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To reconstruct systematically hyperactive transcription factor (TF)-dependent transcription networks in squamous cell carcinomas (SCCs), a computational method (ELMER) was applied to 1293 pan-SCC patient samples, and 44 hyperactive SCC TFs were identified. As a top candidate, DLX5 exhibits a notable bifurcate re-configuration of its bivalent promoter in cancer. Specifically, DLX5 maintains a bivalent state in normal tissues; its promoter is hypermethylation, leading to DLX5 transcriptional silencing in esophageal adenocarcinoma (EAC). In stark contrast, DLX5 promoter gains active histone marks and becomes transcriptionally activated in ESCC, which is directly mediated by SOX2. Functionally, silencing of DLX5 substantially inhibits SCC viability both in vitro and in vivo. Mechanistically, DLX5 cooperates with TP63 in regulating ∼2000 enhancers and promoters, which converge on activating cancer-promoting pathways. Together, our data establish a novel and strong SCC-promoting factor and elucidate a new epigenomic mechanism - bifurcate chromatin re-configuration - during cancer development.

CpG Island promoter genes make up more than half of human genes, and a subset regulated by Polycomb-Repressive Complex 2 (PRC2

CpG Island promoter genes make up more than half of human genes, and a subset regulated by Polycomb-Repressive Complex 2 (PRC2+-CGI) become DNA hypermethylated and silenced in cancer. Here, we perform a systematic analysis of CGI genes across TCGA cancer types, finding that PRC2+-CGI genes are frequently prone to transcriptional upregulation as well. These upregulated PRC2+-CGI genes control important pathways such as Epithelial-Mesenchymal Transition (EMT) and TNFα-associated inflammatory response, and have greater cancer-type specificity than other CGI genes. Using publicly available chromatin datasets and genetic perturbations, we show that transcription factor binding sites (TFBSs) within distal enhancers underlie transcriptional activation of PRC2+-CGI genes, coinciding with loss of the PRC2-associated mark H3K27me3 at the linked promoter. In contrast, PRC2-free CGI genes are predominantly regulated by promoter TFBSs which are common to most cancer types. Surprisingly, a large subset of PRC2+-CGI genes that are upregulated in one cancer type are also hypermethylated/silenced in at least one other cancer type, underscoring the high degree of regulatory plasticity of these genes, likely derived from their complex regulatory control during normal development., A subset of CpG Island promoter genes are regulated by Polycomb-Repressive Complex 2 (PRC2+-CGI), which become DNA hypermethylated and silenced in cancer. Here, the authors investigate the transcriptomic and epigenomic characteristics of PRC2-occupied CGI and free CGI across pan-cancer types.

Sex is an important factor that contributes to the clinical and biological heterogeneities in Alzheimer’s disease (AD), but the regulatory mechanisms underlying sex disparity in AD are still not well understood. DNA methylation is an important epigenetic modification that regulates gene transcription and is known to be involved in AD. We performed the first large-scale sex-specific meta-analysis of DNA methylation differences in AD neuropathology, by re-analyzing four recent epigenome-wide association studies totaling more than 1000 postmortem prefrontal cortex brain samples using a uniform analytical pipeline. For each cohort, we employed two complementary analytical strategies, a sex-stratified analysis that examined methylation-Braak stage associations in male and female samples separately, and a sex-by-Braak stage interaction analysis that compared the magnitude of these associations between different sexes. Our analysis uncovered 14 novel CpGs, mapped to genes such as TMEM39A and TNXB that are associated with the AD Braak stage in a sex-specific manner. TMEM39A is known to be involved in inflammation, dysregulated type I interferon responses, and other immune processes. TNXB encodes tenascin proteins, which are extracellular matrix glycoproteins demonstrated to modulate synaptic plasticity in the brain. Moreover, for many previously implicated genes in AD neuropathology, such as MBP and AZU1, our analysis provided the new insights that they were predominately driven by effects in only one sex. These sex-specific DNA methylation differences were enriched in divergent biological processes such as integrin activation in females and complement activation in males. Our study implicated multiple new loci and biological processes that affected AD neuropathology in a sex-specific manner.

Sex is an important factor that contributes to the clinical and biological heterogeneities in Alzheimer’s disease (AD), but the regulatory mechanisms underlying sex disparity in AD are still not well understood. DNA methylation is an important epigenetic modification that regulates gene transcription and is known to be involved in AD. We performed the first large-scale sex-specific meta-analysis of DNA methylation changes in AD, by re-analyzing four recent epigenome-wide association studies totaling more than 1000 postmortem prefrontal cortex brain samples using a uniform analytical pipeline. For each cohort we employed two complementary analytical strategies, a sex-stratified analysis that examined methylation-Braak stage associations in male and female samples separately, and a sex-by-Braak stage interaction analysis that compared the magnitude of these associations between different sexes. Our analysis uncovered 14 novel CpGs, mapped to genes such as TMEM39A and TNXB that are associated with AD in a sex-specific manner. TMEM39A is known to be involved in inflammation, dysregulated type I interferon responses, and other immune processes. TNXB encodes tenascin proteins, which are extracellular matrix glycoproteins demonstrated to modulate synaptic plasticity in the brain. Moreover, for many previously implicated AD genes, such as MBP and AZU1, our analysis provided the new insights that they were predominately driven by effects in only one sex. These sex-specific DNA methylation changes were enriched in divergent biological processes such as integrin activation in females and complement activation in males. Importantly, a number of drugs commonly prescribed for AD patients also targeted these genes with sex-specific DNA methylation changes. Our study implicated multiple new loci and biological processes that affected AD in a sex-specific manner and highlighted the importance of sex-specific treatment regimens for AD patients.

