

The set of genomic loci that are differentially methylated in tumors vs. normal controls for both the serous and endometrioid subtypes. Columns show median methylation (β values) in controls (N) and tumors (T). P values were computed using a two-sided Wilcoxon rank sum test and adjusted using Holm’s correction for multiple hypotheses (for the 21 528 probes used in the analysis). Rows were ordered in decreasing order of difference between median in serous tumors minus median in serous controls.

controls according to the hierarchical clustering at 500 discriminative sites shown in Figure 2A. This suggests that they may share an infrequent, although recognized phenotype analogous to CIMP-0 (CpG island methylator phenotype zero) in colorectal cancers.40,41 Such a low-intensity (zero) methylator phenotype has been reported to occur in ovarian tumors by Strathdee et al. using a minimal set of genes.42 A subset of serous-like endometrial tumors with minimal DNA methylation changes compared with normal endometrium has also been described in the TCGA marker paper on endometrial carcinoma.34 Differential methylation across multiple cancer types and tumor cell lines We narrowed the list of 12 differentially methylated loci identified in our study of gynecological tumors down to seven loci that were also differentially methylated in 23 cancer cell lines compared with 28 non-cancer cell lines from ENCODE (Table 2; Fig. 3). Each of these seven loci (ZNF154, VHL, BTNL2, FLJ44674, KRTAP11-1, DEFB118, and CASP8) also showed differential methylation in a majority (at least 13 of 16, 81.25%) of the cancer types that we examined from TCGA data (Table 3; Fig. 4). Based on the consistency of these results, differential methylation at these genomic locations implicates them as a panel of reliable diagnostic markers to distinguish tumors from healthy tissue in multiple cancer types. Here we focus further on the analysis of the loci in the ZNF154, VHL, and CASP8 promoters. Negative correlation between DNA methylation and gene expression Probes associated with genes ZNF154, VHL, and CASP8 exhibit significant levels of negative correlation between methylation and gene expression in at least half of all the cancer types included in our study (Table 4; Fig. 5; Figs. S2 and S3). Specifically, ZNF154 is expressed at low levels in normal tissues.

The data that we used had been normalized by TCGA to set the upper quartile count at 1000 for each cancer type (with the exception of stomach cancer, see “Materials and Methods” for details). The median value of expression in controls for ZNF154 is 63, while the median expression value computed over all the 20 501 genes in the data set is 200 (these two quantities were averaged across tissue types, excluding stomach and thyroid, as well as types with no expression data for controls). ZNF154 expression levels drop even lower in cancer samples, with a median value of expression in tumors equal to 30 (while the median background value for all genes in tumors is 192). In contrast, CASP8 and VHL show higher overall levels of expression than ZNF154 and are more highly expressed in tumors than in controls. The median levels of CASP8 expression across cancer types are 673 in tumors and 481 in controls. Likewise, the median levels of expression for VHL are 734 in tumors and 645 in controls. Whereas probe cg21790626-ZNF154 falls within a CpG island at the promoter of ZNF154, probe cg16869108VHL is located at the south shore (i.e., within a distance of 2 kb downstream) of a CpG island at the VHL promoter. In contrast, probe cg26799474-CASP8 is not associated with any known CpG island, shore or shelf, but is located within 1 kb downstream from the transcription start site. The fact that all these probes are located at or near promoters may be indicative of methylation-mediated gene silencing in tumor and normal conditions, respectively. Therefore, these three loci should also be taken into account as putative biological markers that could confer functional consequences, in light of the negative correlations with transcription levels. The other loci that we examined, including cg20312687-DEFB118, cg07014174-KRTAP11-1, and cg25391023-BTNL2, did not show significant levels of correlation between methylation and expression for many of the cancer types that we investigated, while in the case of probe

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Figure 1. Methylation levels in tumors vs. normal controls for ovarian and endometrial cancer samples. For each probe, points associated with individual samples are drawn over the corresponding boxplots and violin-plots. The vertical axis shows the β value associated with each point, with values ranging from 0 (completely unmethylated) to 1 (completely methylated). Median values of methylation for each sample class are shown in Table 1.

