Systematic identification of genotype-dependent enhancer variants in eosinophilic esophagitis

Eosinophilic esophagitis (EoE) is a rare atopic disorder associated with esophageal dysfunction, including difficulty swallowing, food impaction, and inflammation, that develops in a small subset of people with food allergies. Genome-wide association studies (GWASs) have identified 9 independent EoE risk loci reaching genome-wide significance (p < 5 × 10-8) and 27 additional loci of suggestive significance (5 × 10-8 < p < 1 × 10-5). In the current study, we perform linkage disequilibrium (LD) expansion of these loci to nominate a set of 531 variants that are potentially causal. To systematically interrogate the gene regulatory activity of these variants, we designed a massively parallel reporter assay (MPRA) containing the alleles of each variant within their genomic sequence context cloned into a GFP reporter library. Analysis of reporter gene expression in TE-7, HaCaT, and Jurkat cells revealed cell-type-specific gene regulation. We identify 32 allelic enhancer variants, representing 6 genome-wide significant EoE loci and 7 suggestive EoE loci, that regulate reporter gene expression in a genotype-dependent manner in at least one cellular context. By annotating these variants with expression quantitative trait loci (eQTL) and chromatin looping data in related tissues and cell types, we identify putative target genes affected by genetic variation in individuals with EoE. Transcription factor enrichment analyses reveal possible roles for cell-type-specific regulators, including GATA3. Our approach reduces the large set of EoE-associated variants to a set of 32 with allelic regulatory activity, providing functional insights into the effects of genetic variation in this disease.

Replication and meta-analyses nominate numerous eosinophilic esophagitis risk genes

BACKGROUND

Eosinophilic esophagitis (EoE) is an emerging, chronic, rare allergic disease associated with marked eosinophil accumulation in the esophagus. Previous genome-wide association studies have provided strong evidence for 3 genome-wide susceptibility loci.

OBJECTIVE

We sought to replicate known and suggestive EoE genetic risk loci and conduct a meta-analysis of previously reported data sets.

METHODS

An EoE-Custom single-nucleotide polymophism (SNP) Chip containing 956 candidate EoE risk single-nucleotide polymorphisms was used to genotype 627 cases and 365 controls. Statistical power was enhanced by adding 1959 external controls and performing meta-analyses with 2 independent EoE genome-wide association studies.

RESULTS

Meta-analysis identified replicated association and genome-wide significance at 6 loci: 2p23 (2 independent genetic effects) and 5q22, 10p14, 11q13, and 16p13. Seven additional loci were identified at suggestive significance (P < 10-6): 1q31, 5q23, 6q15, 6q21, 8p21, 17q12, and 22q13. From these risk loci, 13 protein-coding EoE candidate risk genes were expressed in a genotype-dependent manner. EoE risk genes were expressed in disease-relevant cell types, including esophageal epithelia, fibroblasts, and immune cells, with some expressed as a function of disease activity. The genetic risk burden of EoE-associated genetic variants was markedly larger in cases relative to controls (P < 10-38); individuals with the highest decile of genetic burden had greater than 12-fold risk of EoE compared with those within the lowest decile.

CONCLUSIONS

This study extends the genetic underpinnings of EoE, highlighting 13 genes whose genotype-dependent expression expands our etiologic understanding of EoE and provides a framework for a polygenic risk score to be validated in future studies.

Eosinophilic oesophagitis endotype classification by molecular, clinical, and histopathological analyses: a cross-sectional study

BACKGROUND

Eosinophilic oesophagitis is understood in terms of quantifiable histological, endoscopic, and molecular features. Data are scant for inter-relations of these features and their potential to identify distinct disease endotypes. We aimed to identify clinical-pathological correlations between endoscopic and histological disease variables by transcription profiling of the oesophagus of patients with eosinophilic oesophagitis of varying severity and disease activity states.

METHODS

We did a cross-sectional study across ten hospital sites in the USA associated with the Consortium of Eosinophilic Gastrointestinal Disease Researchers. We analysed oesophageal biopsy specimens taken from paediatric and adult patients with eosinophilic oesophagitis (discovery cohort), using the eosinophilic oesophagitis diagnostic panel (EDP), a set of 96 informative transcripts. Histological and endoscopic features were assessed by quantification of oesophageal eosinophils and use of the eosinophilic oesophagitis histology scoring system (HSS) and the eosinophilic oesophagitis endoscopic reference score (EREFS). Associations among the various histological, endoscopic, and molecular features were analysed by Spearman correlation. Results were replicated in a biologically independent, single-centre, validation cohort of patients with active eosinophilic oesophagitis.

