The multi-omic landscape of transcription factor inactivation in cancer

Identification of a DNA-methylome-based signature for prognosis prediction in driver gene-negative lung adenocarcinoma

"Driver gene-negative" lung adenocarcinoma (LUAD) was of rare treatment options and a poor prognosis. Presently, for them, few biomarkers are available for stratification analysis to make appropriate treatment strategy. This study aimed to develop a DNA-methylome-based signature to realize the precise risk-stratifying. Here, an Illumina MethylationEPIC Beadchip was applied to obtain differentially methylated CpG sites (DMCs). A four-CpG-based signature, named as TLA, was successfully established, whose prognosis-predicting power was well verified in one internal (n = 78) and other external (n = 110) validation cohorts. Patients with high-risk scores had shorter overall survival (OS) in all cohorts [hazard ratio (HR): 11.79, 5.16 and 2.99, respectively]. Additionally, it can effectively divide patients into low-risk and high-risk groups, with significantly different OS in the diverse subgroups stratified by the standard clinical parameters. As an independent prognostic factor, TLA may assist in improving the nomogram's 5-year OS-predicting ability (AUC 0.756, 95% CI:0.695-0.816), superior to TNM alone (AUC 0.644, 95% CI: 0.590-0.698). Additionally, the relationship of TLA-related genes, TAC1, LHX9, and ALX1, with prognosis and tumour invasion made them serve as potential therapy targets for driver gene-negative LUAD.

Computational single-cell methods for predicting cancer risk

Despite recent biotechnological breakthroughs, cancer risk prediction remains a formidable computational and experimental challenge. Addressing it is critical in order to improve prevention, early detection and survival rates. Here, I briefly summarize some key emerging theoretical and computational challenges as well as recent computational advances that promise to help realize the goals of cancer-risk prediction. The focus is on computational strategies based on single-cell data, in particular on bottom-up network modeling approaches that aim to estimate cancer stemness and dedifferentiation at single-cell resolution from a systems-biological perspective. I will describe two promising methods, a tissue and cell-lineage independent one based on the concept of diffusion network entropy, and a tissue and cell-lineage specific one that uses transcription factor regulons. Application of these tools to single-cell and single-nucleus RNA-seq data from stages prior to invasive cancer reveal that they can successfully delineate the heterogeneous inter-cellular cancer-risk landscape, identifying those cells that are more likely to turn cancerous. Bottom-up systems biological modeling of single-cell omic data is a novel computational analysis paradigm that promises to facilitate the development of preventive, early detection and cancer-risk prediction strategies.

Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets

Tumorigenesis is a multistep process, with oncogenic mutations in a normal cell conferring clonal advantage as the initial event. However, despite pervasive somatic mutations and clonal expansion in normal tissues, their transformation into cancer remains a rare event, indicating the presence of additional driver events for progression to an irreversible, highly heterogeneous, and invasive lesion. Recently, researchers are emphasizing the mechanisms of environmental tumor risk factors and epigenetic alterations that are profoundly influencing early clonal expansion and malignant evolution, independently of inducing mutations. Additionally, clonal evolution in tumorigenesis reflects a multifaceted interplay between cell-intrinsic identities and various cell-extrinsic factors that exert selective pressures to either restrain uncontrolled proliferation or allow specific clones to progress into tumors. However, the mechanisms by which driver events induce both intrinsic cellular competency and remodel environmental stress to facilitate malignant transformation are not fully understood. In this review, we summarize the genetic, epigenetic, and external driver events, and their effects on the co-evolution of the transformed cells and their ecosystem during tumor initiation and early malignant evolution. A deeper understanding of the earliest molecular events holds promise for translational applications, predicting individuals at high-risk of tumor and developing strategies to intercept malignant transformation.