cg13897627-FLJ44674 no expression data was available for the FLJ44674 transcript. Targeted methylation across multiple cancer types and tumor cell lines The recurrent hypermethylation of ZNF154 across multiple tumor samples indicated a targeted mechanism of DNA methylation rather than a random or spurious event. Specifically, this locus was heavily methylated in 22 out of the 23 cancer cell lines (with the exception of NT2-D1, a pluripotent human testicular embryonal carcinoma cell line, Fig. 3) and in tumor samples from 15 out of the 16 cancer types from TCGA (the only exception being thyroid cancer, Fig. 4, which behaved as an outlier at most of the other significant probe sites). In contrast, methylation levels remained consistently low in control samples from the majority

of tissue types, with slight exceptions in three tissues from the gastrointestinal tract (colon, rectum, and stomach; Fig. 4). To further investigate the scope of the CpG island methylation, we examined an extended region using data from the high-resolution Illumina 450K methylation arrays. With this data set, hypermethylation in tumors was detectable at all four probes located in the CpG island, both in cancer cell lines from ENCODE (Fig. 6A) and tumor samples from TCGA (Fig. 6B), with the exception of thyroid. In fact, hypermethylation was also observed at all probes located within the north and the south shores in ENCODE cell lines (Fig. 6A), and the south shore in TCGA tumors (Fig. 6B, data not available for the north shore from TCGA). Of note, ovarian cancer was excluded from the results shown in Figure 6B because no TCGA data were available from the Illumina 450K platform.

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Figure 2. Methylation at the ZNF154 promoter is correlated with widespread methylation of CpG islands in endometrioid tumors. (A) Hierarchical clustering of all our ovarian and endometrial cancer samples using the 500 probes with the highest variance in methylation, selected as in Kolbe et al.25 The heat map shows levels of methylation as β values associated with each pair of probe (columns) and sample (rows). Top horizontal color bar shows which of the probes are located within CpG islands. The vertical color bar on the left shows the level of methylation at cg21790626-ZNF154 for each individual sample. The vertical color bar on the right shows the sample class, including serous controls (SC), serous tumors (ST), endometrioid controls (EC), endometrioid tumors (ET) and papillary serous tumors of low malignant potential (LMP). (B) DNA methylation at cg21790626-ZNF154 vs. average DNA methylation at the set of 380 probes from the top panel that are located within CpG islands. Each point in the plot corresponds to an individual sample. (C) Distribution of P values for Spearman correlation coefficients between DNA methylation at cg21790626-ZNF154 and average methylation at sets of 380 probes that were randomly selected in CpG islands across the entire genome. Each of the two histograms shows results for one million random choices of probe sets. www.landesbioscience.com Epigenetics 1359

Table 2. Differentially methylated loci in ovarian and endometrial cancer and human cell lines Probe

Gene

CpG locus (hg19)

Normal

Cancer

P value

cg21790626

ZNF154

chr19:58 220 494

0.18

0.94

3.11e-08

cg16869108

VHL

chr3:10 184 319

0.82

0.46

4.76e-02

cg25391023

BTNL2

chr6:32 374 754

0.53

0.08

1.37e-04

cg13897627

FLJ44674

chr16:49 378 497

0.59

0.09

4.19e-04

cg07014174

KRTAP11-1

chr21:32 253 760

0.60

0.07

1.59e-05

cg20312687

DEFB118

chr20:29 956 585

0.70

0.07

1.38e-05

cg26799474

CASP8

chr2:202 098 951

0.89

0.16

1.63e-04

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The set of genomic loci that are differentially methylated in serous vs. controls, endometrioid vs. controls and also in cancer vs. normal cell lines from ENCODE. Columns show median methylation (β values) in cell lines labeled as normal and cancer. P values were computed using a two-sided Wilcoxon rank-sum test and adjusted using Holm’s correction for multiple hypotheses (for the 12 candidate probes shown in Table 1). Rows were ordered in decreasing order of difference between median in cancer minus median in normals.

