DNA Methylome Analyses Implicate Fallopian Tube Epithelia as the Origin for High-Grade Serous Ovarian Cancer

This article is featured in Highlights of This Issue, p. 765

High-grade serous ovarian cancer (HGSC), the most common and lethal subtype of epithelial ovarian cancer (EOC), is frequently diagnosed at an advanced stage, where long-term survival is poor (1). Understanding the mechanisms driving initiation and progression of HGSC is critical for the development of new diagnostic and therapeutic approaches. In this context, the tissue and cellular origin of HGSC remains a critical question in the field (2–4). The first widely accepted model for the origin of HGSC implicated transformation of the ovarian surface epithelia (OSE), possibly by incessant ovulation (5, 6). This hypothesis is supported by several observations, including the existence of benign epithelial cysts (cystadenomas) in the ovary, a precursor lesion for EOC, and epidemiologic links between ovulation and ovarian cancer (3, 4). On the basis of the OSE origin model, experimental model systems for EOC have used primary and immortalized mouse and human OSE cells, and transgenic mice created by genetic manipulation of the OSE (7, 8). A more recent hypothesis for the origin of HGSC invokes transformation of fallopian tube fimbriae epithelia (FTE; refs. 9, 10). This model is supported by the identification of precursor lesions in the distal fallopian tube, including “p53 signatures,” lesions characterized by increased p53 protein expression due to TP53 mutations, and serous tubal intraepithelial carcinoma (STIC) lesions, which are characterized by increased proliferation and multiple cell layers. Cells emanating from STIC lesions are hypothesized to spread to the ovary, where they develop into invasive serous carcinoma (1, 11). The FTE model is supported by several observations: (i) identification of common FTE but not OSE precursor lesions in BRCA mutation carriers (12), (ii) reduced EOC risk in BRCA mutation carriers following bilateral tubal ligation (13), iii) gene-expression profiling studies showing similarity of FTE and HGSC (14, 15), and (iv) development of HGSC in vivo following the engineering of specific genetic alterations (Tp53, Brca1/2, and Pten) in mouse FTE (16).

In the mammalian embryo, extensive cytosine DNA demethylation erases the bulk of the gamete methylation pattern, which is followed by coordinated remethylation establishing cell and tissue type–specific DNA methylation in somatic cells (17). Consequently, DNA methylation participates in the establishment of an epigenetic signature that contributes to cell and tissue type-specific chromatin organization and gene expression (18–21). Whole-genome bisulfite sequencing analysis of several human tissues in different individuals has recently revealed widespread tissue-specific DNA methylation variation in humans (22). In addition to its normal function in X-chromosome inactivation, genomic imprinting, tissue differentiation, and transposable element silencing, altered DNA methylation makes a major contribution to human diseases, including cancer (23, 24). In cancer, two general DNA methylation defects are common: gene-specific promoter hypermethylation and global DNA hypomethylation (24). Despite these oncogenic changes, tumors might be anticipated to retain tissue-specific DNA methylation that reflect their cellular and tissue origin (25, 26).

In this study, we hypothesized that the DNA methylome provides a means to investigate cell/tissue origin in HGSC. Comparative analysis of the methylome of HGSC, normal FTE, and normal OSE may provide insight into the tissue of origin of HGSC, due to tissue-specific methylation (18, 22, 27, 28). We interrogated the methylomes of HGSC, FTE, and OSE using two complementary methods, and analyzed methylation in several different genomic contexts, to determine the degree of relatedness of the HGSC methylome to FTE and OSE. Our data indicate that the HGSC methylome is consistently and significantly more similar to FTE than OSE. Beyond the implications of this study for understanding the origin of HGSC, DNA methylome profiling may serve as a useful method for cell of origin mapping for other cancers. The stability of DNA methylation, a covalent DNA modification, is an advantageous aspect of this approach in the clinical setting.

We obtained fresh-frozen primary HGSC (n = 10) from patients undergoing surgical resection at Roswell Park Cancer Institute (RPCI), and patient-matched fresh-frozen normal OSE and FTE (n = 5) at RPCI, as described previously (Supplementary Table S1; ref. 29). Briefly, OSE and FTE obtained from patients without malignancy were harvested by mechanical scraping and processing of the epithelial layer of resected ovaries and the fimbriae end of fallopian tubes, immediately upon surgical removal. HGSC tissues were estimated by pathology to contain >80% neoplastic cells. All samples were collected using IRB-approved protocols at RPCI, and sample processing has been described previously (29, 30). We isolated genomic DNA using the Puregene Tissue Kit (Qiagen), which includes RNAse treatment. When selecting HGSC samples, we took into account cancer-specific global DNA hypomethylation, in which repetitive DNA elements are significantly hypomethylated genome-wide (29, 31). Specifically, to eliminate potentially confounding effects due to this phenotype, we used HGSC showing similar LINE-1 DNA methylation as FTE and OSE, as determined by pyrosequencing (data not shown; refs. 29, 31). However, an independent analysis of HGSC samples displaying global DNA hypomethylation affirmed the conclusions presented here (data not shown).