On epigenetic stochasticity, entropy and cancer risk

Epigenetic changes are known to accrue in normal cells as a result of ageing and cumulative exposure to cancer risk factors. Increasing evidence points towards age-related epigenetic changes being acquired in a quasi-stochastic manner, and that they may play a causal role in cancer development. Here, I describe the quasi-stochastic nature of DNA methylation (DNAm) changes in ageing cells as well as in normal cells at risk of neoplastic transformation, discussing the implications of this stochasticity for developing cancer risk prediction strategies, and in particular, how it may require a conceptual paradigm shift in how we select cancer risk markers. I also describe the mounting evidence that a significant proportion of DNAm changes in ageing and cancer development are related to cell proliferation, reflecting tissue-turnover and the opportunity this offers for predicting cancer risk via the development of epigenetic mitotic-like clocks. Finally, I describe how age-associated DNAm changes may be causally implicated in cancer development via an irreversible suppression of tissue-specific transcription factors that increases epigenetic and transcriptomic entropy, promoting a more plastic yet aberrant cancer stem-cell state. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Blood-based DNA methylation signatures in cancer: A systematic review

DNA methylation profiles are in dynamic equilibrium via the initiation of methylation, maintenance of methylation and demethylation, which control gene expression and chromosome stability. Changes in DNA methylation patterns play important roles in carcinogenesis and primarily manifests as hypomethylation of the entire genome and the hypermethylation of individual loci. These changes may be reflected in blood-based DNA, which provides a non-invasive means for cancer monitoring. Previous blood-based DNA detection objects primarily included circulating tumor DNA/cell-free DNA (ctDNA/cfDNA), circulating tumor cells (CTCs) and exosomes. Researchers gradually found that methylation changes in peripheral blood mononuclear cells (PBMCs) also reflected the presence of tumors. Blood-based DNA methylation is widely used in early diagnosis, prognosis prediction, dynamic monitoring after treatment and other fields of clinical research on cancer. The reversible methylation of genes also makes them important therapeutic targets. The present paper summarizes the changes in DNA methylation in cancer based on existing research and focuses on the characteristics of the detection objects of blood-based DNA, including ctDNA/cfDNA, CTCs, exosomes and PBMCs, and their application in clinical research.

Computational identification of preneoplastic cells displaying high stemness and risk of cancer progression

Evidence points towards the differentiation state of cells as a marker of cancer risk and progression. Measuring the differentiation state of single cells in a preneoplastic population could thus enable novel strategies for early detection and risk prediction. Recent maps of somatic mutagenesis in normal tissues from young healthy individuals have revealed cancer driver mutations, indicating that these do not correlate well with differentiation state and that other molecular events also contribute to cancer development. We hypothesized that the differentiation state of single cells can be measured by estimating the regulatory activity of the transcription factors (TFs) that control differentiation within that cell lineage. To this end, we present a novel computational method called CancerStemID that estimates a stemness index of cells from single-cell RNA-Seq data. CancerStemID is validated in two human esophageal squamous cell carcinoma (ESCC) cohorts, demonstrating how it can identify undifferentiated preneoplastic cells whose transcriptomic state is overrepresented in invasive cancer. Spatial transcriptomics and whole-genome bisulfite sequencing demonstrated that differentiation activity of tissue-specific TFs was decreased in cancer cells compared to the basal cell-of-origin layer and established that differentiation state correlated with differential DNA methylation at the promoters of these TFs, independently of underlying NOTCH1 and TP53 mutations. The findings were replicated in a mouse model of ESCC development, and the broad applicability of CancerStemID to other cancer-types was demonstrated. In summary, these data support an epigenetic stem-cell model of oncogenesis and highlight a novel computational strategy to identify stem-like preneoplastic cells that undergo positive selection.