To independently validate these findings and further extend the scope of analysis, we performed amplicon sequencing of bisulfite treated genomic DNA harvested from K562 and GM12878 cell lines (Fig. 7). We chose these two lines because they are Tier 1 cell lines from ENCODE analyses (http://www.genome. gov/26524238) and have the most extensive characterization of all cell lines used in ENCODE. Constrained by the placement of primers for amplifying bisulfite treated DNA, we selected a 302 base pair interval, centered 24 bases downstream from the ZNF154 transcription start site and containing 20 CpG dinucleotides (12 of these were located in the annotated CpG island). We observed a significant difference in DNA methylation levels where the majority of amplicons showed strong methylation across the CpG island in K562 cells, but not in GM12878 cells (Fig. 7A), consistent with our analysis of ENCODE and TCGA data (Figs. 6 and 7A). Furthermore, considering methylation along individual DNA fragments (i.e., sequencing reads), we found methylation in the majority of CpGs from K562 amplicons, whereas most CpGs in GM12878 were not methylated (Fig. 7B). These data support the conclusion that DNA methylation at this locus specifically affects a relatively wide region (300–500 bp) in most tumors and tumor-derived cell lines, but not in normal tissues or immortalized cell lines derived from normal cells. A similar analysis of the Illumina 450K methylation array data at the VHL (chr3:10 183 306–10 183 941) and CASP8 (chr2:202 097 173–202 098 951) promoter regions shows consistent hypomethylation across a wide variety of cell lines and cancer types (Figs. S4 and S5). In these cases the separation between tumor cell lines and non-tumor cell lines was less precisely defined than for ZNF154. Moreover, CASP8 was consistently hypomethylated in all 16 tumors, whereas VHL showed negligible signals in kidney renal papillary cell carcinoma and thyroid cancer. Chromatin accessibility To further investigate the regulatory function of the differentially methylated loci associated with ZNF154, CASP8, and VHL, we analyzed chromatin properties at those sites. We observed statistically significant levels of association between DNaseI hypersensitivity and the normal vs. cancer designation in ENCODE cell lines, which is indicative of regulatory chromatin structure at the unmethylated ZNF154 CpG island. The DNaseI

hypersensitivity cluster located at chr19:58 219 906–58 220 755 contained peak signals for a much larger fraction of normal cell lines than cancer cell lines (87.3% in controls vs. 25% in cancer, P = 4.5e-09, one-sided Fisher’s exact test). This association remained significant, but was reversed, at the genomic locations near the CASP8 promoter (33.8% in controls vs. 78.5% in cancer, P = 5.8e-05, cluster located at chr2:202 098 006–202 098 275) and the VHL promoter (53.5% in controls vs. 89.4% in cancer, P = 5.5e-04, cluster located at chr3:10 184 306–10 185 010). The mean hypersensitivity signals measured in cancer vs. non-cancer cell lines (Fig. 8A) further reinforced the conclusion of statistically significant differences. More precisely, the mean DNaseI hypersensitivity at the ZNF154 promoter was 4.7-fold higher in normal than in cancer cell lines (31.50 in normal vs. 6.75 in cancer, P = 1.3e-07, one-sided t-test). It was 4.4-fold higher in cancer than in normal cell lines for CASP8 (115.36 in cancer vs. 26.39 in normal, P = 0.0310) and 3.2-fold higher in cancer than in normal cell lines for VHL (18.54 in cancer vs. 5.87 in normal, P = 0.0074). These observations of chromatin structure are consistent with the patterns of differential promoter methylation that we find across different types of cancer, wherein open chromatin co-locates with unmethylated regions and closed chromatin colocates with methylated DNA.43 Finally, we analyzed levels of the H3K4me3 and H3K27ac histone marks for these three regions near the ZNF154, CASP8, and VHL promoters in cancer and normal cell lines using ENCODE data. High levels of H3K4me3 and H3K27ac are associated with promoters of actively transcribed genes.44 The H3K4me3 mark has been reported to protect CpG islands from DNA methylation.45,46 Consistent with our DNaseI analysis, the CpG island at the ZNF154 promoter exhibits active chromatin modifications and shows higher average levels of H3K4me3 and H3K27ac in normal cell lines than in cancer cell lines (Fig. 8B). These histone mark differences were statistically significant (P = 0.0168 for H3K4me3 and P = 0.0027 for H3K27ac; one-sided t-tests). As was the case for DNaseI hypersensitivity, the histone modification pattern is reversed in the hypomethylated regions at CASP8 and VHL, where we observe higher levels of H3K4me3 and H3K27ac in cancer cell lines than in normal cell lines (Fig. 8B). These results support the conclusion that CASP8 and VHL are being

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Figure 3. Seven of the 12 loci selected from our ovarian and endometrial cancer analysis show differential methylation in cancer vs. normal cell lines from ENCODE. The heat map shows β values that reflect the level of methylation associated with each probe (columns) in each cell line (rows). The color side bar on the left distinguishes cell lines that were derived from cancer (magenta), normal (green) or unspecified (black) tissues. Unsupervised hierarchical clustering based on pairwise correlations was used to order rows and columns. Median levels of methylation in cancer and normal cell lines are provided in Table 2.

upregulated in cancer, although the differences failed to reach statistical significance. This fact can be explained by the weaker differential signal associated with the two hypomethylated loci, which may not be reliably detectable given the small number of samples used in the analysis and the consequent restrictions on the statistical power of our tests (histone modification data were only available for 8 normal and 4 cancer cell lines). Alternatively, marks at these loci may be less consistent across different cell lines than they are at the ZNF154 locus.