FINDINGS

The discovery cohort contained 185 samples and the validation cohort comprised 100 specimens. In the discovery cohort, EDP showed intersite consistency, significant correlation with oesophageal eosinophils (p<0·0001), and similar findings between paediatric and adult patients. Of eight HSS domains, basal zone hyperplasia correlated with the EDP (median Spearman ρ 0·47 [IQR 0·36-0·60]). Of five EREFS features, distal furrows correlated with the EDP (median Spearman ρ 0·42 [0·32-0·50]). By analysing active eosinophilic oesophagitis in the discovery cohort, the EDP identified three clusters associated with distinct endotypes (termed EoEe1-3) despite similar eosinophil levels. EoEe1 was associated with a normal-appearing oesophagus (risk ratio [RR] 3·27, 95% CI 1·04-10·27; p=0·0443), an inverse association with a history of oesophageal dilation (0·27, 0·09-0·82; p=0·0105) and showed relatively mild histological, endoscopic, and molecular changes. EoEe2 showed an inflammatory and steroid-refractory phenotype (RR 2·77, 95% CI 1·11-6·95; p=0·0376) and had the highest expression of inflammatory cytokines and steroid-responding genes. EoEe3 was associated with a narrow-calibre oesophagus (RR 7·98, 95% CI 1·84-34·64; p=0·0013) and adult onset (2·22, 1·19-4·12; p=0·0155), and showed the highest degree of endoscopic and histological severity and the lowest expression of epithelial differentiation genes. These endotypes were replicated in the validation cohort by clustering and with an eosinophilic oesophagitis endotype-prediction algorithm.

INTERPRETATION

Our new disease classification stratifies patients with eosinophilic oesophagitis into subgroups with potential clinical and therapeutic significance and provides a framework for a precision medicine approach to eosinophilic oesophagitis.

FUNDING

National Institutes of Health.

Identification of endometrial cancer methylation features using combined methylation analysis methods

Background

DNA methylation is a stable epigenetic mark that is frequently altered in tumors. DNA methylation features are attractive biomarkers for disease states given the stability of DNA methylation in living cells and in biologic specimens typically available for analysis. Widespread accumulation of methylation in regulatory elements in some cancers (specifically the CpG island methylator phenotype, CIMP) can play an important role in tumorigenesis. High resolution assessment of CIMP for the entire genome, however, remains cost prohibitive and requires quantities of DNA not available for many tissue samples of interest. Genome-wide scans of methylation have been undertaken for large numbers of tumors, and higher resolution analyses for a limited number of cancer specimens. Methods for analyzing such large datasets and integrating findings from different studies continue to evolve. An approach for comparison of findings from a genome-wide assessment of the methylated component of tumor DNA and more widely applied methylation scans was developed.

Methods

Methylomes for 76 primary endometrial cancer and 12 normal endometrial samples were generated using methylated fragment capture and second generation sequencing, MethylCap-seq. Publically available Infinium HumanMethylation 450 data from The Cancer Genome Atlas (TCGA) were compared to MethylCap-seq data.

Results

Analysis of methylation in promoter CpG islands (CGIs) identified a subset of tumors with a methylator phenotype. We used a two-stage approach to develop a 13-region methylation signature associated with a “hypermethylator state.” High level methylation for the 13-region methylation signatures was associated with mismatch repair deficiency, high mutation rate, and low somatic copy number alteration in the TCGA test set. In addition, the signature devised showed good agreement with previously described methylation clusters devised by TCGA.

Conclusion

We identified a methylation signature for a “hypermethylator phenotype” in endometrial cancer and developed methods that may prove useful for identifying extreme methylation phenotypes in other cancers.

Abstract B41: Methylome profiling identifies unique patterns of DNA methylation in endometrial cancers

Introduction: DNA methylation is a stable epigenetic mark often perturbed during carcinogenesis. Growing evidence demonstrates a role for DNA methylation both as a regulator of gene expression and as a potential biomarker. Widespread accumulation of methylation in regulatory elements in certain cancers (termed the CpG island methylator phenotype) may play a role in carcinogenesis. Few studies, however, have investigated genome-wide methylation in a clinical cohort, as the methodology for analyzing these large datasets remains poorly developed. Endometrial cancer is the most common female gynecological cancer in the US, with sporadic endometrioid as the predominant subtype.

