Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation

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.

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.

DNA-methylation variability in normal mucosa: a field cancerization marker in patients with adenomatous polyps

BACKGROUND

The phenomenon of field cancerization reflects the transition of normal cells into those predisposed to cancer. Assessing the scope and intensity of this process in the colon may support risk prediction and colorectal cancer prevention.

METHODS

The Swiss Epigenetic Colorectal Cancer Study (SWEPIC) study, encompassing 1111 participants for DNA methylation analysis and a subset of 84 for RNA sequencing, was employed to detect field cancerization in individuals with adenomatous polyps (AP). Methylation variations were evaluated for their discriminative capability, including in external cohorts, genomic localization, clinical correlations, and associated RNA expression patterns.

RESULTS

Normal cecal tissue of individuals harboring an AP in the proximal colon manifested dysregulated DNA methylation compared to tissue from healthy individuals at 558 unique loci. Leveraging these adenoma-related differentially variable and methylated CpGs (aDVMCs), our classifier discerned between healthy and AP-adjacent tissues across SWEPIC datasets (cross-validated area under the receiver operating characteristic curve [ROC AUC] = 0.63-0.81), including within age-stratified cohorts. This discriminative capacity was validated in 3 external sets, differentiating healthy from cancer-adjacent tissue (ROC AUC = 0.82-0.88). Notably, aDVMC dysregulation correlated with polyp multiplicity. More than 50% of aDVMCs were significantly associated with age. These aDVMCs were enriched in active regions of the genome (P < .001), and associated genes exhibited altered expression in AP-adjacent tissues.

CONCLUSIONS

Our findings underscore the early onset of field cancerization in the right colon during the neoplastic transformation process. A more extensive validation of aDVMC dysregulation as a stratification tool could pave the way for enhanced surveillance approaches, especially given its linkage to adenoma emergence.

Cervical cancer screening using DNA methylation triage in a real-world population

Cervical cancer (CC) screening in women comprises human papillomavirus (HPV) testing followed by cytology triage of positive cases. Drawbacks, including cytology's low reproducibility and requirement for short screening intervals, raise the need for alternative triage methods. Here we used an innovative triage technique, the WID-qCIN test, to assess the DNA methylation of human genes DPP6, RALYL and GSX1 in a real-life cohort of 28,017 women aged ≥30 years who attended CC screening in Stockholm between January and March 2017. In the analysis of all 2,377 HPV-positive samples, a combination of WID-qCIN (with a predefined threshold) and HPV16 and/or HPV18 (HPV16/18) detected 93.4% of cervical intraepithelial neoplasia grade 3 and 100% of invasive CCs. The WID-qCIN/HPV16/18 combination predicted 69.4% of incident cervical intraepithelial neoplasia grade 2 or worse compared with 18.2% predicted by cytology. Cytology or WID-qCIN/HPV16/18 triage would require 4.1 and 2.4 colposcopy referrals to detect one cervical intraepithelial neoplasia grade 2 or worse, respectively, during the 6 year period. These findings support the use of WID-qCIN/HPV16/18 as an improved triage strategy for HPV-positive women.

An improved epigenetic counter to track mitotic age in normal and precancerous tissues

The cumulative number of stem cell divisions in a tissue, known as mitotic age, is thought to be a major determinant of cancer-risk. Somatic mutational and DNA methylation (DNAm) clocks are promising tools to molecularly track mitotic age, yet their relationship is underexplored and their potential for cancer risk prediction in normal tissues remains to be demonstrated. Here we build and validate an improved pan-tissue DNAm counter of total mitotic age called stemTOC. We demonstrate that stemTOC's mitotic age proxy increases with the tumor cell-of-origin fraction in each of 15 cancer-types, in precancerous lesions, and in normal tissues exposed to major cancer risk factors. Extensive benchmarking against 6 other mitotic counters shows that stemTOC compares favorably, specially in the preinvasive and normal-tissue contexts. By cross-correlating stemTOC to two clock-like somatic mutational signatures, we confirm the mitotic-like nature of only one of these. Our data points towards DNAm as a promising molecular substrate for detecting mitotic-age increases in normal tissues and precancerous lesions, and hence for developing cancer-risk prediction strategies.

Statistical Models for High-Risk Intestinal Metaplasia with DNA Methylation Profiling

We consider the newly developed multinomial mixed-link models for a high-risk intestinal metaplasia (IM) study with DNA methylation data. Different from the traditional multinomial logistic models commonly used for categorical responses, the mixed-link models allow us to select the most appropriate link function for each category. We show that the selected multinomial mixed-link model (Model 1) using the total number of stem cell divisions (TNSC) based on DNA methylation data outperforms the traditional logistic models in terms of cross-entropy loss from ten-fold cross-validations with significant p-values 8.12×10-4 and 6.94×10-5. Based on our selected model, the significance of TNSC's effect in predicting the risk of IM is justified with a p-value less than 10-6. We also select the most appropriate mixed-link models (Models 2 and 3) when an additional covariate, the status of gastric atrophy, is available. When the status is negative, mild, or moderate, we recommend Model 2; otherwise, we prefer Model 3. Both Models 2 and 3 can predict the risk of IM significantly better than Model 1, which justifies that the status of gastric atrophy is informative in predicting the risk of IM.

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'.

Plasma cell-free DNA methylation analysis for ovarian cancer detection: Analysis of samples from a case-control study and an ovarian cancer screening trial

Analysis of cell-free DNA methylation (cfDNAme), alone or combined with CA125, could help to detect ovarian cancers earlier and may reduce mortality. We assessed cfDNAme in regions of ZNF154, C2CD4D and WNT6 via targeted bisulfite sequencing in diagnostic and early detection (preceding diagnosis) settings. Diagnostic samples were obtained via prospective blood collection in cell-free DNA tubes in a convenience series of patients with a pelvic mass. Early detection samples were matched case-control samples derived from the UK Familial Ovarian Cancer Screening Study (UKFOCSS). In the diagnostic set (ncases  = 27, ncontrols  = 41), the specificity of cfDNAme was 97.6% (95% CI: 87.1%-99.9%). High-risk cancers were detected with a sensitivity of 80% (56.3%-94.3%). Combination of cfDNAme and CA125 resulted in a sensitivity of 94.4% (72.7%-99.9%) for high-risk cancers. Despite technical issues in the early detection set (ncases  = 29, ncontrols  = 29), the specificity of cfDNAme was 100% (88.1%-100.0%). We detected 27.3% (6.0%-61.0%) of high-risk cases with relatively lower genomic DNA (gDNA) contamination. The sensitivity rose to 33.3% (7.5%-70.1%) in samples taken <1 year before diagnosis. We detected ovarian cancer in several patients up to 1 year before diagnosis despite technical limitations associated with archival samples (UKFOCSS). Combined cfDNAme and CA125 assessment may improve ovarian cancer screening in high-risk populations, but future large-scale prospective studies will be required to validate current findings.

Estimating genome-wide DNA methylation heterogeneity with methylation patterns

BACKGROUND

In a heterogeneous population of cells, individual cells can behave differently and respond variably to the environment. This cellular diversity can be assessed by measuring DNA methylation patterns. The loci with variable methylation patterns are informative of cellular heterogeneity and may serve as biomarkers of diseases and developmental progression. Cell-to-cell methylation heterogeneity can be evaluated through single-cell methylomes or computational techniques for pooled cells. However, the feasibility and performance of these approaches to precisely estimate methylation heterogeneity require further assessment.