Discussion ZNF154 (also known as pHZ-92) is located at 19q13.4 and encodes a member of the Krüppel C2H2-type zinc finger protein

family, containing 12 C2H2-type zinc-finger domains and one KRAB domain. Some zinc-finger proteins are known to play active roles in transcription47,48 and some are known to be expressed at low levels,49 which is consistent with our analysis of TCGA RNA-Seq data. Certain proteins encoding specific zincfinger domains have been reported to recognize non-methylated DNA and to recruit chromatin-modifying elements to CpG islands,50 and there is evidence for the implication of several zincfinger transcription factors in cancer.51-55 Hypermethylation of ZNF154 has previously been reported by independent sources to occur in bladder,18,56 breast,57 head and neck,58,59 hepatocellular,60 lung,29 ovarian,25,61 prostate,62 and renal63 cancers. Still, very little is known about the biological function of the protein encoded by ZNF154, and the possible functional significance

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Table 3. TCGA data: types of cancer and number of methylation and expression data samples

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Cancer name

TCGA ID

Methylation T

Expression

TN

N

Total

T

TN

N

Total

Bladder urothelial carcinoma

BLCA

135

18

0

171

106

16

0

138

Breast invasive carcinoma

BRCA

749

105

8

967

736

107

0

950

Colon adenocarcinoma

COAD

346

75

0

496

191

0

0

191

Head and neck squamous cell carcinoma

HNSC

260

50

0

360

266

37

0

340

Kidney renal clear cell carcinoma

KIRC

141

359

0

859

411

69

2

551

Kidney renal papillary cell carcinoma

KIRP

54

49

0

152

53

23

2

101

Liver hepatocellular carcinoma

LIHC

49

49

1

148

16

18

2

54

Lung adenocarcinoma

LUAD

382

50

6

488

298

55

2

410

Lung squamous cell carcinoma

LUSC

292

67

2

428

224

34

1

293

Ovarian serous cystadenocarcinoma

OV

596

4

8

612

262

0

0

262

Pancreatic adenocarcinoma

PAAD

42

7

0

56

39

1

0

41

Prostate adenocarcinoma

PRAD

123

49

0

221

103

37

2

179

Rectum adenocarcinoma

READ

151

12

0

175

72

0

0

72

Stomach adenocarcinoma

STAD

231

43

0

317

58

0

0

58

Thyroid carcinoma

THCA

365

50

0

465

353

56

0

465

Uterine corpus endometrioid carcinoma

UCEC

469

31

12

543

360

10

1

381

Number of tumor (T), normal (N), and matched tumor-normal (TN) samples with methylation and expression data. Notice that the number of samples for which both methylation and expression was available is slightly smaller than the partial totals shown in each row (although it was above 90% of the total number of samples with expression data in every type). Methylation data came from Illumina arrays, while expression data consisted of RNA-Seq data for STAD and RNA-Seq V2 data for all the other types (see “Materials and Methods” for details).

of its diminished expression in cancer cell types has not been explored. Recently, this locus was included in a list of 220 genes that are methylated in more than 5% of analyzed tumor samples in tissues from 7 different cancer types.38 The authors of that study reported hypermethylation of probes cg08668790 and cg21790626 in more than 20% of the tumor samples for acutemyeloid leukemia, breast, prostate, lung and ovarian cancer, as well as in between 5% and 20% of the tumors in glioblastoma and colorectal cancer. However, our results provide evidence for a magnitude of effect that goes well beyond occurrence in a relatively restricted fraction of samples. In this respect, our study is the first, to our knowledge, to stress the remarkable combination of signal strength and ubiquity across multiple cancer types that makes this genomic region stand out from the rest and that advocates its exploration as a diagnostic biomarker to distinguish tumors from healthy tissues. Furthermore, our work constitutes the first combined study of DNA methylation and gene expression that delineates epigenetic silencing of the ZNF154 promoter across multiple cancer types. Our results are also the first to demonstrate that hypermethylation is observed across the entire CpG island located at the ZNF154 promoter, rather than only at one or a few array-specific loci. Besides the ZNF154 promoter, we also examined the region surrounding two of the probes that exhibited differential hypomethylation in cancer, near VHL and CASP8. Even though our results for VHL and CASP8 are weaker than for ZNF154, we are the first group to report pan-cancer differential methylation at specific genomic regions located near the promoters of these two

