MICMIC: identification of DNA methylation of distal regulatory regions with causal effects on tumorigenesis

The DNA methylome of pediatric brain tumors appears shaped by structural variation and predicts survival

Structural variation heavily influences the molecular landscape of cancer, in part by impacting DNA methylation-mediated transcriptional regulation. Here, using multi-omic datasets involving >2400 pediatric brain and central nervous system tumors of diverse histologies from the Children's Brain Tumor Network, we report hundreds of genes and associated CpG islands (CGIs) for which the nearby presence of somatic structural variant (SV) breakpoints is recurrently associated with altered expression or DNA methylation, respectively, including tumor suppressor genes ATRX and CDKN2A. Altered DNA methylation near enhancers associates with nearby somatic SV breakpoints, including MYC and MYCN. A subset of genes with SV-CGI methylation associations also have expression associations with patient survival, including BCOR, TERT, RCOR2, and PDLIM4. DNA methylation changes in recurrent or progressive tumors compared to the initial tumor within the same patient can predict survival in pediatric and adult cancers. Our comprehensive and pan-histology genomic analyses reveal mechanisms of noncoding alterations impacting cancer genes.

A modular dCas9-based recruitment platform for combinatorial epigenome editing

Targeted epigenome editing tools allow precise manipulation and investigation of genome modifications, however they often display high context dependency and variable efficacy between target genes and cell types. While systems that simultaneously recruit multiple distinct 'effector' chromatin regulators can improve efficacy, they generally lack control over effector composition and spatial organisation. To overcome this we have created a modular combinatorial epigenome editing platform, called SSSavi. This system is an interchangeable and reconfigurable docking platform fused to dCas9 that enables simultaneous recruitment of up to four different effectors, allowing precise control of effector composition and spatial ordering. We demonstrate the activity and specificity of the SSSavi system and, by testing it against existing multi-effector targeting systems, demonstrate its comparable efficacy. Furthermore, we demonstrate the importance of the spatial ordering of the recruited effectors for effective transcriptional regulation. Together, the SSSavi system enables exploration of combinatorial effector co-recruitment to enhance manipulation of chromatin contexts previously resistant to targeted editing.

Cis- and trans-eQTM analysis reveals novel epigenetic and transcriptomic immune markers of atopic asthma in airway epithelium

BACKGROUND

Expression quantitative trait methylation (eQTM) analyses uncover associations between DNA methylation markers and gene expression. Most eQTM analyses of complex diseases have focused on cis-eQTM pairs (within 1 megabase).

OBJECTIVES

This study sought to identify cis- and trans-methylation markers associated with gene expression in airway epithelium from youth with and without atopic asthma.

METHODS

In this study, the investigators conducted both cis- and trans-eQTM analyses in nasal (airway) epithelial samples from 158 Puerto Rican youth with atopic asthma and 100 control subjects without atopy or asthma. The investigators then attempted to replicate their findings in nasal epithelial samples from 2 studies of children, while also examining whether their results in nasal epithelium overlap with those from an eQTM analysis in white blood cells from the Puerto Rican subjects.

RESULTS

This study identified 9,108 cis-eQTM pairs and 2,131,500 trans-eQTM pairs. Trans-associations were significantly enriched for transcription factor and microRNA target genes. Furthermore, significant cytosine-phosphate-guanine sites (CpGs) were differentially methylated in atopic asthma and significant genes were enriched for genes differentially expressed in atopic asthma. In this study, 50.7% to 62.6% of cis- and trans-eQTM pairs identified in Puerto Rican youth were replicated in 2 smaller cohorts at false discovery rate-adjusted P < .1. Replicated genes in the trans-eQTM analysis included biologically plausible asthma-susceptibility genes (eg, HDC, NLRP3, ITGAE, CDH26, and CST1) and are enriched in immune pathways.

CONCLUSIONS

Studying both cis- and trans-epigenetic regulation of airway epithelial gene expression can identify potential causal and regulatory pathways or networks for childhood asthma. Trans-eQTM CpGs may regulate gene expression in airway epithelium through effects on transcription factor and microRNA target genes.