Epigenome-wide association studies (EWAS) often detect a large number of differentially methylated sites or regions, many are located in distal regulatory regions. To further prioritize these significant sites, there is a critical need to better understand the functional impact of CpG methylation. Recent studies demonstrated CpG methylation-dependent transcriptional regulation is a widespread phenomenon. Here we present MethReg, an R/Bioconductor package that analyzes matched DNA-methylation and gene-expression data, along with external transcription factor (TF) binding information, to evaluate, prioritize, and annotate CpG sites with high regulatory potential. By simultaneous modeling three key elements that contribute to gene transcription (CpG methylation, target gene expression and TF activity), MethReg identifies TF-target gene associations that are present only in a subset of samples with high (or low) methylation levels at the CpG that influences TF activities, which can be missed in analyses that use all samples. Using real colorectal cancer and Alzheimer’s disease datasets, we show MethReg significantly enhances our understanding of the regulatory roles of DNA methylation in complex diseases.

DNA methylation differences in Alzheimer’s disease (AD) have been reported. Here, we conducted a meta-analysis of more than 1000 prefrontal cortex brain samples to prioritize the most consistent methylation differences in multiple cohorts. Using a uniform analysis pipeline, we identified 3751 CpGs and 119 differentially methylated regions (DMRs) significantly associated with Braak stage. Our analysis identified differentially methylated genes such as MAMSTR, AGAP2, and AZU1. The most significant DMR identified is located on the MAMSTR gene, which encodes a cofactor that stimulates MEF2C. Notably, MEF2C cooperates with another transcription factor, PU.1, a central hub in the AD gene network. Our enrichment analysis highlighted the potential roles of the immune system and polycomb repressive complex 2 in pathological AD. These results may help facilitate future mechanistic and biomarker discovery studies in AD., Previous studies have identified DNA methylation changes in Alzheimer’s disease. Here the authors perform a meta-analysis of DNA methylation datasets to identify epigenetic correlates of Alzheimer’s disease in the prefrontal cortex.

ObjectivesOesophageal squamous cell carcinoma (OSCC) and adenocarcinoma (OAC) are distinct cancers in terms of a number of clinical and epidemiological characteristics, complicating the design of clinical trials and biomarker developments. We analysed 1048 oesophageal tumour-germline pairs from both subtypes, to characterise their genomic features, and biological and clinical significance.DesignPreviously exome-sequenced samples were re-analysed to identify significantly mutated genes (SMGs) and mutational signatures. The biological functions of novel SMGs were investigated using cell line and xenograft models. We further performed whole-genome bisulfite sequencing and chromatin immunoprecipitation (ChIP)-seq to characterise epigenetic alterations.ResultsOSCC and OAC displayed nearly mutually exclusive sets of driver genes, indicating that they follow independent developmental paths. The combined sample size allowed the statistical identification of a number of novel subtype-specific SMGs, mutational signatures and prognostic biomarkers. Particularly, we identified a novel mutational signature similar to Catalogue Of Somatic Mutations In Cancer (COSMIC)signature 16, which has prognostic value in OSCC. Two newly discovered SMGs, CUL3 and ZFP36L2, were validated as important tumour-suppressors specific to the OSCC subtype. We further identified their additional loss-of-function mechanisms. CUL3 was homozygously deleted specifically in OSCC and other squamous cell cancers (SCCs). Notably, ZFP36L2 is associated with super-enhancer in healthy oesophageal mucosa; DNA hypermethylation in its super-enhancer reduced active histone markers in squamous cancer cells, suggesting an epigenetic inactivation of a super-enhancer-associated SCC suppressor.ConclusionsThese data comprehensively contrast differences between OSCC and OAC at both genomic and epigenomic levels, and reveal novel molecular features for further delineating the pathophysiological mechanisms and treatment strategies for these cancers.