RESULTS

Here, we proposed model-based methods adopted from a mathematical framework originally from biodiversity, to estimate genome-wide DNA methylation heterogeneity. We evaluated the performance of our models and the existing methods with feature comparison, and tested on both synthetic datasets and real data. Overall, our methods have demonstrated advantages over others because of their better correlation with the actual heterogeneity. We also demonstrated that methylation heterogeneity offers an additional layer of biological information distinct from the conventional methylation level. In the case studies, we showed that distinct profiles of methylation heterogeneity in CG and non-CG methylation can predict the regulatory roles between genomic elements in Arabidopsis. This opens up a new direction for plant epigenomics. Finally, we demonstrated that our score might be able to identify loci in human cancer samples as putative biomarkers for early cancer detection.

CONCLUSIONS

We adopted the mathematical framework from biodiversity into three model-based methods for analyzing genome-wide DNA methylation heterogeneity to monitor cellular heterogeneity. Our methods, namely MeH, have been implemented, evaluated with existing methods, and are open to the research community.

Efficiency of CIN2+ Detection by Thyrotropin-Releasing Hormone (TRH) Site-Specific Methylation

Cervical cancer screening typically involves a Pap smear combined with high-risk human papillomavirus (hr-HPV) detection. Women with hr-HPV positivity but normal cytology, as well as those with precancerous abnormal cytology, such as low-grade squamous intraepithelial lesions (LSIL) and high-grade SIL (HSIL), are referred for colposcopy and histology examination to identify abnormal lesions, such as cervical intraepithelial neoplasia (CIN) and cervical cancer. However, in order to enhance the accuracy of detection, bioinformatics analysis of a microarray database was performed, which identified cg01009664, a methylation marker of the thyrotropin-releasing hormone (TRH). Consequently, a real-time PCR assay was developed to distinguish CIN2+ (CIN2, CIN3, and cervical cancer) from CIN2- (CIN1 and normal cervical epithelia). The real-time PCR assay utilized specific primers targeting methylated cg01009664 sites, whereas an unmethylated reaction was used to check the DNA quality. A cut-off value for the methylated reaction of Ct < 33 was established, resulting in improved precision in identifying CIN2+. In the first cohort group, the assay demonstrated a sensitivity of 93.7% and a specificity of 98.6%. In the cytology samples identified as atypical squamous cells of undetermined significance (ASC-US) and LSIL, the sensitivity and specificity for detecting CIN2+ were 95.0% and 98.9%, respectively. However, when self-collected samples from women with confirmed histology were tested, the sensitivity for CIN2+ detection dropped to 49.15%, while maintaining a specificity of 100%. Notably, the use of clinician-collected samples increased the sensitivity of TRH methylation testing. TRH methylation analysis can effectively identify women who require referral for colposcopy examinations, aiding in the detection of CIN2+.

Notch, SUMOylation, and ESR-Mediated Signalling Are the Main Molecular Pathways Showing Significantly Different Epimutation Scores between Expressing or Not Oestrogen Receptor Breast Cancer in Three Public EWAS Datasets

Oestrogen receptor expression in breast cancer (BC) cells is a marker of high cellular differentiation and allows the identification of two BC groups (ER-positive and ER-negative) that, although not completely homogeneous, differ in biological characteristics, clinical behaviour, and therapeutic options. The study, based on three publicly available EWAS (Epigenetic Wide Association Study) datasets, focuses on the comparison between these two groups of breast cancer using an epimutation score. The score is calculated not only based on the presence of the epimutation, but also on the deviation amplitude of the methylation outlier value. For each dataset, we performed a functional analysis based first on the functional gene region of each annotated gene (we aggregated the data per gene region TSS1500, TSS200, first-exon, and body-gene identified by the information from the Illumina Data Sheet), and then, we performed a pathway enrichment analysis through the REACTOME database based on the genes with the highest epimutation score. Thus, we blended our results and found common pathways for all three datasets. We found that a higher and significant epimutation score due to hypermethylation in ER-positive BC is present in the promoter region of the genes belonging to the SUMOylation pathway, the Notch pathway, the IFN-γ signalling pathway, and the deubiquitination protease pathway, while a higher and significant level of epimutation due to hypomethylation in ER-positive BC is present in the promoter region of the genes belonging to the ESR-mediated pathway. The presence of this state of promoter hypomethylation in the ESR-mediated signalling genes is consistent and coherent with an active signalling pathway mediated by oestrogen function in the group of ER-positive BC. The SUMOylation and Notch pathways are associated with BC pathogenesis and have been found to play distinct roles in the two BC subgroups. We speculated that the altered methylation profile may play a role in regulating signalling pathways with specific functions in the two subgroups of ER BC.

EpiMix is an integrative tool for epigenomic subtyping using DNA methylation

DNA methylation (DNAme) is a major epigenetic factor influencing gene expression with alterations leading to cancer and immunological and cardiovascular diseases. Recent technological advances have enabled genome-wide profiling of DNAme in large human cohorts. There is a need for analytical methods that can more sensitively detect differential methylation profiles present in subsets of individuals from these heterogeneous, population-level datasets. We developed an end-to-end analytical framework named "EpiMix" for population-level analysis of DNAme and gene expression. Compared with existing methods, EpiMix showed higher sensitivity in detecting abnormal DNAme that was present in only small patient subsets. We extended the model-based analyses of EpiMix to cis-regulatory elements within protein-coding genes, distal enhancers, and genes encoding microRNAs and long non-coding RNAs (lncRNAs). Using cell-type-specific data from two separate studies, we discover epigenetic mechanisms underlying childhood food allergy and survival-associated, methylation-driven ncRNAs in non-small cell lung cancer.

Assessing the risk of cervical neoplasia in the post-HPV vaccination era

This review is based on the recent EUROGIN scientific session: "Assessing risk of cervical cancer in the post-vaccination era," which addressed the demands of cervical intraepithelial neoplasia (CIN)/squamous intraepithelial lesion (SIL) triage now that the prevalence of vaccine-targeted oncogenic high-risk (hr) human papillomaviruses (HPVs) is decreasing. Change in the prevalence distribution of oncogenic HPV types that follows national HPV vaccination programs is setting the stage for loss of positive predictive value of conventional but possibly also new triage modalities. Understanding the contribution of the latter, most notably hypermethylation of cellular and viral genes in a new setting where most oncogenic HPV types are no longer present, requires studies on their performance in vaccinated women with CIN/SIL that are associated with nonvaccine HPV types. Lessons learned from this research may highlight the potential of cervical cells for risk prediction of all women's cancers.

HPV-induced host epigenetic reprogramming is lost upon progression to high-grade cervical intraepithelial neoplasia

The impact of a pathogen on host disease can only be studied in samples covering the entire spectrum of pathogenesis. Persistent oncogenic human papilloma virus (HPV) infection is the most common cause for cervical cancer. Here, we investigate HPV-induced host epigenome-wide changes prior to development of cytological abnormalities. Using cervical sample methylation array data from disease-free women with or without an oncogenic HPV infection, we develop the WID (Women's cancer risk identification)-HPV, a signature reflective of changes in the healthy host epigenome related to high-risk HPV strains (AUC = 0.78, 95% CI: 0.72-0.85, in nondiseased women). Looking at HPV-associated changes across disease development, HPV-infected women with minor cytological alterations (cervical intraepithelial neoplasia grade 1/2, CIN1/2), but surprisingly not those with precancerous changes or invasive cervical cancer (CIN3+), show an increased WID-HPV index, indicating the WID-HPV may reflect a successful viral clearance response absent in progression to cancer. Further investigation revealed the WID-HPV is positively associated with apoptosis (ρ = 0.48; P < .001) and negatively associated with epigenetic replicative age (ρ = -0.43; P < .001). Taken together, our data suggest the WID-HPV captures a clearance response associated with apoptosis of HPV-infected cells. This response may be dampened or lost with increased underlying replicative age of infected cells, resulting in progression to cancer.