Methods: This study profiles the methylomes of 76 sporadic endometrioid endometrial primary tumors and 12 normal endometrium using sequenced methylated fragments captured by the MBD protein (MethylCap-seq).

Results: Analysis of bulk methylation in promoter CpG islands (CGI) identified a subset of tumors with a putative methylator phenotype. We are currently developing a gene signature to more easily detect endometrial tumors with a methylator phenotype, perform cross-platform analyses, and further characterize clinicopathological variables associated with the phenotype. We have also developed a pipeline for quality analysis and show that exclusion of poor quality samples increases statistical power to call differential methylation and reduces analysis artifacts.

Conclusion: The results of our study highlight MethylCap-seq as a useful tool for profiling methylation patterns in large clinical datasets.

Citation Format: Michael P. Trimarchi, Pearlly S. Yan, Kun Huang, Joanna Groden, Ralf Bundschuh, Paul J. Goodfellow. Methylome profiling identifies unique patterns of DNA methylation in endometrial cancers. [abstract]. In: Proceedings of the AACR Special Conference on Chromatin and Epigenetics in Cancer; Jun 19-22, 2013; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2013;73(13 Suppl):Abstract nr B41.

Cancer epigenetics: a perspective on the role of DNA methylation in acquired endocrine resistance

Epigenetic mechanisms, including DNA methylation, are responsible for determining and maintaining cell fate, stably differentiating the various tissues in our bodies. Increasing evidence shows that DNA methylation plays a significant role in cancer, from the silencing of tumor suppressors to the activation of oncogenes and the promotion of metastasis. Recent studies also suggest a role for DNA methylation in drug resistance. This perspective article discusses how DNA methylation may contribute to the development of acquired endocrine resistance, with a focus on breast cancer. In addition, we discuss DNA methylome profiling and how recent developments in this field are shedding new light on the role of epigenetics in endocrine resistance. Hormone ablation is the therapy of choice for hormone-sensitive breast tumors, yet as many as 40% of patients inevitably relapse, and these hormone refractory tumors often have a poor prognosis. Epigenetic studies could provide DNA methylation biomarkers to predict and diagnose acquired resistance in response to treatment. Elucidation of epigenetic mechanisms may also lead to the development of new treatments that specifically target epigenetic abnormalities or vulnerabilities in cancer cells. Expectations must be tempered by the fact that epigenetic mechanisms of endocrine resistance remain poorly understood, and further study is required to better understand how altering epigenetic pathways with therapeutics can promote or inhibit endocrine resistance in different contexts. Going forward, DNA methylome profiling will become increasingly central to epigenetic research, heralding a network-based approach to epigenetics that promises to advance our understanding of the etiology of cancer in ways not previously possible.

Enrichment-based DNA methylation analysis using next-generation sequencing: sample exclusion, estimating changes in global methylation, and the contribution of replicate lanes

Background

DNA methylation is an important epigenetic mark and dysregulation of DNA methylation is associated with many diseases including cancer. Advances in next-generation sequencing now allow unbiased methylome profiling of entire patient cohorts, greatly facilitating biomarker discovery and presenting new opportunities to understand the biological mechanisms by which changes in methylation contribute to disease. Enrichment-based sequencing assays such as MethylCap-seq are a cost effective solution for genome-wide determination of methylation status, but the technical reliability of methylation reconstruction from raw sequencing data has not been well characterized.

Methods

We analyze three MethylCap-seq data sets and perform two different analyses to assess data quality. First, we investigate how data quality is affected by excluding samples that do not meet quality control cutoff requirements. Second, we consider the effect of additional reads on enrichment score, saturation, and coverage. Lastly, we verify a method for the determination of the global amount of methylation from MethylCap-seq data by comparing to a spiked-in control DNA of known methylation status.

Results

We show that rejection of samples based on our quality control parameters leads to a significant improvement of methylation calling. Additional reads beyond ~13 million unique aligned reads improved coverage, modestly improved saturation, and did not impact enrichment score. Lastly, we find that a global methylation indicator calculated from MethylCap-seq data correlates well with the global methylation level of a sample as obtained from a spike-in DNA of known methylation level.

Conclusions

We show that with appropriate quality control MethylCap-seq is a reliable tool, suitable for cohorts of hundreds of patients, that provides reproducible methylation information on a feature by feature basis as well as information about the global level of methylation.