Transcription factor HOXB2 upregulates NUSAP1 to promote the proliferation, invasion and migration of nephroblastoma cells via the PI3K/Akt signaling pathway

The transcription factor homeobox protein Hox-B2 (HOXB2) and its downstream factor nucleolar and spindle-associated protein 1 (NUSAP1) play important regulatory roles in cell proliferation, invasion and migration. However, their effects and specific mechanisms in nephroblastoma have not been previously investigated, to the best of our knowledge. Therefore, in the present study, the mRNA and protein expression levels of HOXB2 and NUSAP1 were determined in nephroblastoma cells using reverse transcription-quantitative PCR and western blot analyses, respectively. Furthermore, cell transfection experiments were carried out to knock down NUSAP1 and overexpress HOXB2 in nephroblastoma cell lines. The proliferative, invasive and migratory abilities of nephroblastoma cells were assessed by MTT, EdU, colony formation, wound healing and Transwell assays. In addition, the JASPAR website was used to predict the association between HOXB2 and NUSAP1, which was further verified by dual-luciferase reporter and chromatin immunoprecipitation assays. Finally, the expression levels of the PI3K/Akt signaling pathway-related proteins were measured by western blot analysis. The results showed that the expression of NUSAP1 was abnormally upregulated in nephroblastoma cell lines. However, NUSAP1 silencing attenuated the proliferation, invasion and migration abilities of nephroblastoma cells. The results also suggested that HOXB2 could transcriptionally activate NUSAP1. Therefore, HOXB2 overexpression abrogated the inhibitory effect of NUSAP1 silencing on the proliferation and metastasis of nephroblastoma cells, possibly via the PI3K/Akt signaling pathway. The aforementioned findings indicated that HOXB2 may upregulate NUSAP1 to promote the proliferation, invasion and migration of nephroblastoma cells via the PI3K/Akt signaling pathway.

A pan-tissue DNA methylation atlas enables in silico decomposition of human tissue methylomes at cell-type resolution

Bulk-tissue DNA methylomes represent an average over many different cell types, hampering our understanding of cell-type-specific contributions to disease development. As single-cell methylomics is not scalable to large cohorts of individuals, cost-effective computational solutions are needed, yet current methods are limited to tissues such as blood. Here we leverage the high-resolution nature of tissue-specific single-cell RNA-sequencing datasets to construct a DNA methylation atlas defined for 13 solid tissue types and 40 cell types. We comprehensively validate this atlas in independent bulk and single-nucleus DNA methylation datasets. We demonstrate that it correctly predicts the cell of origin of diverse cancer types and discovers new prognostic associations in olfactory neuroblastoma and stage 2 melanoma. In brain, the atlas predicts a neuronal origin for schizophrenia, with neuron-specific differential DNA methylation enriched for corresponding genome-wide association study risk loci. In summary, the DNA methylation atlas enables the decomposition of 13 different human tissue types at a high cellular resolution, paving the way for an improved interpretation of epigenetic data.

This resource presents an in silico generated DNA methylation atlas that can be used for cell-type deconvolution of human tissues.

Multi-omic characterization of genome-wide abnormal DNA methylation reveals diagnostic and prognostic markers for esophageal squamous-cell carcinoma

This study investigates aberrant DNA methylations as potential diagnosis and prognosis markers for esophageal squamous-cell carcinoma (ESCC), which if diagnosed at advanced stages has <30% five-year survival rate. Comparing genome-wide methylation sites of 91 ESCC and matched adjacent normal tissues, we identified 35,577 differentially methylated CpG sites (DMCs) and characterized their distribution patterns. Integrating whole-genome DNA and RNA-sequencing data of the same samples, we found multiple dysregulated transcription factors and ESCC-specific genomic correlates of identified DMCs. Using featured DMCs, we developed a 12-marker diagnostic panel with high accuracy in our dataset and the TCGA ESCC dataset, and a 4-marker prognostic panel distinguishing high-risk patients. In-vitro experiments validated the functions of 4 marker host genes. Together these results provide additional evidence for the important roles of aberrant DNA methylations in ESCC development and progression. Our DMC-based diagnostic and prognostic panels have potential values for clinical care of ESCC, laying foundations for developing targeted methylation assays for future non-invasive cancer detection methods.