Cell-Type Deconvolution of Bulk DNA Methylation Data with EpiSCORE

DNA methylation data generated from bulk tissue represents a mixture of many different cell types. Variation in the cell-type composition of tissues is thus a major confounder when inferring differential DNA methylation. Due to the high cost of single-cell methylome sequencing, computational methods that can dissect the cell-type heterogeneity of bulk DNA methylomes offer an efficient and cost-effective solution, especially in the context of large-scale EWAS. In this chapter, we present a step-by-step tutorial of Epigenetic cell-type deconvolution using Single-Cell Omic References (EpiSCORE), a reference-based method that leverages the high-resolution nature of single-cell RNA-Seq datasets to facilitate microdissection of bulk-tissue DNA methylomes.

Assessing Differential Variability of High-Throughput DNA Methylation Data

PURPOSE OF REVIEW

DNA methylation (DNAm) is essential to human development and plays an important role as a biomarker due to its susceptibility to environmental exposures. This article reviews the current state of statistical methods developed for differential variability analysis focusing on DNAm data.

RECENT FINDINGS

With the advent of high-throughput technologies allowing for highly reliable and cost-effective measurements of DNAm, many epigenome studies have analyzed DNAm levels to uncover biological mechanisms underlying past environmental exposures and subsequent health outcomes. These studies typically focused on detecting sites or regions which differ in their mean DNAm levels among exposure groups. However, more recent studies highlighted the importance of identifying differentially variable sites or regions as biologically relevant features. Currently, the analysis of differentially variable DNAm sites has not yet gained widespread adoption in environmental studies; yet, it is important to examine the effects of environmental exposures on inter-individual epigenetic variability. In this article, we describe six of the most widely used statistical approaches for analyzing differential variability of DNAm levels and provide a discussion of their advantages and current limitations.

DNA methylation-based detection and prediction of cervical intraepithelial neoplasia grade 3 and invasive cervical cancer with the WID™-qCIN test

BACKGROUND

Cervical screening using primary human papilloma virus (HPV) testing and cytology is being implemented in several countries. Cytology as triage for colposcopy referral suffers from several shortcomings. HPV testing overcomes some of these but lacks specificity in women under 30. Here, we aimed to develop and validate an automatable triage test that is highly sensitive and specific independently of age and sample heterogeneity, and predicts progression to CIN3+ in HPV+ patients.

RESULTS

The WID™-qCIN, assessing three regions in human genes DPP6, RALYL, and GSX1, was validated in both a diagnostic (case-control) and predictive setting (nested case-control), in a total of 761 samples. Using a predefined threshold, the sensitivity of the WID™-qCIN test was 100% and 78% to detect invasive cancer and CIN3, respectively. Sensitivity to detect CIN3+ was 65% and 83% for women < and ≥ 30 years of age. The specificity was 90%. Importantly, the WID™-qCIN test identified 52% of ≥ 30-year-old women with a cytology negative (cyt-) index sample who were diagnosed with CIN3 1-4 years after sample donation.

CONCLUSION

We identified suitable DNAme regions in an epigenome-wide discovery using HPV+ controls and CIN3+ cases and established the WID™-qCIN, a PCR-based DNAme test. The WID™-qCIN test has a high sensitivity and specificity that may outperform conventional cervical triage tests and can in an objective, cheap, and scalable fashion identify most women with and at risk of (pre-)invasive cervical cancer. However, evaluation was limited to case-control settings and future studies will assess performance and generalisability in a randomised controlled trial.

DNA methylation fingerprint of hepatocellular carcinoma from tissue and liquid biopsies

Hepatocellular carcinoma (HCC) is amongst the cancers with highest mortality rates and is the most common malignancy of the liver. Early detection is vital to provide the best treatment possible and liquid biopsies combined with analysis of circulating tumour DNA methylation show great promise as a non-invasive approach for early cancer diagnosis and monitoring with low false negative rates. To identify reliable diagnostic biomarkers of early HCC, we performed a systematic analysis of multiple hepatocellular studies and datasets comprising > 1500 genome-wide DNA methylation arrays, to define a methylation signature predictive of HCC in both tissue and cell-free DNA liquid biopsy samples. Our machine learning pipeline identified differentially methylated regions in HCC, some associated with transcriptional repression of genes related with cancer progression, that benchmarked positively against independent methylation signatures. Combining our signature of 38 DNA methylation regions, we derived a HCC detection score which confirmed the utility of our approach by identifying in an independent dataset 96% of HCC tissue samples with a precision of 98%, and most importantly successfully separated cfDNA of tumour samples from healthy controls. Notably, our risk score could identify cell-free DNA samples from patients with other tumours, including colorectal cancer. Taken together, we propose a comprehensive HCC DNA methylation fingerprint and an associated risk score for detection of HCC from tissue and liquid biopsies.

Age-Related DNA Methylation in Normal Kidney Tissue Identifies Epigenetic Cancer Risk Susceptibility Loci in the ANKRD34B and ZIC1 Genes

Both age-dependent and age-independent alteration of DNA methylation in human tissues are functionally associated with the development of many malignant and non-malignant human diseases. TCGA-KIRC data were biometrically analyzed to identify new loci with age-dependent DNA methylation that may contribute to tumor risk in normal kidney tissue. ANKRD34B and ZIC1 were evaluated as candidate genes by pyrosequencing of 539 tissues, including 239 normal autopsy, 157 histopathologically tumor-adjacent normal, and 143 paired tumor kidney samples. All candidate CpG loci demonstrated a strong correlation between relative methylation levels and age (R = 0.70−0.88, p < 2 × 10−16) and seven out of 10 loci were capable of predicting chronological age in normal kidney tissues, explaining 84% of the variance (R = 0.92). Moreover, significantly increased age-independent methylation was found for 9 out of 10 CpG loci in tumor-adjacent tissues, compared to normal autopsy tissues (p = 0.001−0.028). Comparing tumor and paired tumor-adjacent tissues revealed two patient clusters showing hypermethylation, one cluster without significant changes in methylation, and a smaller cluster demonstrating hypomethylation in the tumors (p < 1 × 10−10). Taken together, our results show the presence of additional methylation risk factors besides age for renal cancer in normal kidney tissue. Concurrent tumor-specific hypermethylation suggests a subset of these loci are candidates for epigenetic renal cancer susceptibility.

Making sense of the ageing methylome

Over time, the human DNA methylation landscape accrues substantial damage, which has been associated with a broad range of age-related diseases, including cardiovascular disease and cancer. Various age-related DNA methylation changes have been described, including at the level of individual CpGs, such as differential and variable methylation, and at the level of the whole methylome, including entropy and correlation networks. Here, we review these changes in the ageing methylome as well as the statistical tools that can be used to quantify them. We detail the evidence linking DNA methylation to ageing phenotypes and the longevity strategies aimed at altering both DNA methylation patterns and machinery to extend healthspan and lifespan. Lastly, we discuss theories on the mechanistic causes of epigenetic ageing.

Pathophysiology of Circulating Biomarkers and Relationship With Vascular Aging: A Review of the Literature From VascAgeNet Group on Circulating Biomarkers, European Cooperation in Science and Technology Action 18216

Impairment of the arteries is a product of sustained exposure to various deleterious factors and progresses with time; a phenomenon inherent to vascular aging. Oxidative stress, inflammation, the accumulation of harmful agents in high cardiovascular risk conditions, changes to the extracellular matrix, and/or alterations of the epigenetic modification of molecules, are all vital pathophysiological processes proven to contribute to vascular aging, and also lead to changes in levels of associated circulating molecules. Many of these molecules are consequently recognized as markers of vascular impairment and accelerated vascular aging in clinical and research settings, however, for these molecules to be classified as biomarkers of vascular aging, further criteria must be met. In this paper, we conducted a scoping literature review identifying thirty of the most important, and eight less important, biomarkers of vascular aging. Herein, we overview a selection of the most important molecules connected with the above-mentioned pathological conditions and study their usefulness as circulating biomarkers of vascular aging.