Targeting epigenetic aberrations of sarcoma in CRISPR era

Sarcomas are rare malignancies that exhibit diverse biological, genetic, morphological, and clinical characteristics. Genetic alterations, such as gene fusions, mutations in transcriptional machinery components, histones, and DNA methylation regulatory molecules, play an essential role in sarcomagenesis. These mutations induce and/or cooperate with specific epigenetic aberrations required for the growth and maintenance of sarcomas. Appropriate mouse models have been developed to clarify the significance of genetic and epigenetic interactions in sarcomas. Studies using the mouse models for human sarcomas have demonstrated major advances in our understanding the developmental processes as well as tumor microenvironment of sarcomas. Recent technological progresses in epigenome editing will not only improve the studies using animal models but also provide a direct clue for epigenetic therapies. In this manuscript, we review important epigenetic aberrations in sarcomas and their representative mouse models, current methods of epigenetic editing using CRISPR/dCas9 systems, and potential applications in sarcoma studies and therapeutics.

Immune Pathways with Aging Characteristics Improve Immunotherapy Benefits and Drug Prediction in Human Cancer

(1) Background: Perturbation of immune-related pathways can make substantial contributions to cancer. However, whether and how the aging process affects immune-related pathways during tumorigenesis remains largely unexplored. (2) Methods: Here, we comprehensively investigated the immune-related genes and pathways among 25 cancer types using genomic and transcriptomic data. (3) Results: We identified several pathways that showed aging-related characteristics in various cancers, further validated by conventional aging-related gene sets. Genomic analysis revealed high mutation burdens in cytokines and cytokines receptors pathways, which were strongly correlated with aging in diverse cancers. Moreover, immune-related pathways were found to be favorable prognostic factors in melanoma. Furthermore, the expression level of these pathways had close associations with patient response to immune checkpoint blockade therapy in melanoma and non-small cell lung cancer. Applying a net-work-based method, we predicted immune- and aging-related genes in pan-cancer and utilized these genes for potential immunotherapy drug discovery. Mapping drug target data to our top-ranked genes identified potential drug targets, FYN, JUN, and SRC. (4) Conclusions: Taken together, our systematic study helped interpret the associations among immune-related pathways, aging, and cancer and could serve as a resource for promoting clinical treatment.

Single-Cell Dissection of the Multiomic Landscape of High-Grade Serous Ovarian Cancer

High-grade serous cancer (HGSC) is the most common subtype of ovarian cancer. HGSC is highly aggressive with poor patient outcomes, and a deeper understanding of HGSC tumorigenesis could help guide future treatment development. To systematically characterize the underlying pathologic mechanisms and intratumoral heterogeneity in human HGSC, we used an optimized single-cell multiomics sequencing technology to simultaneously analyze somatic copy-number alterations (SCNA), DNA methylation, chromatin accessibility, and transcriptome in individual cancer cells. Genes associated with interferon signaling, metallothioneins, and metabolism were commonly upregulated in ovarian cancer cells. Integrated multiomics analyses revealed that upregulation of interferon signaling and metallothioneins was influenced by both demethylation of their promoters and hypomethylation of satellites and LINE1, and potential key transcription factors regulating glycolysis using chromatin accessibility data were uncovered. In addition, gene expression and DNA methylation displayed similar patterns in matched primary and abdominal metastatic tumor cells of the same genetic lineage, suggesting that metastatic cells potentially preexist in the subclones of primary tumors. Finally, the lineages of cancer cells with higher residual DNA methylation levels and upregulated expression of CCN1 and HSP90AA1 presented greater metastatic potential. This study characterizes the critical genetic, epigenetic, and transcriptomic features and their mutual regulatory relationships in ovarian cancer, providing valuable resources for identifying new molecular mechanisms and potential therapeutic targets for HGSC.