Novel epigenetic network biomarkers for early detection of esophageal cancer

BACKGROUND

Early detection of esophageal cancer is critical to improve survival. Whilst studies have identified biomarkers, their interpretation and validity is often confounded by cell-type heterogeneity.

RESULTS

Here we applied systems-epigenomic and cell-type deconvolution algorithms to a discovery set encompassing RNA-Seq and DNA methylation data from esophageal adenocarcinoma (EAC) patients and matched normal-adjacent tissue, in order to identify robust biomarkers, free from the confounding effect posed by cell-type heterogeneity. We identify 12 gene-modules that are epigenetically deregulated in EAC, and are able to validate all 12 modules in 4 independent EAC cohorts. We demonstrate that the epigenetic deregulation is present in the epithelial compartment of EAC-tissue. Using single-cell RNA-Seq data we show that one of these modules, a proto-cadherin module centered around CTNND2, is inactivated in Barrett's Esophagus, a precursor lesion to EAC. By measuring DNA methylation in saliva from EAC cases and controls, we identify a chemokine module centered around CCL20, whose methylation patterns in saliva correlate with EAC status.

CONCLUSIONS

Given our observations that a CCL20 chemokine network is overactivated in EAC tissue and saliva from EAC patients, and that in independent studies CCL20 has been found to be overactivated in EAC tissue infected with the bacterium F. nucleatum, a bacterium that normally inhabits the oral cavity, our results highlight the possibility of using DNAm measurements in saliva as a proxy for changes occurring in the esophageal epithelium. Both the CTNND2/CCL20 modules represent novel promising network biomarkers for EAC that merit further investigation.

Charting differentially methylated regions in cancer with Rocker-meth

Differentially DNA methylated regions (DMRs) inform on the role of epigenetic changes in cancer. We present Rocker-meth, a new computational method exploiting a heterogeneous hidden Markov model to detect DMRs across multiple experimental platforms. Through an extensive comparative study, we first demonstrate Rocker-meth excellent performance on synthetic data. Its application to more than 6,000 methylation profiles across 14 tumor types provides a comprehensive catalog of tumor type-specific and shared DMRs, and agnostically identifies cancer-related partially methylated domains (PMD). In depth integrative analysis including orthogonal omics shows the enhanced ability of Rocker-meth in recapitulating known associations, further uncovering the pan-cancer relationship between DNA hypermethylation and transcription factor deregulation depending on the baseline chromatin state. Finally, we demonstrate the utility of the catalog for the study of colorectal cancer single-cell DNA-methylation data.

Matteo Benelli et al. present Rocker-meth, a new Hidden Markov Model (HMM)-based method, to robustly identify differentially methylated regions (DMRs). They use Rocker-meth to analyse more than 6000 methylation profiles across 14 cancer types, providing a catalog of tumor-specific and shared DMRs.

DNA methylation and histone variants in aging and cancer

Aging-related diseases such as cancer can be traced to the accumulation of molecular disorder including increased DNA mutations and epigenetic drift. We provide a comprehensive review of recent results in mice and humans on modifications of DNA methylation and histone variants during aging and in cancer. Accumulated errors in DNA methylation maintenance lead to global decreases in DNA methylation with relaxed repression of repeated DNA and focal hypermethylation blocking the expression of tumor suppressor genes. Epigenetic clocks based on quantifying levels of DNA methylation at specific genomic sites is proving to be a valuable metric for estimating the biological age of individuals. Histone variants have specialized functions in transcriptional regulation and genome stability. Their concentration tends to increase in aged post-mitotic chromatin, but their effects in cancer are mainly determined by their specialized functions. Our increased understanding of epigenetic regulation and their modifications during aging has motivated interventions to delay or reverse epigenetic modifications using the epigenetic clocks as a rapid readout for efficacity. Similarly, the knowledge of epigenetic modifications in cancer is suggesting new approaches to target these modifications for cancer therapy.