Inferring Functional Epigenetic Modules by Integrative Analysis of Multiple Heterogeneous Networks

Gene expression and methylation are critical biological processes for cells, and how to integrate these heterogeneous data has been extensively investigated, which is the foundation for revealing the underlying patterns of cancers. The vast majority of the current algorithms fuse gene methylation and expression into a network, failing to fully explore the relations and heterogeneity of them. To resolve these problems, in this study we define the epigenetic modules as a gene set whose members are co-methylated and co-expressed. To address the heterogeneity of data, we construct gene co-expression and co-methylation networks, respectively. In this case, the epigenetic module is characterized as a common module in multiple networks. Then, a non-negative matrix factorization-based algorithm that jointly clusters the co-expression and co-methylation networks is proposed for discovering the epigenetic modules (called Ep-jNMF). Ep-jNMF is more accurate than the baselines on the artificial data. Moreover, Ep-jNMF identifies more biologically meaningful modules. And the modules can predict the subtypes of cancers. These results indicate that Ep-jNMF is efficient for the integration of expression and methylation data.

Exposure to arsenic at different life-stages and DNA methylation meta-analysis in buccal cells and leukocytes

BACKGROUND

Arsenic (As) exposure through drinking water is a global public health concern. Epigenetic dysregulation including changes in DNA methylation (DNAm), may be involved in arsenic toxicity. Epigenome-wide association studies (EWAS) of arsenic exposure have been restricted to single populations and comparison across EWAS has been limited by methodological differences. Leveraging data from epidemiological studies conducted in Chile and Bangladesh, we use a harmonized data processing and analysis pipeline and meta-analysis to combine results from four EWAS.

METHODS

DNAm was measured among adults in Chile with and without prenatal and early-life As exposure in PBMCs and buccal cells (N = 40, 850K array) and among men in Bangladesh with high and low As exposure in PBMCs (N = 32, 850K array; N = 48, 450K array). Linear models were used to identify differentially methylated positions (DMPs) and differentially variable positions (DVPs) adjusting for age, smoking, cell type, and sex in the Chile cohort. Probes common across EWAS were meta-analyzed using METAL, and differentially methylated and variable regions (DMRs and DVRs, respectively) were identified using comb-p. KEGG pathway analysis was used to understand biological functions of DMPs and DVPs.

RESULTS

In a meta-analysis restricted to PBMCs, we identified one DMP and 23 DVPs associated with arsenic exposure; including buccal cells, we identified 3 DMPs and 19 DVPs (FDR < 0.05). Using meta-analyzed results, we identified 11 DMRs and 11 DVRs in PBMC samples, and 16 DMRs and 19 DVRs in PBMC and buccal cell samples. One region annotated to LRRC27 was identified as a DMR and DVR. Arsenic-associated KEGG pathways included lysosome, autophagy, and mTOR signaling, AMPK signaling, and one carbon pool by folate.

CONCLUSIONS

Using a two-step process of (1) harmonized data processing and analysis and (2) meta-analysis, we leverage four DNAm datasets from two continents of individuals exposed to high levels of As prenatally and during adulthood to identify DMPs and DVPs associated with arsenic exposure. Our approach suggests that standardizing analytical pipelines can aid in identifying biological meaningful signals.

An epigenomic landscape of cervical intraepithelial neoplasia and cervical cancer using single-base resolution methylome and hydroxymethylome

BACKGROUND

Cervical cancer (CC) is the second leading cause of cancer death among women worldwide. Epigenetic regulation of gene expression through DNA methylation and hydroxymethylation plays a pivotal role during tumorigenesis. In this study, to analyze the epigenomic landscape and identify potential biomarkers for CCs, we selected a series of samples from normal to cervical intra-epithelial neoplasia (CINs) to CCs and performed an integrative analysis of whole-genome bisulfite sequencing (WGBS-seq), oxidative WGBS, RNA-seq, and external histone modifications profiling data.

RESULTS

In the development and progression of CC, there were genome-wide hypo-methylation and hypo-hydroxymethylation, accompanied by local hyper-methylation and hyper-hydroxymethylation. Hydroxymethylation prefers to distribute in the CpG islands and CpG shores, as displayed a trend of gradual decline from health to CIN2, while a trend of increase from CIN3 to CC. The differentially methylated and hydroxymethylated region-associated genes both enriched in Hippo and other cancer-related signaling pathways that drive cervical carcinogenesis. Furthermore, we identified eight novel differentially methylated/hydroxymethylated-associated genes (DES, MAL, MTIF2, PIP5K1A, RPS6KA6, ANGEL2, MPP, and PAPSS2) significantly correlated with the overall survival of CC. In addition, no any correlation was observed between methylation or hydroxymethylation levels and somatic copy number variations in CINs and CCs.

CONCLUSION

Our current study systematically delineates the map of methylome and hydroxymethylome from CINs to CC, and some differentially methylated/hydroxymethylated-associated genes can be used as the potential epigenetic biomarkers in CC prognosis.

Leveraging Genetic Reports and Electronic Health Records for the Prediction of Primary Cancers: Algorithm Development and Validation Study

BACKGROUND

Precision oncology has the potential to leverage clinical and genomic data in advancing disease prevention, diagnosis, and treatment. A key research area focuses on the early detection of primary cancers and potential prediction of cancers of unknown primary in order to facilitate optimal treatment decisions.

OBJECTIVE

This study presents a methodology to harmonize phenotypic and genetic data features to classify primary cancer types and predict cancers of unknown primaries.

METHODS

We extracted genetic data elements from oncology genetic reports of 1011 patients with cancer and their corresponding phenotypical data from Mayo Clinic's electronic health records. We modeled both genetic and electronic health record data with HL7 Fast Healthcare Interoperability Resources. The semantic web Resource Description Framework was employed to generate the network-based data representation (ie, patient-phenotypic-genetic network). Based on the Resource Description Framework data graph, Node2vec graph-embedding algorithm was applied to generate features. Multiple machine learning and deep learning backbone models were compared for cancer prediction performance.

RESULTS

With 6 machine learning tasks designed in the experiment, we demonstrated the proposed method achieved favorable results in classifying primary cancer types (area under the receiver operating characteristic curve [AUROC] 96.56% for all 9 cancer predictions on average based on the cross-validation) and predicting unknown primaries (AUROC 80.77% for all 8 cancer predictions on average for real-patient validation). To demonstrate the interpretability, 17 phenotypic and genetic features that contributed the most to the prediction of each cancer were identified and validated based on a literature review.

CONCLUSIONS

Accurate prediction of cancer types can be achieved with existing electronic health record data with satisfactory precision. The integration of genetic reports improves prediction, illustrating the translational values of incorporating genetic tests early at the diagnosis stage for patients with cancer.