We performed Illumina Infinium 450K bead arrays (450K), which assessed methylation at approximately 470,000 CpG sites, at the RPCI (n = 7) and University of Utah (n = 13) Genomics Core Facilities (Supplementary Table S1). We performed Agilent SureSelect Methylome bisulfite sequencing (methyl-seq), a targeted solution hybridization method (32), which analyzed methylation at approximately 4 × 10⁶ CpGs, at the University of Nebraska Medical Center (UNMC) Epigenomics Core (n = 7; Supplementary Table S1). We used either the Zymo Pico Methyl-Seq or Agilent SureSelect Methyl-Seq Kit for library preparations, and Agilent SureSelect baits to pull down the final sequences. We conducted high-throughput sequencing at the UNMC Sequencing Core, using an Illumina HiSeq 2500 Genome Analyzer. Sequencing parameters and results are shown in Supplementary Table S2. We aligned sequence tags to the human genome (hg19) using Bismark, and selected only those CpG with ≥ ×10 coverage (33). For clarity, we refer to newly generated methylome data as “Karpf 450K data” or “Karpf Methyl-seq data.”

We used 450K data from FTE (n = 7) and primary HGSC (n = 78) from Gene Expression Omnibus (GEO) GSE65821 (Supplementary Table S3); these data have been published and are referred to as “Bowtell 450K data” (34). We only used primary tumor samples, to avoid the influence of disease recurrence and drug resistance. We also utilized Illumina Infinium 27K methylation data (27K) from primary HGSC (n = 550) using The Cancer Genome Atlas (TCGA) data portal (35); these data are referred to as “TCGA 27K data.” For analysis of TCGA data, we only utilized CpG sites that overlapped Karpf FTE and OSE 450K data (25,779 CpG sites).

We used RnBeads (36) to analyze all methylome data (450K, 27K, Methyl-seq), and restricted our analysis to CpG methylation. We analyzed both differentially methylated CpG sites (DMC; ≥25% methylation change) and differentially methylated regions (DMR; contiguous regions of any length containing ≥3 CpGs and ≥25% methylation change). RnBead data included 5kb genomic tiles (n = 131,408), genes [transcriptional start site (TSS) to transcription end, n = 30,514], promoters (−1,500 to +500 bp relative to the TSS, n = 30,630) and CpG islands (n = 26,595). We additionally analyzed CpG island shores (±2,000bp of CpG island; n = 53,190; ref. 37), and enhancers (n = 32,693; Transcribed Enhancer Atlas Database; http://enhancer.binf.ku.dk/index.php). We determined the overlap of CpG sites with different genomic regions using the Bedtools intersect routine (38). We used an FDR-adjusted P value of <0.05 within RnBeads. We created hierarchal clustering and methylation heat maps using TM4 microarray software Multi Experiment Viewer (MeV), based on a Pearson correlation metric and average linkage (39, 40).

To compare individual HGSC DNA methylomes with FTE and OSE, we determined DMC for each HGSC using the R software package DSS (41), by smoothing the RnBeads normalized percent methylation values for 0.5 kb units, and using a moving average algorithm. DMR were defined as regions of any length containing ≥3 CpGs and ≥25% methylation change. We quantified the number of HGSC DMC and DMR that showed a significant difference as compared to FTE or OSE, using a Wald test P value of ≤0.05.

We downloaded the coordinates of all human HOX genes and pseudogenes (n = 333) from the Homeobox database (http://homeodb.zoo.ox.ac.uk/), and aligned CpG sites using genomic locations from −10 kb upstream the TSS to the transcript end. We determined the overlap of CpG sites that were significantly different (FDR-adjusted P value of <0.05) between FTE and OSE with HOX gene genomic regions using the Bedtools intersect routine (38). We created hierarchal clustering and methylation heatmaps using TM4 microarray software Multi Experiment Viewer (MeV), based on a Pearson correlation metric and average linkage (39, 40).

DNA methylation data (450K, *.idat files, and methyl-seq, *.fastq files) were deposited into the NCBI Gene Expression Omnibus (GEO# GSE81228).