SIGNIFICANCE

Integrated analysis of multiomic changes and epigenetic regulation in high-grade serous ovarian cancer provides insights into the molecular characteristics of this disease, which could help improve diagnosis and treatment.

Enhancer methylation dynamics drive core transcriptional regulatory circuitry in pan-cancer

Accumulating evidence has demonstrated that enhancer methylation has strong and dynamic regulatory effects on gene expression. Some transcription factors (TFs) can auto- and cross-regulate in a feed-forward manner, and cooperate with their enhancers to form core transcriptional regulatory circuitries (CRCs). However, the elaborated regulatory mechanism between enhancer methylation and CRC remains the tip of the iceberg. Here, we revealed that DNA methylation could drive the tissue-specific enhancer basal transcription and target gene expression in human cancers. By integrating methylome, transcriptome, and 3D genomic data, we identified enhancer methylation triplets (enhancer methylation-enhancer transcription-target gene expression) and dissected potential regulatory patterns within them. Moreover, we observed that cancer-specific core TFs regulated by enhancers were able to shape their enhancer methylation forming the enhancer methylation-driven CRCs (emCRCs). Further parsing of clinical implications showed rewired emCRCs could serve as druggable targets and prognostic risk markers. In summary, the integrative analysis of enhancer methylation regulome would facilitate portraying the cancer epigenomics landscape and developing the epigenetic anti-cancer approaches.

Epigenetic alterations at distal enhancers are linked to proliferation in human breast cancer

Aberrant DNA methylation is an early event in breast carcinogenesis and plays a critical role in regulating gene expression. Here, we perform genome-wide expression-methylation Quantitative Trait Loci (emQTL) analysis through the integration of DNA methylation and gene expression to identify disease-driving pathways under epigenetic control. By grouping the emQTLs using biclustering we identify associations representing important biological processes associated with breast cancer pathogenesis including regulation of proliferation and tumor-infiltrating fibroblasts. We report genome-wide loss of enhancer methylation at binding sites of proliferation-driving transcription factors including CEBP-β, FOSL1, and FOSL2 with concomitant high expression of proliferation-related genes in aggressive breast tumors as we confirm with scRNA-seq. The identified emQTL-CpGs and genes were found connected through chromatin loops, indicating that proliferation in breast tumors is under epigenetic regulation by DNA methylation. Interestingly, the associations between enhancer methylation and proliferation-related gene expression were also observed within known subtypes of breast cancer, suggesting a common role of epigenetic regulation of proliferation. Taken together, we show that proliferation in breast cancer is linked to loss of methylation at specific enhancers and transcription factor binding and gene activation through chromatin looping.

EnMCB: an R/bioconductor package for predicting disease progression based on methylation correlated blocks using ensemble models

MOTIVATION

Based on the concept that adjacent CpG sites in the same DNA strand may be modified by a methyltransferase or demethylase together, current study found that the combination of multiple CpGs into a single block may improve cancer diagnosis. However, there is no R package available for building models based on methylation correlated blocks.

RESULTS

Here, we present a package named stacked ensemble of machine learning models for methylation correlated blocks (EnMCB) to build signatures based on DNA methylation correlated blocks for survival prediction. The Cox regression, support vector regression, mboost and elastic-net model were combined in the ensemble model. Methylation profiles from Cancer Genome Atlas were used as real datasets. The package automatically partitions the genome into blocks of tightly co-methylated CpG sites, termed methylation correlated blocks. After partitioning and modeling, the diagnostic capacities for predicting patients' survival are given.