Cause and effect in epigenetics - where lies the truth, and how can experiments reveal it?: Epigenetic self-reinforcing loops obscure causation in cancer and aging

Epigenetic changes are implicated in aging and cancer. Sometimes, it is clear whether the causing agent of the condition is a genetic factor or epigenetic. In other cases, the causative factor is unclear, and could be either genetic or epigenetic. Is there a general role for epigenetic changes in cancer and aging? Here, I present the paradigm of causative roles executed by epigenetic changes. I discuss cases with clear roles of the epigenome in cancer and aging, and other cases showing involvement of other factors. I also present the possibility that sometimes causality is difficult to assign because of the presence of self-reinforcing loops in epigenetic regulation. Such loops hinder the identification of the causative factor. I provide an experimental framework by which the role of the epigenome can be examined in a better setting and where the presence of such loops could be investigated in more detail.

DNA Methylation Markers in Lung Cancer

Lung cancer is the most common cancer and the leading cause of cancer-related morbidity and mortality worldwide. As early symptoms of lung cancer are minimal and non-specific, many patients are diagnosed at an advanced stage. Despite a concerted effort to diagnose lung cancer early, no biomarkers that can be used for lung cancer screening and prognosis prediction have been established so far. As global DNA demethylation and gene-specific promoter DNA methylation are present in lung cancer, DNA methylation biomarkers have become a major area of research as potential alternative diagnostic methods to detect lung cancer at an early stage. This review summarizes the emerging DNA methylation changes in lung cancer tumorigenesis, focusing on biomarkers for early detection and their potential clinical applications in lung cancer.

Improved detection of tumor suppressor events in single-cell RNA-Seq data

Tissue-specific transcription factors are frequently inactivated in cancer. To fully dissect the heterogeneity of such tumor suppressor events requires single-cell resolution, yet this is challenging because of the high dropout rate. Here we propose a simple yet effective computational strategy called SCIRA to infer regulatory activity of tissue-specific transcription factors at single-cell resolution and use this tool to identify tumor suppressor events in single-cell RNA-Seq cancer studies. We demonstrate that tissue-specific transcription factors are preferentially inactivated in the corresponding cancer cells, suggesting that these are driver events. For many known or suspected tumor suppressors, SCIRA predicts inactivation in single cancer cells where differential expression does not, indicating that SCIRA improves the sensitivity to detect changes in regulatory activity. We identify NKX2-1 and TBX4 inactivation as early tumor suppressor events in normal non-ciliated lung epithelial cells from smokers. In summary, SCIRA can help chart the heterogeneity of tumor suppressor events at single-cell resolution.

EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data

Cell type heterogeneity presents a challenge to the interpretation of epigenome data, compounded by the difficulty in generating reliable single-cell DNA methylomes for large numbers of cells and samples. We present EPISCORE, a computational algorithm that performs virtual microdissection of bulk tissue DNA methylation data at single cell-type resolution for any solid tissue. EPISCORE applies a probabilistic epigenetic model of gene regulation to a single-cell RNA-seq tissue atlas to generate a tissue-specific DNA methylation reference matrix, allowing quantification of cell-type proportions and cell-type-specific differential methylation signals in bulk tissue data. We validate EPISCORE in multiple epigenome studies and tissue types.

Epigenetic Aging: More Than Just a Clock When It Comes to Cancer

The incidence of cancer, adjusted for secular trends, is directly related to age, and advanced chronologic age is one of the most significant risk factors for cancer. Organismal aging is associated with changes at the molecular, cellular, and tissue levels and is affected by both genetic and environmental factors. The specific mechanisms through which these age-associated molecular changes contribute to the increased risk of aging-related disease, such as cancer, are incompletely understood. DNA methylation, a prominent epigenetic mark, also changes over a lifetime as part of an "epigenetic aging" process. Here, we give an update and review of epigenetic aging, in particular, the phenomena of epigenetic drift and epigenetic clock, with regard to its implication in cancer etiology. We discuss the discovery of the DNA methylation-based biomarkers for biological tissue age and the construction of various epigenetic age estimators for human clinical outcomes and health/life span. Recent studies in various types of cancer point to the significance of epigenetic aging in tumorigenesis and its potential use for cancer risk prediction. Future studies are needed to assess the potential clinical impact of strategies focused on lowering cancer risk by preventing premature aging or promoting healthy aging.