genes (Figs. S4 and S5). Both VHL and CASP8 are known tumor suppressor genes.24,64-67 Cancer-related hypomethylation of CASP8 and VHL has been reported in lung squamous carcinoma 29 and in head and neck cancer.58 CASP8 is a cysteine protease known to play an important role in cell apoptosis. Demethylation of CASP8 can restore gene expression and induce apoptosis,65 and therefore the low levels of methylation observed at this locus in tumors might be indicative of a flawed or ineffectual death-triggering mechanism (i.e., an attempt to trigger apoptosis that arrives too late, or that is counteracted by some other biological process). VHL encodes a protein that functions as a part of the VCB-CUL2 complex, which is involved in protein degradation. Targets of this complex include elements of the hypoxia-inducible factor (HIF) complex, which is involved in adaptation to changing oxygen levels and a variety of cellular functions including cell-cycle control, cell division, differentiation, extracellular matrix assembly and angiogenesis.67 Silencing of VHL due to hypermethylation of the CpG island at its promoter in a set of patients with clearcell renal carcinomas was one of the first known examples of a tumor suppressor being silenced by aberrant DNA methylation in cancer.24 Our analysis of TCGA data does not reveal hypermethylation of this CpG island as a recurrent event across cancer types, but it shows recurrent hypomethylation over two of the probes located within its south shore (Fig. S5). Future studies to determine whether hypomethylation at the south shore of VHL leads to activation of this gene will be important, because restoration of VHL function in VHL-deficient tumors has been proposed as a therapeutic approach to cancer treatment.66

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Figure 4. Mean levels of methylation in tumors (red) vs. normal controls (blue) at the genomic locations shown in Table 2 for 16 types of cancer from TCGA. Error bars show a 95% confidence interval centered at the mean. The number of samples used for each cancer type is provided in Table 3.

The remarkable consistency of the differential methylation signatures presented in our analysis across many types of solid tumors and cell lines suggests the existence of a recurrent epigenetic mechanism that targets these loci in cancer,14,68,69 but not in normal conditions. Whether aberrant methylation is a cause or a consequence of tumorigenesis remains to be elucidated. Answering this question will require a better understanding of the molecular machinery involved in methylation and demethylation at specific genomic targets, placing a special emphasis on the extent and significance of the events that are suspected to alter proper cellular function in tumor cells.

Materials and Methods Data Gynecologic cancer samples Sample preparation Ovarian, endometrial and fallopian tube tissues were handled as published in Kolbe et al.25 Additional endometrial normal and ovarian endometrioid tumor samples were provided by the Cooperative Human Tissue Network, funded by the National Cancer Institute. Samples are from post-menopausal individuals with atrophic endometrium obtained from routine hysterectomy or pelvic resection for non-endometrial cancers, whereas

endometrioid endometrial tumors were from premenopausal or perimenopausal women. The NIH Office of Human Subjects Research determined that research using these samples was not “human subjects research” per the Common Rule (45 CFR 46). DNA was isolated following the protocol of Trizol reagent (Invitrogen) and treated with sodium bisulfite according to the protocol of EZ DNA Methylation Kit (Zymo Research), with slight modification. One-half microgram of DNA was used for each conversion reaction. The hybridization reaction was performed according to the HumanMethylation27 Illumina BeadChip protocol and scanned using an Illumina iScan System. DNA methylation data for TCGA ovarian tumors Methylation data were obtained from the TCGA data repository (https://tcga-data.nci.nih.gov/tcga/) for ovarian serous carcinoma.31 Samples were primarily drawn from batch 9 of the ovarian carcinoma data set, which included 8 normal fallopian tube controls and 39 tumors. An additional 4 controls were provided in batch 40. These data were acquired using the same HumanMethylation27 platform from Illumina as our own samples. We downloaded TCGA data that had been annotated as level 3 data, which represents calculated β values mapped to the genome, per sample (wiki.nci.nih.gov/display/TCGA/ Data+level#Datalevel-level3).

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Table 4. Correlation between methylation and expression at differentially methylated loci cg21790626 ZNF154

cg26799474 CASP8

cg16869108 VHL

cg20312687 DEFB118

cg07014174 KRTAP11-1

cg25391023 BTNL2

BLCA

-0.60 (

Recurrent patterns of DNA methylation in the ZNF154, CASP8, and VHL promoters across a wide spectrum of human solid epithelial tumors and cancer cell lines.

The study of aberrant DNA methylation in cancer holds the key to the discovery of novel biological markers for diagnostics and can help to delineate i...
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