AVAILABILITY

EnMCB is freely available for download at GitHub (https://github.com/whirlsyu/EnMCB/) and Bioconductor (http://bioconductor.org/packages/release/bioc/html/EnMCB.html).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Targeting cancer epigenetics with CRISPR-dCAS9: Principles and prospects

Cancer therapeutics is an ever-evolving field due to incessant demands for effective and precise treatment options. Over the last few decades, cancer treatment strategies have shifted somewhat from surgery to targeted precision medicine. CRISPR-dCas9 is an emerging version of precision cancer therapy that has been adapted from the prokaryotic CRISPR-Cas system. Once ligated to epigenetic effectors (EE), CRISPR-dCas9 can function as an epigenetic editing tool and CRISPR-dCas9-EE complexes could be exploited to alter cancerous epigenetic features associated with different cancer hallmarks. In this article, we discuss the rationale of epigenetic editing as a therapeutic strategy against cancer. We also outline how sgRNA-dCas9 was derived from the CRISPR-Cas system. In addition, the current status of sgRNA-dCas9 use (in vivo and in vitro) in cancer is updated with a molecular illustration of CRISPR-dCas9-mediated epigenetic and transcriptional modulation. As sgRNA-dCas9 is still at the developmental phase, challenges are inherent to its use. We evaluate major challenges in targeting cancer with sgRNA-dCas9 such as off-target effects, lack of sgRNA designing rubrics, target site selection dilemmas and deficient sgRNA-dCas9 delivery systems. Finally, we appraise the sgRNA-dCas9 as a prospective cancer therapeutic by summarizing ongoing improvements of sgRNA-dCas9 methodology.

Predicting lung adenocarcinoma disease progression using methylation-correlated blocks and ensemble machine learning classifiers

Applying the knowledge that methyltransferases and demethylases can modify adjacent cytosine-phosphorothioate-guanine (CpG) sites in the same DNA strand, we found that combining multiple CpGs into a single block may improve cancer diagnosis. However, survival prediction remains a challenge. In this study, we developed a pipeline named "stacked ensemble of machine learning models for methylation-correlated blocks" (EnMCB) that combined Cox regression, support vector regression (SVR), and elastic-net models to construct signatures based on DNA methylation-correlated blocks for lung adenocarcinoma (LUAD) survival prediction. We used methylation profiles from the Cancer Genome Atlas (TCGA) as the training set, and profiles from the Gene Expression Omnibus (GEO) as validation and testing sets. First, we partitioned the genome into blocks of tightly co-methylated CpG sites, which we termed methylation-correlated blocks (MCBs). After partitioning and feature selection, we observed different diagnostic capacities for predicting patient survival across the models. We combined the multiple models into a single stacking ensemble model. The stacking ensemble model based on the top-ranked block had the area under the receiver operating characteristic curve of 0.622 in the TCGA training set, 0.773 in the validation set, and 0.698 in the testing set. When stratified by clinicopathological risk factors, the risk score predicted by the top-ranked MCB was an independent prognostic factor. Our results showed that our pipeline was a reliable tool that may facilitate MCB selection and survival prediction.

Data integration for inferring context-specific gene regulatory networks

Transcriptional regulatory networks control context-specific gene expression patterns and play important roles in normal and disease processes. Advances in genomics are rapidly increasing our ability to measure different components of the regulation machinery at the single-cell and bulk population level. An important challenge is to combine different types of regulatory genomic measurements to construct a more complete picture of gene regulatory networks across different disease, environmental, and developmental contexts. In this review, we focus on recent computational methods that integrate regulatory genomic data sets to infer context specificity and dynamics in regulatory networks.

Expression Quantitative Trait Methylation Analysis Reveals Methylomic Associations With Gene Expression in Childhood Asthma

BACKGROUND

Nasal (airway) epithelial methylation profiles have been associated with asthma, but the effects of such profiles on expression of distant cis-genes are largely unknown.

RESEARCH QUESTION

To identify genes whose expression is associated with proximal and distal CpG probes (within 1 Mb), and to assess whether and how such genes are differentially expressed in atopic asthma.

STUDY DESIGN AND METHODS

Genome-wide expression quantitative trait methylation (eQTM) analysis in nasal epithelium from Puerto Rican subjects (aged 9-20 years) with (n = 219) and without (n = 236) asthma. After the eQTM analysis, a Gene Ontology Enrichment analysis was conducted for the top 500 eQTM genes, and mediation analyses were performed to identify paths from DNA methylation to atopic asthma through gene expression. Asthma was defined as physician-diagnosed asthma and wheeze in the previous year, and atopy was defined as at least one positive IgE to allergens. Atopic asthma was defined as the presence of both atopy and asthma.