Epigenetics of lung cancer: a translational perspective

BACKGROUND

Lung cancer remains the most common cause of cancer-related death, with a 5-year survival rate of only 18%. In recent years, the development of targeted pharmacological agents and immunotherapies has substantially increased the survival of a subset of patients. However, most patients lack such efficacious therapy and are, thus, treated with classical chemotherapy with poor clinical outcomes. Therefore, novel therapeutic strategies are urgently needed. In recent years, the development of epigenetic assays and their application to cancer research have highlighted the relevance of epigenetic regulation in the initiation, development, progression and treatment of lung cancer.

CONCLUSIONS

A variety of epigenetic modifications do occur at different steps of lung cancer development, some of which are key to tumor progression. The rise of cutting-edge technologies such as single cell epigenomics is, and will continue to be, crucial for uncovering epigenetic events at a single cell resolution, leading to a better understanding of the biology underlying lung cancer development and to the design of novel therapeutic options. This approach has already led to the development of strategies involving single agents or combined agents targeting epigenetic modifiers, currently in clinical trials. Here, we will discuss the epigenetics of every step of lung cancer development, as well as the translation of these findings into clinical applications.

Tensorial blind source separation for improved analysis of multi-omic data

There is an increased need for integrative analyses of multi-omic data. We present and benchmark a novel tensorial independent component analysis (tICA) algorithm against current state-of-the-art methods. We find that tICA outperforms competing methods in identifying biological sources of data variation at a reduced computational cost. On epigenetic data, tICA can identify methylation quantitative trait loci at high sensitivity. In the cancer context, tICA identifies gene modules whose expression variation across tumours is driven by copy-number or DNA methylation changes, but whose deregulation relative to normal tissue is independent of such alterations, a result we validate by direct analysis of individual data types.

DNA Methylation Patterns in Normal Tissue Correlate more Strongly with Breast Cancer Status than Copy-Number Variants

Normal tissue at risk of neoplastic transformation is characterized by somatic mutations, copy-number variation and DNA methylation changes. It is unclear however, which type of alteration may be more informative of cancer risk. We analyzed genome-wide DNA methylation and copy-number calls from the same DNA assay in a cohort of healthy breast samples and age-matched normal samples collected adjacent to breast cancer. Using statistical methods to adjust for cell type heterogeneity, we show that DNA methylation changes can discriminate normal-adjacent from normal samples better than somatic copy-number variants. We validate this important finding in an independent dataset. These results suggest that DNA methylation alterations in the normal cell of origin may offer better cancer risk prediction and early detection markers than copy-number changes.

Genes Predisposed to DNA Hypermethylation during Acquired Resistance to Chemotherapy Are Identified in Ovarian Tumors by Bivalent Chromatin Domains at Initial Diagnosis

Bivalent chromatin domains containing both active H3K4me3 and repressive H3K27me3 histone marks define gene sets poised for expression or silencing in differentiating embryonic stem (ES) cells. In cancer cells, aberrantly poised genes may facilitate changes in transcriptional states after exposure to anticancer drugs. In this study, we used ChIP-seq to characterize genome-wide positioning of H3K4me3- and H3K27me3-associated chromatin in primary high-grade serous ovarian carcinomas and in normal ovarian surface and fallopian tube tissue. Gene sets with proximal bivalent marks defined in this manner were evaluated subsequently as signatures of systematic change in DNA methylation and gene expression, comparing pairs of tissue samples taken from patients at primary presentation and relapse following chemotherapy. We found that gene sets harboring bivalent chromatin domains at their promoters in tumor tissue, but not normal epithelia, overlapped with Polycomb-repressive complex target genes as well as transcriptionally silenced genes in normal ovarian and tubal stem cells. The bivalently marked genes we identified in tumors before chemotherapy displayed increased promoter CpG methylation and reduced gene expression at relapse after chemotherapy of ovarian cancer. Overall, our results support the hypothesis that preexisting histone modifications at genes in a poised chromatin state may lead to epigenetic silencing during acquired drug resistance.Significance: These results suggest epigenetic targets for intervention to prevent the emergence of cancer drug resistance. Cancer Res; 78(6); 1383-91. ©2018 AACR.