DNA Methylation in Ovarian Tumors-a Comparison Between Fresh Tissue and FFPE Samples

Among women, ovarian cancer (OC) is one of the most severe forms of malignancy, accounting for a low 5-year survival rate, of approximately 52%. Early symptoms are unspecific and hence hard to detect. The origin of OC and its subtypes are still unclear, underlying the need for efficient diagnostic biomarkers. In that regard, epigenetics studies are emerging in cancer diagnostics, with encouraging outcomes. Among them, DNA methylation profiling has shown that the origins of the cancer epigenome are associated with molecular factors that are crucial to carcinogenesis, such as regulation of oncogenes and tumor suppressors. Furthermore, those events have been detected in abnormal cell morphology before neoplastic formation, indicating its potential crucial use in the OC diagnostics in the future. Nonetheless, studies are limited, and whether methylation analysis can be performed optimally in formalin-fixed paraffin-embedded (FFPE) preparations of OC cases is still elusive. In the present report, we investigated the performance of DNA methylation analysis in FFPE samples, compared to their matched fresh frozen tissue in a small cohort of OC samples. We found that the overall DNA methylation profile in FFPE tissue showed high concordance to that found in fresh frozen tissue, and accounting for the small cohort size, the differentially methylated sites found primarily in frozen tissue, compared to benign samples, were also reproducible in FFPE. Overall, by using samples from our current clinical setting of tissue preservation, these preliminary observations might provide insights into the clinical use of FFPE tissues in methylation studies without critically compromising the outcome.

Human methylome variation across Infinium 450K data on the Gene Expression Omnibus

While DNA methylation (DNAm) is the most-studied epigenetic mark, few recent studies probe the breadth of publicly available DNAm array samples. We collectively analyzed 35 360 Illumina Infinium HumanMethylation450K DNAm array samples published on the Gene Expression Omnibus. We learned a controlled vocabulary of sample labels by applying regular expressions to metadata and used existing models to predict various sample properties including epigenetic age. We found approximately two-thirds of samples were from blood, one-quarter were from brain and one-third were from cancer patients. About 19% of samples failed at least one of Illumina's 17 prescribed quality assessments; signal distributions across samples suggest modifying manufacturer-recommended thresholds for failure would make these assessments more informative. We further analyzed DNAm variances in seven tissues (adipose, nasal, blood, brain, buccal, sperm and liver) and characterized specific probes distinguishing them. Finally, we compiled DNAm array data and metadata, including our learned and predicted sample labels, into database files accessible via the recountmethylation R/Bioconductor companion package. Its vignettes walk the user through some analyses contained in this paper.

DNA methylation and cancer incidence: lymphatic-hematopoietic versus solid cancers in the Strong Heart Study

BACKGROUND

Epigenetic alterations may contribute to early detection of cancer. We evaluated the association of blood DNA methylation with lymphatic-hematopoietic cancers and, for comparison, with solid cancers. We also evaluated the predictive ability of DNA methylation for lymphatic-hematopoietic cancers.

METHODS

Blood DNA methylation was measured using the Illumina Infinium methylationEPIC array in 2324 Strong Heart Study participants (41.4% men, mean age 56 years). 788,368 CpG sites were available for differential DNA methylation analysis for lymphatic-hematopoietic, solid and overall cancers using elastic-net and Cox regression models. We conducted replication in an independent population: the Framingham Heart Study. We also analyzed differential variability and conducted bioinformatic analyses to assess for potential biological mechanisms.

RESULTS

Over a follow-up of up to 28 years (mean 15), we identified 41 lymphatic-hematopoietic and 394 solid cancer cases. A total of 126 CpGs for lymphatic-hematopoietic cancers, 396 for solid cancers, and 414 for overall cancers were selected as predictors by the elastic-net model. For lymphatic-hematopoietic cancers, the predictive ability (C index) increased from 0.58 to 0.87 when adding these 126 CpGs to the risk factor model in the discovery set. The association was replicated with hazard ratios in the same direction in 28 CpGs in the Framingham Heart Study. When considering the association of variability, rather than mean differences, we found 432 differentially variable regions for lymphatic-hematopoietic cancers.

CONCLUSIONS

This study suggests that differential methylation and differential variability in blood DNA methylation are associated with lymphatic-hematopoietic cancer risk. DNA methylation data may contribute to early detection of lymphatic-hematopoietic cancers.

ZNF471 modulates EMT and functions as methylation regulated tumor suppressor with diagnostic and prognostic significance in cervical cancer

Cervical cancer (CC) is a leading cause of cancer-related death among women in developing countries. However, the underlying mechanisms and molecular targets for therapy remain to be fully understood. We investigated the epigenetic regulation, biological functions, and clinical utility of zinc-finger protein 471 (ZNF471) in CC. Analysis of cervical tissues and five independent public datasets of CC showed significant hypermethylation of the ZNF471 gene promoter. In CC cell lines, promoter DNA methylation was inversely correlated with ZNF471 expression. The sensitivity and specificity of the ZNF471 hypermethylation for squamous intraepithelial lesion (SIL) vs tumor and normal vs tumor was above 85% with AUC of 0.937. High methylation and low ZNF471 expression predicted poor overall and recurrence-free survival. We identified -686 to +114 bp as ZNF471 promoter, regulated by methylation using transient transfection and luciferase assays. The promoter CpG site methylation of ZNF471 was significantly different among cancer types and tumor grades. Gal4-based heterologous luciferase reporter gene assays revealed that ZNF471 acts as a transcriptional repressor. The retroviral mediated overexpression of ZNF471 in SiHa and CaSki cells inhibited growth, proliferation, cell migration, invasion; delayed cell cycle progression in vitro by increasing cell doubling time; and reduced tumor growth in vivo in nude mice. ZNF471 overexpression inhibited key members of epithelial-mesenchymal transition (EMT), Wnt, and PI3K-AKT signaling pathways. ZNF471 inhibited EMT by directly targeting vimentin as analyzed by bioinformatic analysis, ChIP-PCR, and western blotting. Thus, ZNF471 CpG specific promoter methylation may determine the prognosis of CC and could function as a potential tumor suppressor by targeting EMT signaling.

Targeting aberrant DNA methylation in mesenchymal stromal cells as a treatment for myeloma bone disease

Multiple myeloma (MM) progression and myeloma-associated bone disease (MBD) are highly dependent on bone marrow mesenchymal stromal cells (MSCs). MM-MSCs exhibit abnormal transcriptomes, suggesting the involvement of epigenetic mechanisms governing their tumor-promoting functions and prolonged osteoblast suppression. Here, we identify widespread DNA methylation alterations of bone marrow-isolated MSCs from distinct MM stages, particularly in Homeobox genes involved in osteogenic differentiation that associate with their aberrant expression. Moreover, these DNA methylation changes are recapitulated in vitro by exposing MSCs from healthy individuals to MM cells. Pharmacological targeting of DNMTs and G9a with dual inhibitor CM-272 reverts the expression of hypermethylated osteogenic regulators and promotes osteoblast differentiation of myeloma MSCs. Most importantly, CM-272 treatment prevents tumor-associated bone loss and reduces tumor burden in a murine myeloma model. Our results demonstrate that epigenetic aberrancies mediate the impairment of bone formation in MM, and its targeting by CM-272 is able to reverse MBD.

Mesenchymal stromal cells (MSCs) have been shown to support multiple myeloma (MM) development. Here, MSCs isolated from the bone marrow of MM patients are shown to have altered DNA methylation patterns and a methyltransferase inhibitor reverts MM-associated bone loss and reduces tumour burden in MM murine models.

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.

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.