ObjectiveWhile oesophageal squamous cell carcinoma remains infrequent in Western populations, the incidence of oesophageal adenocarcinoma (EAC) has increased sixfold to eightfold over the past four decades. We aimed to characterise oesophageal cancer-specific and subtypes-specific gene regulation patterns and their upstream transcription factors (TFs). DesignTo identify regulatory elements, we profiled fresh-frozen oesophageal normal samples, tumours and cell lines with chromatin immunoprecipitation sequencing (ChIP-Seq). Mathematical modelling was performed to establish (super)-enhancers landscapes and interconnected transcriptional circuitry formed by master TFs. Coregulation and cooperation between master TFs were investigated by ChIP-Seq, circularised chromosome conformation capture sequencing and luciferase assay. Biological functions of candidate factors were evaluated both in vitro and in vivo.ResultsWe found widespread and pervasive alterations of the (super)-enhancer reservoir in both subtypes of oesophageal cancer, leading to transcriptional activation of a myriad of novel oncogenes and signalling pathways, some of which may be exploited pharmacologically (eg, leukemia inhibitory factor (LIF) pathway). Focusing on EAC, we bioinformatically reconstructed and functionally validated an interconnected circuitry formed by four master TFs—ELF3, KLF5, GATA6 and EHF—which promoted each other’s expression by interacting with each super-enhancer. Downstream, these master TFs occupied almost all EAC super-enhancers and cooperatively orchestrated EAC transcriptome. Each TF within the transcriptional circuitry was highly and specifically expressed in EAC and functionally promoted EAC cell proliferation and survival.ConclusionsBy establishing cancer-specific and subtype-specific features of the EAC epigenome, our findings promise to transform understanding of the transcriptional dysregulation and addiction of EAC, while providing molecular clues to develop novel therapeutic modalities against this malignancy.

Background The development of next generation sequencing (NGS) methods led to a rapid rise in the generation of large genomic datasets, but the development of user-friendly tools to analyze and visualize these datasets has not developed at the same pace. This presents a two-fold challenge to biologists; the expertise to select an appropriate data analysis pipeline, and the need for bioinformatics or programming skills to apply this pipeline. The development of graphical user interface (GUI) applications hosted on web-based servers such as Shiny can make complex workflows accessible across operating systems and internet browsers to those without programming knowledge. Results We have developed GENAVi (Gene Expression Normalization Analysis and Visualization) to provide a user-friendly interface for normalization and differential expression analysis (DEA) of human or mouse feature count level RNA-Seq data. GENAVi is a GUI based tool that combines Bioconductor packages in a format for scientists without bioinformatics expertise. We provide a panel of 20 cell lines commonly used for the study of breast and ovarian cancer within GENAVi as a foundation for users to bring their own data to the application. Users can visualize expression across samples, cluster samples based on gene expression or correlation, calculate and plot the results of principal components analysis, perform DEA and gene set enrichment and produce plots for each of these analyses. To allow scalability for large datasets we have provided local install via three methods. We improve on available tools by offering a range of normalization methods and a simple to use interface that provides clear and complete session reporting and for reproducible analysis. Conclusion The development of tools using a GUI makes them practical and accessible to scientists without bioinformatics expertise, or access to a data analyst with relevant skills. While several GUI based tools are currently available for RNA-Seq analysis we improve on these existing tools. This user-friendly application provides a convenient platform for the normalization, analysis and visualization of gene expression data for scientists without bioinformatics expertise.

The advent of Next-Generation Sequencing (NGS) technologies has opened new perspectives in deciphering the genetic mechanisms underlying complex diseases. Nowadays, the amount of genomic data is massive and substantial efforts and new tools are required to unveil the information hidden in the data. The Genomic Data Commons (GDC) Data Portal is a platform that contains different genomic studies including the ones from The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives, accounting for more than 40 tumor types originating from nearly 30000 patients. Such platforms, although very attractive, must make sure the stored data are easily accessible and adequately harmonized. Moreover, they have the primary focus on the data storage in a unique place, and they do not provide a comprehensive toolkit for analyses and interpretation of the data. To fulfill this urgent need, comprehensive but easily accessible computational methods for integrative analyses of genomic data that do not renounce a robust statistical and theoretical framework are required. In this context, the R/Bioconductor package TCGAbiolinks was developed, offering a variety of bioinformatics functionalities. Here we introduce new features and enhancements of TCGAbiolinks in terms of i) more accurate and flexible pipelines for differential expression analyses, ii) different methods for tumor purity estimation and filtering, iii) integration of normal samples from other platforms iv) support for other genomics datasets, exemplified here by the TARGET data. Evidence has shown that accounting for tumor purity is essential in the study of tumorigenesis, as these factors promote confounding behavior regarding differential expression analysis. With this in mind, we implemented these filtering procedures in TCGAbiolinks. Moreover, a limitation of some of the TCGA datasets is the unavailability or paucity of corresponding normal samples. We thus integrated into TCGAbiolinks the possibility to use normal samples from the Genotype-Tissue Expression (GTEx) project, which is another large-scale repository cataloging gene expression from healthy individuals. The new functionalities are available in the TCGAbiolinks version 2.8 and higher released in Bioconductor version 3.7., SCOPUS: ar.j, info:eu-repo/semantics/published