The aim of this study was to compare the degree of relatedness of the DNA methylome of primary HGSC with primary FTE and OSE, to infer the tissue lineage of HGSC. For this task, we used a set of patient-matched primary FTE and OSE tissues from patients without malignancy (n = 5). To verify the origin of these samples, we compared their 450K methylomes with recently reported FTE 450K methylome data (n = 7; ref. 34). We compared CpG methylation within a variety of genomic contexts, as well as comparisons of total DMC and DMR (see Materials and Methods). This analysis verified conservation of the two independent FTE methylome data and illustrated significant divergence, in all genomic contexts, using patient-matched OSE (Supplementary Table S4).

We analyzed and compared HGSC, FTE, and OSE methylomes from Karpf 450K data. Figure 1 presents a comparison of methylation of all CpG sites (DMC) and all DMR (5 kb tiles). In both comparisons, HGSC methylation was strikingly more similar to FTE than OSE. To validate this finding, we calculated Pearson correlation coefficients for the comparisons, using all CpG sites or methylation restricted to different genomic regions. In each instance, HGSC showed greater correlation with FTE, as compared with OSE (Supplementary Fig. S1). We next used principal component analyses (PCA) to test this relationship, and again observed an increased similarity of HGSC with FTE, as compared with OSE (Supplementary Fig. S2).

CpG island shores are genomic regions adjacent to CpG islands that display tissue-specific DNA methylation (37), making them relevant for this study. We identified individual CpG sites within CpG island shores showing differential methylation in FTE and OSE, and used these to perform hierarchical clustering of the three sample groups. Importantly, all HGSC samples clustered more closely to FTE than to OSE (Fig. 2A). We additionally observed that one FTE sample clustered within the OSE samples (Fig. 2A; see also Figs. 2B and 3). We speculate this is due to a predominance of individual-specific DNA methylation differences in this particular patient.

A summary of all Karpf 450K methylome comparisons is provided in Table 1 (top section). χ² testing validated the significantly increased similarity of HGSC and FTE as compared with OSE, in each comparison.

We examined the methylation of two genes involved FTE lineage specificity, PAX8 and MSLN (14, 16), hypothesizing that they may show divergence in OSE. As anticipated, PAX8 and MSLN showed relatively similar methylation in HGSC and FTE as compared with OSE (Supplementary Fig. S3). We next examined methylation of HOX genes, as they are involved in development and tissue differentiation, are known to be regulated by DNA methylation, and can show altered methylation and expression in HGSC (42–45). We analyzed methylation of all HOX (and HOX domain-containing) genes (n = 333 genes, n = 9011 CpGs) using Karpf 450K data. Hierarchical clustering of sample groups using HOX-associated CpGs that showed significant differential methylation between FTE and OSE (n = 179) revealed increased similarity of HGSC and FTE (Fig. 3). Specific examples of HOX genes showing divergent methylation patterns in OSE as compared with FTE and HGSC are HOXB1 and HOXB7, which were hypomethylated in OSE as compared to FTE and HGSC (Fig. 4A and B), and HOXD3 and EMX2 (a homeodomain-containing gene), which were hypermethylated in OSE as compared with FTE or HGSC (Fig. 4C and D). We also noted that several HOX genes, including HOXA5, A9, A10, A11, B5, and D11, showed cancer-specific DNA hypermethylation, in agreement with earlier reports (Supplementary Figs. S4–S5; refs. 45–47).

As a complementary approach to 450K, we used methyl-seq to analyze a subgroup of tumor and normal samples, as described in Materials and Methods (Supplementary Table S1). Data from 450K and methyl-seq were well correlated (Supplementary Table S5). The compiled results of methyl-seq are reported in Table 1. As observed with Karpf 450K data, χ² testing of methyl-seq revealed significantly increased similarity of HGSC and FTE as compared with HGSC and OSE, in each genomic context.

We used two published primary HGSC datasets, Bowtell 450K data (n = 78; ref.34) and TCGA 27K data (n = 550; ref. 35), and compared these with our patient-matched primary FTE and OSE 450K data. Given the low genomic coverage of Illumina 27K, we confined our analysis of methylation within different genomic contexts to Bowtell 450K data. Pearson correlation and PCA analyses both indicated increased similarity of HGSC to FTE as compared with OSE (Supplementary Figs. S6–S7). Hierarchical clustering of CpG island shore methylation data also revealed increased relatedness of HGSC with FTE as compared with OSE (Fig. 2B). χ² analysis revealed significantly greater similarity of HGSC with FTE as compared with OSE, in all genomic contexts (Table 1). Despite the reduced coverage of 27K data, it still illustrated the similarity of HGSC to FTE as compared with OSE (Supplementary Fig. S8; Table 1).