Construction and Validation of Novel Diagnostic and Prognostic DNA Methylation Signatures for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent life-threatening human cancers and the leading cause of cancer-related mortality, with increased global incidence within the last decade. Identification of effective diagnostic and prognostic biomarkers would enable reliable risk stratification and efficient screening of high-risk patients, thereby facilitating clinical decision-making. Herein, we performed a comprehensive, robust DNA methylation analysis based on genome-wide DNA methylation profiling. We constructed a diagnostic signature with five DNA methylation markers, which precisely distinguished HCC patients from normal controls. Cox regression and LASSO analysis were applied to construct a prognostic signature with four DNA methylation markers. A one-to-one correlation analysis was carried out between genes of the whole genome and our prognostic signature. Exploration of the biological function and the role of the underlying significantly correlated genes was conducted. A mixed dataset of 463 HCC patients and 253 normal controls, derived from six independent datasets, was used to valid the diagnostic signature. Results showed a specificity of 96.84% and sensitivity of 96.77%. Class scores for the diagnostic signature were significantly different between normal controls, individuals with liver diseases, and HCC patients. The present signature has the potential to serve as a biomarker to monitor health in normal controls. Additionally, HCC patients were successfully separated into low-risk and high-risk groups by the prognostic signature, with a better prognosis for patients in the low-risk group. Kaplan-Meier and ROC analysis confirmed that the prognostic signature performed well. We found eight of the top ten genes to positively correlate with risk scores of the prognostic signature, and to be involved in cell cycle regulation. This eight-gene panel also served as a prognostic signature. The robust evidence presented in this study therefore demonstrates the effectiveness of the prognostic signature. In summary, we constructed diagnostic and prognostic signatures, which have potential for use in diagnosis, surveillance, and prognostic prediction for HCC patients. Eight genes that were significantly and positively correlated with the prognostic signature were strongly associated with cell cycle processes. Therefore, the prognostic signature can be used as a guide by which to measure responsiveness to cell-cycle-targeting agents.

A Linear Regression and Deep Learning Approach for Detecting Reliable Genetic Alterations in Cancer Using DNA Methylation and Gene Expression Data

DNA methylation change has been useful for cancer biomarker discovery, classification, and potential treatment development. So far, existing methods use either differentially methylated CpG sites or combined CpG sites, namely differentially methylated regions, that can be mapped to genes. However, such methylation signal mapping has limitations. To address these limitations, in this study, we introduced a combinatorial framework using linear regression, differential expression, deep learning method for accurate biological interpretation of DNA methylation through integrating DNA methylation data and corresponding TCGA gene expression data. We demonstrated it for uterine cervical cancer. First, we pre-filtered outliers from the data set and then determined the predicted gene expression value from the pre-filtered methylation data through linear regression. We identified differentially expressed genes (DEGs) by Empirical Bayes test using Limma. Then we applied a deep learning method, "nnet" to classify the cervical cancer label of those DEGs to determine all classification metrics including accuracy and area under curve (AUC) through 10-fold cross validation. We applied our approach to uterine cervical cancer DNA methylation dataset (NCBI accession ID: GSE30760, 27,578 features covering 63 tumor and 152 matched normal samples). After linear regression and differential expression analysis, we obtained 6287 DEGs with false discovery rate (FDR) <0.001. After performing deep learning analysis, we obtained average classification accuracy 90.69% (±1.97%) of the uterine cervical cancerous labels. This performance is better than that of other peer methods. We performed in-degree and out-degree hub gene network analysis using Cytoscape. We reported five top in-degree genes (PAIP2, GRWD1, VPS4B, CRADD and LLPH) and five top out-degree genes (MRPL35, FAM177A1, STAT4, ASPSCR1 and FABP7). After that, we performed KEGG pathway and Gene Ontology enrichment analysis of DEGs using tool WebGestalt(WEB-based Gene SeT AnaLysis Toolkit). In summary, our proposed framework that integrated linear regression, differential expression, deep learning provides a robust approach to better interpret DNA methylation analysis and gene expression data in disease study.

Stochastic Epigenetic Mutations Are Associated with Risk of Breast Cancer, Lung Cancer, and Mature B-cell Neoplasms

BACKGROUND

Age-related epigenetic dysregulations are associated with several diseases, including cancer. The number of stochastic epigenetic mutations (SEM) has been suggested as a biomarker of life-course accumulation of exposure-related DNA damage; however, the predictive role of SEMs in cancer has seldom been investigated.

METHODS

A SEM, at a given CpG site, was defined as an extreme outlier of DNA methylation value distribution across individuals. We investigated the association of the total number of SEMs with the risk of eight cancers in 4,497 case-control pairs nested in three prospective cohorts. Furthermore, we investigated whether SEMs were randomly distributed across the genome or enriched in functional genomic regions.

RESULTS

In the three-study meta-analysis, the estimated ORs per one-unit increase in log(SEM) from logistic regression models adjusted for age and cancer risk factors were 1.25; 95% confidence interval (CI), 1.11-1.41 for breast cancer, and 1.23; 95% CI, 1.07-1.42 for lung cancer. In the Melbourne Collaborative Cohort Study, the OR for mature B-cell neoplasm was 1.46; 95% CI, 1.25-1.71. Enrichment analyses indicated that SEMs frequently occur in silenced genomic regions and in transcription factor binding sites regulated by EZH2 and SUZ12 (P < 0.0001 and P = 0.0005, respectively): two components of the polycomb repressive complex 2 (PCR2). Finally, we showed that PCR2-specific SEMs are generally more stable over time compared with SEMs occurring in the whole genome.

CONCLUSIONS

The number of SEMs is associated with a higher risk of different cancers in prediagnostic blood samples.

IMPACT

We identified a candidate biomarker for cancer early detection, and we described a carcinogenesis mechanism involving PCR2 complex proteins worthy of further investigations.

A comparison of epigenetic mitotic-like clocks for cancer risk prediction

Background

DNA methylation changes that accrue in the stem cell pool of an adult tissue in line with the cumulative number of cell divisions may contribute to the observed variation in cancer risk among tissues and individuals. Thus, the construction of epigenetic “mitotic” clocks that can measure the lifetime number of stem cell divisions is of paramount interest.

Methods

Building upon a dynamic model of DNA methylation gain in unmethylated CpG-rich regions, we here derive a novel mitotic clock (“epiTOC2”) that can directly estimate the cumulative number of stem cell divisions in a tissue. We compare epiTOC2 to a different mitotic model, based on hypomethylation at solo-WCGW sites (“HypoClock”), in terms of their ability to measure mitotic age of normal adult tissues and predict cancer risk.

Results

Using epiTOC2, we estimate the intrinsic stem cell division rate for different normal tissue types, demonstrating excellent agreement (Pearson correlation = 0.92, R² = 0.85, P = 3e−6) with those derived from experiment. In contrast, HypoClock’s estimates do not (Pearson correlation = 0.30, R² = 0.09, P = 0.29). We validate these results in independent datasets profiling normal adult tissue types. While both epiTOC2 and HypoClock correctly predict an increased mitotic rate in cancer, epiTOC2 is more robust and significantly better at discriminating preneoplastic lesions characterized by chronic inflammation, a major driver of tissue turnover and cancer risk. Our data suggest that DNA methylation loss at solo-WCGWs is significant only when cells are under high replicative stress and that epiTOC2 is a better mitotic age and cancer risk prediction model for normal adult tissues.