RESULTS

We identified 16,867 significant methylation-gene expression pairs (false-discovery rate-adjusted P < .01) in nasal epithelium from study participants. Most eQTM methylation probes were distant (average distance, ∼378 kb) from their target genes, and also more likely to be located in enhancer regions of their target genes in lung tissue than control probes. The top 500 eQTM genes were enriched in pathways for immune processes and epithelial integrity and were more likely to have been previously identified as differentially expressed in atopic asthma. In a mediation analysis, we identified 5,934 paths through which methylation markers could affect atopic asthma through gene expression in nasal epithelium.

INTERPRETATION

Previous epigenome-wide association studies of asthma have estimated the effects of DNA methylation markers on expression of nearby genes in airway epithelium. Our findings suggest that distant epigenetic regulation of gene expression in airway epithelium plays a role in atopic asthma.

CRISPR Tools for Physiology and Cell State Changes: Potential of Transcriptional Engineering and Epigenome Editing

Given the large amount of genome-wide data that have been collected during the last decades, a good understanding of how and why cells change during development, homeostasis, and disease might be expected. Unfortunately, the opposite is true; triggers that cause cellular state changes remain elusive, and the underlying molecular mechanisms are poorly understood. Although genes with the potential to influence cell states are known, the historic dependency on methods that manipulate gene expression outside the endogenous chromatin context has prevented us from understanding how cells organize, interpret, and protect cellular programs. Fortunately, recent methodological innovations are now providing options to answer these outstanding questions, by allowing to target and manipulate individual genomic and epigenomic loci. In particular, three experimental approaches are now feasible due to DNA targeting tools, namely, activation and/or repression of master transcription factors in their endogenous chromatin context; targeting transcription factors to endogenous, alternative, or inaccessible sites; and finally, functional manipulation of the chromatin context. In this article, we discuss the molecular basis of DNA targeting tools and review the potential of these new technologies before we summarize how these have already been used for the manipulation of cellular states and hypothesize about future applications.

Harnessing targeted DNA methylation and demethylation using dCas9

DNA methylation is an essential DNA modification that plays a crucial role in genome regulation during differentiation and development, and is disrupted in a range of disease states. The recent development of CRISPR/catalytically dead CRISPR/Cas9 (dCas9)-based targeted DNA methylation editing tools has enabled new insights into the roles and functional relevance of this modification, including its importance at regulatory regions and the role of aberrant methylation in various diseases. However, while these tools are advancing our ability to understand and manipulate this regulatory layer of the genome, they still possess a variety of limitations in efficacy, implementation, and targeting specificity. Effective targeted DNA methylation editing will continue to advance our fundamental understanding of the role of this modification in different genomic and cellular contexts, and further improvements may enable more accurate disease modeling and possible future treatments. In this review, we discuss strategies, considerations, and future directions for targeted DNA methylation editing.

Dead Cas Systems: Types, Principles, and Applications

The gene editing tool CRISPR-Cas has become the foundation for developing numerous molecular systems used in research and, increasingly, in medical practice. In particular, Cas proteins devoid of nucleolytic activity (dead Cas proteins; dCas) can be used to deliver functional cargo to programmed sites in the genome. In this review, we describe current CRISPR systems used for developing different dCas-based molecular approaches and summarize their most significant applications. We conclude with comments on the state-of-art in the CRISPR field and future directions.

Methylation and transcriptome analysis reveal lung adenocarcinoma-specific diagnostic biomarkers

Background

DNA methylation can regulate the role of long noncoding RNAs (lncRNAs) in the development of lung adenocarcinoma (LUAD). The present study aimed to identify methylation-driven lncRNAs and mRNAs as biomarkers in the prognosis of LUAD using bioinformatics analysis.