The above data report group-based comparative methylome analyses. To determine the extent to which individual HGSC samples are related to FTE and OSE, we used Karpf 450K and Bowtell 450K data to classify individual samples according to total DMC or total DMR. For total DMC, 76 of 78 (97%) HGSC showed increased similarity to FTE, whereas, for DMR, 77 of 78 (99%) HGSC showed increased similarity to FTE (Fig. 5A and B). These data affirm the conclusions drawn from group-wise analyses.

Using the DNA methylome as a classifier, our data reveal that HGSC more closely resembles FTE than OSE. This relationship was conserved regardless of the HGSC sample population, methylome analysis method, or genomic location, including all CpGs, CpG islands, genes, promoters, and all DMR. In addition, CpG island shores and enhancers maintained this relationship, which is notable based on previous studies indicating the importance of tissue-specific differential methylation in CpG island shores, and the well-established role of enhancers in driving tissue specification (37, 48). Thus, our study provides support for the model that HGSC originates in the fimbriae end of the fallopian tube. We also note that use of the proper normal control is an essential aspect of molecular studies of HGSC, and our data indicate that FTE should be used for this purpose.

Within the FTE, there are two major cell types, secretory and ciliated cells. In addition, FTE harbors a minor population of basal cells, which includes the stem cell niche (3). Secretory cells are proliferative and help regenerate the epithelium during cell turnover, whereas ciliated cells are terminally differentiated (11). Determination of the DNA methylomes of individual FTE cell types will provide further insight into the cell lineage of HGSC, and how the methylome becomes altered in cancer.

HOX proteins play key roles in cell and tissue identity and HOX genes can be regulated by DNA methylation (42–44). We therefore analyzed HOX genes and observed that overall HOX methylation in HGSC more closely resembled FTE as compared with OSE. In particular, we show that HOXB1, HOXB7, HOXD3, and EMX2 illustrate a methylation signature that was conserved between HGSC and FTE, but divergent in OSE.

Although overall our data implicate strong conservation between the HGSC and FTE methylomes as compared with OSE, we noted in one comparison (of CpG island shore methylation) that a small subset of HGSC clustered more closely to OSE (Fig. 2B). Therefore, we cannot formally exclude that a subpopulation of HGSC has greater similarity to, and therefore potentially arises from, OSE. In addition, we cannot exclude that the robust relative conservation of the HGSC and FTE methylomes observed overall, rather than implicating FTE as the cell/tissue origin, could result from a transdifferentiation event, for example, Müllerian metaplasia (4). A final limitation of our study is that it used clinically advanced HGSC. Methylome studies of early-stage tumors or HGSC precursor lesions will provide important additional insight into the cellular origin of HGSC.

Beyond the relevance of this study for addressing the tissue of origin of HGSC, our data more generally suggest that DNA methylome analyses may serve as a useful method for mapping cellular and tissue origins in other cancers. The stability of DNA methylation, as both a covalent chemical modification and a stable (replication-coupled) epigenetic mark, is advantageous when considering methylome profiling in the clinical setting. DNA methylation can be measured in biological fluids, fresh-frozen tissue samples, and formalin-fixed paraffin-embedded archival materials (49). Moreover, emerging methylome data implicate DNA methylation in the processes of tissue differentiation and the maintenance of tissue specificity (18, 19, 22, 27, 28). These two traits (biochemical stability and tissue specificity) support the utility and relevance of DNA methylome profiling to investigate the cellular origins of cancer.

No potential conflicts of interest were disclosed.

Conception and design: D. Klinkebiel, A.R. Karpf

Development of methodology: D. Klinkebiel

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): D. Klinkebiel, W. Zhang, S. Akers, K. Odunsi

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): D. Klinkebiel, A.R. Karpf

Writing, review, and/or revision of the manuscript: D. Klinkebiel, S. Akers, K. Odunsi, A.R. Karpf

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): S. Akers, A.R. Karpf

Study supervision: A.R. Karpf

We thank Ann-Marie Patch and David Bowtell for helpful assistance and data sharing, and Shashikant Lele for support. We thank the University of Buffalo, Center of Excellence in Genomics and Bioinformatics, RPCI Bioinformatics and Genomics Core, University of Utah Genomics Core, UNMC Epigenomics Core, UNMC Bioinformatics Core, and the UNMC DNA Sequencing and Microarray Core (supported by NIGMS 8P20GM103427 and P20GM103471) for assistance.

This work was supported by The Otis Glebe Medical Research Foundation (A.R. Karpf), The Betty J. and Charles D. McKinsey Ovarian Cancer Research Fund (to A.R. Karpf), The Fred & Pamela Buffett Cancer Center (NIH P30 CA036727; to A.R. Karpf), and NIH T32CA108456 (to S.N. Akers). K. Odunsi acknowledges support from NIH P30 CA016056, NIH R01CA158318, NIH P50CA159981, and the Roswell Park Alliance Foundation.

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