Systems-epigenomics inference of transcription factor activity implicates aryl-hydrocarbon-receptor inactivation as a key event in lung cancer development

Background

Diverse molecular alterations associated with smoking in normal and precursor lung cancer cells have been reported, yet their role in lung cancer etiology remains unclear. A prominent example is hypomethylation of the aryl hydrocarbon-receptor repressor (AHRR) locus, which is observed in blood and squamous epithelial cells of smokers, but not in lung cancer.

Results

Using a novel systems-epigenomics algorithm, called SEPIRA, which leverages the power of a large RNA-sequencing expression compendium to infer regulatory activity from messenger RNA expression or DNA methylation (DNAm) profiles, we infer the landscape of binding activity of lung-specific transcription factors (TFs) in lung carcinogenesis. We show that lung-specific TFs become preferentially inactivated in lung cancer and precursor lung cancer lesions and further demonstrate that these results can be derived using only DNAm data. We identify subsets of TFs which become inactivated in precursor cells. Among these regulatory factors, we identify AHR, the aryl hydrocarbon-receptor which controls a healthy immune response in the lung epithelium and whose repressor, AHRR, has recently been implicated in smoking-mediated lung cancer. In addition, we identify FOXJ1, a TF which promotes growth of airway cilia and effective clearance of the lung airway epithelium from carcinogens.

Conclusions

We identify TFs, such as AHR, which become inactivated in the earliest stages of lung cancer and which, unlike AHRR hypomethylation, are also inactivated in lung cancer itself. The novel systems-epigenomics algorithm SEPIRA will be useful to the wider epigenome-wide association study community as a means of inferring regulatory activity.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1366-0) contains supplementary material, which is available to authorized users.

Statistical and integrative system-level analysis of DNA methylation data

Epigenetics plays a key role in cellular development and function. Alterations to the epigenome are thought to capture and mediate the effects of genetic and environmental risk factors on complex disease. Currently, DNA methylation is the only epigenetic mark that can be measured reliably and genome-wide in large numbers of samples. This Review discusses some of the key statistical challenges and algorithms associated with drawing inferences from DNA methylation data, including cell-type heterogeneity, feature selection, reverse causation and system-level analyses that require integration with other data types such as gene expression, genotype, transcription factor binding and other epigenetic information.

Epigenetics in multiple myeloma: From mechanisms to therapy

Multiple myeloma (MM) is a tumor of antibody producing plasmablasts/plasma cells that resides within the bone marrow (BM). In addition to the well-established role of genetic lesions and tumor-microenvironment interactions in the development of MM, deregulated epigenetic mechanisms are emerging as important in MM pathogenesis. Recently, MM sequencing and expression projects have revealed that mutations and copy number variations as well as deregulation in the expression of epigenetic modifiers are characteristic features of MM. In the past decade, several studies have suggested epigenetic mechanisms via DNA methylation, histone modifications and non-coding RNAs as important contributing factors in MM with impacts on disease initiation, progression, clonal heterogeneity and response to treatment. Herein we review the present view and knowledge that has accumulated over the past decades on the role of epigenetics in MM, with focus on the interplay between epigenetic mechanisms and the potential use of epigenetic inhibitors as future treatment modalities for MM.