Methods

Differentially expressed RNAs were obtained using the edge R package from 535 LUAD tissues and 59 adjacent non-LUAD tissues. Differentially methylated genes were obtained using the limma R package from 475 LUAD tissues and 32 adjacent non-LUAD tissues. Methylation-driven mRNA and lncRNA were obtained using the MethylMix R package from 465 LUAD tissues with matched DNA methylation and RNA expression and 32 non-LUAD tissues with DNA methylation. Gene ontology and ConsensusPathDB pathway analysis were performed to identify functional enrichment of methylation-driven mRNAs. Univariate and multivariate Cox regression analyses were performed to identify the independent effect of each variable for predicting the prognosis of LUAD. Kaplan–Meier curve analysis of DNA methylation and gene expression might provide potential prognostic biomarkers for LUAD patients.

Results

A total of 99 methylation-driven mRNAs and 17 methylation-driven lncRNAs were obtained. Univariate and multivariate Cox regression analysis showed that 6 lncRNAs (FOXE1, HOXB13-AS1_2, VMO1, HIST1H3F, AJ003147.8, ASXL3) were retrieved to construct a predictive model associated with overall survival in LUAD patients. Combined DNA methylation and gene expression survival analysis revealed that 4 lncRNAs (AC023824.1, AF186192.1, LINC01354 and WASIR2) and 8 mRNAs (S1PR1, CCDC181, F2RL1, EFS, KLHDC9, MPV17L, GKN2, ITPRIPL1) might act as independent biomarkers for the prognosis of LUAD.

Conclusions

Methylation-driven lncRNA and mRNA contribute to the survival of LUAD, and 4 lncRNAs and 8 mRNAs might be potential biomarkers for the prognosis of LUAD.

MR4Cancer: a web server prioritizing master regulators for cancer

MOTIVATION

During cancer stage transition, a master regulator (MR) refers to the key gene controlling cancer initiation and progression by orchestrating the associated target genes (termed as its regulon). Due to their inherent importance, MRs can serve as critical biomarkers for cancer diagnosis and prognosis, and therapeutic targets. However, it is challenging to infer key MRs that might explain gene expression profile changes between two groups due to lack of context-specific regulons, whose expression level can collectively reflect the activity of likely MRs. There is also a need to design an easy-to-use tool of MR identification for research community.

RESULTS

First, we generated cancer-specific regulons for 26 cancer types by analyzing high-throughput omics data from TCGA, and extracted noncancer-specific regulons from public databases. We subsequently developed a web server MR4Cancer, integrating the regulons with statistical inference to identify and prioritize MRs driving a phenotypic divergence of interest. Based on the input gene list (e.g. differentially expressed genes) or expression profile with two groups, MR4Cancer outputs ranked MRs by enrichment testing against the predefined regulons. Gene Ontology and canonical pathway analyses are also conducted to elucidate the function of likely MRs. Moreover, MR4Cancer provides dynamic network visualization for MR-target relations, and users can interactively interrogate the network to produce new hypotheses and high-quality figures for publication. Finally, the presented case studies highlighted the performance of MR4Cancer. We expect this user-friendly and powerful web tool will provide researchers novel insights into tumorigenesis and therapeutic intervention.

AVAILABILITY AND IMPLEMENTATION

http://cis.hku.hk/MR4Cancer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MICMIC: identification of DNA methylation of distal regulatory regions with causal effects on tumorigenesis

Aberrant promoter methylation is a common mechanism for tumor suppressor inactivation in cancer. We develop a set of tools to identify genome-wide DNA methylation in distal regions with causal effect on tumorigenesis called MICMIC. Many predictions are directly validated by dCas9-based epigenetic editing to support the accuracy and efficiency of our tool. Oncogenic and lineage-specific transcription factors are shown to aberrantly shape the methylation landscape by modifying tumor-subtype core regulatory circuitry. Notably, the gene regulatory networks orchestrated by enhancer methylation across different cancer types are seen to converge on a common architecture. MICMIC is available on https://github.com/ZhangJlab/MICMIC .