Conclusions

These results have profound implications for our understanding of epigenetic clocks and for developing cancer risk prediction or early detection assays. We propose that measurement of DNAm at the 163 epiTOC2 CpGs in adult pre-neoplastic lesions, and potentially in serum cell-free DNA, could provide the basis for building feasible pre-diagnostic or cancer risk assays. epiTOC2 is freely available from 10.5281/zenodo.2632938

Establishment of permutation for cancer risk estimation in the urothelium based on genome-wide DNA methylation analysis

The aim of this study was to establish permutation for cancer risk estimation in the urothelium. Twenty-six samples of normal control urothelium obtained from patients without urothelial carcinomas (C), 47 samples of non-cancerous urothelium without noticeable morphological changes obtained from patients with urothelial carcinomas (N), and 46 samples of the corresponding cancerous tissue (T) in the learning cohort and 64 N samples in the validation cohort, i.e. 183 tissue samples in total, were analyzed. Genome-wide DNA methylation analysis was performed using the Infinium HumanMethylation 450K BeadChip, and DNA methylation levels were verified using pyrosequencing and MassARRAY. Amplicon sequencing was performed using the GeneRead DNAseq Targeted Panels V2. Although N samples rarely showed genetic mutations or copy number alterations, they showed DNA methylation alterations at 2502 CpG sites compared to C samples, and such alterations were inherited by or strengthened in T samples, indicating that DNA methylation alterations may participate in field cancerization in the urothelium. Receiver operating characteristic curve analysis confirmed the feasibility of cancer risk estimation to identify urothelium at the precancerous stage by DNA methylation quantification. Cancer risk estimation permutation was established using a combination of two marker CpG loci on the HOXC4, TENM3 and TLR1 genes (sensitivity and specificity 96-100%). Among them, the diagnostic impact of 10 patterns of permutation was successfully validated in the validation cohort (sensitivity and specificity 94-98%). These data suggest that cancer risk estimation using procedures such as urine tests during health checkups might become applicable for clinical use.

The DNA methylome of inflammatory bowel disease (IBD) reflects intrinsic and extrinsic factors in intestinal mucosal cells

Abnormal DNA methylation has been described in human inflammatory conditions of the gastrointestinal tract, such as inflammatory bowel disease (IBD). As other complex diseases, IBD results from the balance between genetic predisposition and environmental exposures. As such, DNA methylation may be the consequence (and potential effector) of both, genetic susceptibility variants and/or environmental signals such as cytokine exposure. We attempted to discern between these two non-excluding possibilities by performing a combined analysis of published DNA methylation data in intestinal mucosal cells of IBD and control samples. We identified abnormal DNA methylation at different levels: deviation from mean methylation signals at site and region levels, and differential variability. A fraction of such changes is associated with genetic polymorphisms linked to IBD susceptibility. In addition, by comparing with another intestinal inflammatory condition (i.e., coeliac disease) we propose that aberrant DNA methylation can also be the result of unspecific processes such as chronic inflammation. Our characterization suggests that IBD methylomes combine intrinsic and extrinsic responses in intestinal mucosal cells, and could point to knowledge-based biomarkers of IBD detection and progression.

Associations of annual ambient PM2.5 components with DNAm PhenoAge acceleration in elderly men: The Normative Aging Study

Current studies indicate that long-term exposure to ambient fine particulate matter (PM_2.5) is related with global mortality, yet no studies have explored relationships of PM_2.5 and its species with DNAm PhenoAge acceleration (DNAmPhenoAccel), a new epigenetic biomarker of phenotypic age. We identified which PM_2.5 species had association with DNAmPhenoAccel in a one-year exposure window in a longitudinal cohort. We collected whole blood samples from 683 elderly men in the Normative Aging Study between 1999 and 2013 (n = 1254 visits). DNAm PhenoAge was calculated using 513 CpGs retrieved from the Illumina Infinium HumanMethylation450 BeadChip. Daily concentrations of PM_2.5 species were measured at a fixed air-quality monitoring site and one-year moving averages were computed. Linear mixed-effect (LME) regression and Bayesian kernel machine (BKM) regression were used to estimate the associations. The covariates included chronological age, body mass index (BMI), cigarette pack years, smoking status, estimated cell types, batch effects etc. Benjamini-Hochberg false discovery rate at a 5% false positive threshold was used to adjust for multiple comparison. During the study period, the mean DNAm PhenoAge and chronological age in our subjects were 68 and 73 years old, respectively. Using LME model, only lead and calcium were significantly associated with DNAmPhenoAccel. For example, an interquartile range (IQR, 0.0011 μg/m³) increase in lead was associated with a 1.29-year [95% confidence interval (CI): 0.47, 2.11] increase in DNAmPhenoAccel. Using BKM model, we selected PM_2.5, lead, and silicon to be predictors for DNAmPhenoAccel. A subsequent LME model showed that only lead had significant effect on DNAmPhenoAccel: 1.45-year (95% CI: 0.46, 2.46) increase in DNAmPhenoAccel following an IQR increase in one-year lead. This is the first study that investigates long-term effects of PM_2.5 components on DNAmPhenoAccel. The results demonstrate that lead and calcium contained in PM_2.5 was robustly associated with DNAmPhenoAccel.

Epigenetic age acceleration of cervical squamous cell carcinoma converged to human papillomavirus 16/18 expression, immunoactivation, and favourable prognosis

BACKGROUND

Ageing-associated molecular changes have been assumed to trigger malignant transformations and the epigenetic clock, and the DNA methylation age has been shown to be highly correlated with chronological age. However, the associations between the epigenetic clock and cervical squamous cell carcinoma (CSCC) prognosis, other molecular characteristics, and clinicopathological features have not been systematically investigated. To this end, we computed the DNA methylation (DNAm) age of 252 CSCC patients and 200 normal samples from TCGA and three external cohorts by using the Horvath clock model. We characterized the differences in human papillomavirus (HPV) 16/18 expression, pathway activity, genomic alteration, and chemosensitivity between two DNAm age subgroups. We then used Cox proportional hazards regression and restricted cubic spline (RCS) analysis to assess the prognostic value of epigenetic acceleration.

RESULTS

DNAm age was significantly associated with chronological age, but it was differentiated between tumour and normal tissue (P < 0.001). Two DNAm age groups, i.e. DNAmAge-ACC and DNAmAge-DEC, were identified; the former had high expression of the E6/E7 oncoproteins of HPV16/18 (P < 0.05), an immunoactive phenotype (all FDRs < 0.05 in enrichment analysis), CpG island hypermethylation (P < 0.001), and lower mutation load (P = 0.011), including for TP53 (P = 0.002). When adjusted for chronological age and tumour stage, every 10-year increase in DNAm age was associated with a 12% decrease in fatality (HR 0.88, 95% CI 0.78-0.99, P = 0.03); DNAmAge-ACC had a 41% lower mortality risk and 47% lower progression rate than DNAmAge-DEC and was more likely to benefit from chemotherapy. RCS revealed a positive non-linear association between DNAm age and both mortality and progression risk (both, P < 0.05).

CONCLUSIONS

DNAm age is an independent predictor of CSCC prognosis. Better prognosis, overexpression of HPV E6/E7 oncoproteins, and higher enrichment of immune signatures were observed in DNAmAge-ACC tumours.

Association between dense PAX1 promoter methylation and HPV16 infection in cervical squamous epithelial neoplasms of Xin Jiang Uyghur and Han women

DNA methylation is an epigenetic alteration that may lead to carcinogenesis by silencing key tumor suppressor genes. Hypermethylation of the paired box gene 1 (PAX1) promoter is important in cervical cancer development. Here, PAX1 methylation levels were compared between Uyghur and Han patients with cervical lesions. Data on PAX1 methylation in different cervical lesions were obtained from the Gene Expression Omnibus (GEO) database, whereas data on survival and PAX1 mRNA expression in invasive cervical cancer (ICC) were retrieved from the Cancer Genome Atlas (TCGA) database. MassARRAY spectrometry was used to detect methylation of 19 CpG sites in the promoter region of PAX1, whereas gene mass spectrograms were drawn by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry. Human papillomavirus (HPV) 16 infection was detected by polymerase chain reaction. PAX1 methylation in high-grade squamous intraepithelial lesion (HSIL) and ICC was significantly higher than in normal tissues. PAX1 hypermethylation was associated with poor prognosis and reduced transcription. ICC-specific PAX1 promoter methylation involved distinct CpG sites in Uyghur and Han patients HPV16 infection in HSIL and ICC patient was significantly higher than in normal women (p < 0.05). Our study revealed a strong association between PAX1 methylation and the development of cervical cancer. Moreover, hypermethylation of distinct CpG sites may induce HSIL transformation into ICC in both Uyghur and Han patients. Our results suggest the existence of ethnic differences in the genetic susceptibility to cervical cancer. Finally, PAX1 methylation and HPV infection exhibited synergistic effects on cervical carcinogenesis.

Potential of Human Nucleus Pulposus-Like Cells Derived From Umbilical Cord to Treat Degenerative Disc Disease

BACKGROUND

Degenerative disc disease (DDD) is a common spinal disorder that manifests with neck and lower back pain caused by the degeneration of intervertebral discs (IVDs). Currently, there is no treatment to cure this debilitating ailment.

OBJECTIVE

To investigate the potential of nucleus pulposus (NP)-like cells (NPCs) derived from human umbilical cord mesenchymal stem cells (MSCs) to restore degenerated IVDs using a rabbit DDD model.

METHODS

NPCs differentiated from MSCs were characterized using quantitative real-time reverse transcription polymerase chain reaction and immunocytochemical analysis. MSCs and NPCs were labeled with fluorescent dye, PKH26, and transplanted into degenerated IVDs of a rabbit model of DDD (n = 9 each). Magnetic resonance imaging of the IVDs was performed before and after IVD degeneration, and following cell transplantation. IVDs were extracted 8 wk post-transplantation and analyzed by various biochemical, immunohistological, and molecular techniques.

RESULTS

NPC derivatives of MSCs expressed known NP-specific genes, SOX9, ACAN, COL2, FOXF1, and KRT19. Transplanted cells survived, dispersed, and integrated into the degenerated IVDs. IVDs augmented with NPCs showed significant improvement in the histology, cellularity, sulfated glycosaminoglycan and water contents of the NP. In addition, expression of human genes, SOX9, ACAN, COL2, FOXF1, KRT19, PAX6, CA12, and COMP, as well as proteins, SOX9, ACAN, COL2, and FOXF1, suggest NP biosynthesis due to transplantation of NPCs. Based on these results, a molecular mechanism for NP regeneration was proposed.

CONCLUSION

The findings of this study demonstrating feasibility and efficacy of NPCs to regenerate NP should spur interest for clinical studies to treat DDD using cell therapy.

Validation of an epigenetic field of susceptibility to detect significant prostate cancer from non-tumor biopsies

Background

An epigenetic field of cancer susceptibility exists for prostate cancer (PC) that gives rise to multifocal disease in the peripheral prostate. In previous work, genome-wide DNA methylation profiling identified altered regions in the normal prostate tissue of men with PC. In the current multicenter study, we examined the predictive strength of a panel of loci to detect cancer presence and grade in patients with negative biopsy tissue.

Results

Four centers contributed benign prostate biopsy tissues blocks from 129 subjects that were either tumor associated (TA, Grade Group [GG] ≥ 2, n = 77) or non-tumor associated (NTA, n = 52). Biopsies were analyzed using pyrosequencing for DNA methylation encompassing CpG loci near CAV1, EVX1, FGF1, NCR2, PLA2G16, and SPAG4 and methylation differences were detected within all gene regions (p < 0.05). A multiplex regression model for biomarker performance incorporating a gene combination discriminated TA from NTA tissues (area under the curve [AUC] 0.747, p = 0.004). A multiplex model incorporating all the above genes and clinical information (PSA, age) identified patients with GG ≥ 2 PC (AUC 0.815, p < 0.0001). In patients with cancer, increased variation in gene methylation levels occurs between biopsies across the prostate.

Conclusions

A widespread epigenetic field defect is utilized to detect GG ≥ 2 PC in patients with histologically negative biopsies. These alterations in non-tumor cells display increased heterogeneity of methylation extent and are spatially distant from tumor foci. These findings have the potential to decrease the need for repeated prostate biopsy.

Pan-Cancer Landscape of Aberrant DNA Methylation across Human Tumors

The discovery of cancer-associated alterations has primarily focused on genetic variants. Nonetheless, altered epigenomes contribute to deregulate transcription and promote oncogenic pathways. Here, we designed an algorithmic approach (RESET) to identify aberrant DNA methylation and associated cis-transcriptional changes across >6,000 human tumors. Tumors exhibiting mutations of chromatin remodeling factors and Wnt signaling displayed DNA methylation instability, characterized by numerous hyper- and hypo-methylated loci. Most silenced and enhanced genes coalesced in specific pathways including apoptosis, DNA repair, and cell metabolism. Cancer-germline antigens (CG) were frequently epigenomically enhanced and their expression correlated with response to anti-PD-1, but not anti-CTLA4, in skin melanoma. Finally, we demonstrated the potential of our approach to explore DNA methylation changes in pediatric tumors, which frequently lack genetic drivers and exhibit epigenomic modifications. Our results provide a pan-cancer map of aberrant DNA methylation to inform functional and therapeutic studies.

Non-monotone transformation of biomarkers to improve diagnostic and screening accuracy in a DNA methylation study with trichotomous phenotypes

We propose a non-monotone transformation to biomarkers in order to improve the diagnostic and screening accuracy. The proposed quadratic transformation only involves modeling the distribution means and variances of the biomarkers and is therefore easy to implement in practice. Mathematical justification was rigorously established to support the validity of the proposed transformation. We conducted extensive simulation studies to assess the performance of the proposed method and compared the new method with the traditional methods. Case studies on real biomedical and epigenetics data were provided to illustrate the proposed transformation. In particular, the proposed method improved the AUC values for a large number of markers in a DNA methylation study and consequently led to the identification of greater number of important biomarkers and biologically meaningful genetic pathways.

Epigenetic Biomarkers in the Management of Ovarian Cancer: Current Prospectives

Ovarian cancer (OC) causes significant morbidity and mortality as neither detection nor screening of OC is currently feasible at an early stage. Difficulty to promptly diagnose OC in its early stage remains challenging due to non-specific symptoms in the early-stage of the disease, their presentation at an advanced stage and poor survival. Therefore, improved detection methods are urgently needed. In this article, we summarize the potential clinical utility of epigenetic signatures like DNA methylation, histone modifications, and microRNA dysregulation, which play important role in ovarian carcinogenesis and discuss its application in development of diagnostic, prognostic, and predictive biomarkers. Molecular characterization of epigenetic modification (methylation) in circulating cell free tumor DNA in body fluids offers novel, non-invasive approach for identification of potential promising cancer biomarkers, which can be performed at multiple time points and probably better reflects the prevailing molecular profile of cancer. Current status of epigenetic research in diagnosis of early OC and its management are discussed here with main focus on potential diagnostic biomarkers in tissue and body fluids. Rapid and point of care diagnostic applications of DNA methylation in liquid biopsy has been precluded as a result of cumbersome sample preparation with complicated conventional methods of isolation. New technologies which allow rapid identification of methylation signatures directly from blood will facilitate sample-to answer solutions thereby enabling next-generation point of care molecular diagnostics. To date, not a single epigenetic biomarker which could accurately detect ovarian cancer at an early stage in either tissue or body fluid has been reported. Taken together, the methodological drawbacks, heterogeneity associated with ovarian cancer and non-validation of the clinical utility of reported potential biomarkers in larger ovarian cancer populations has impeded the transition of epigenetic biomarkers from lab to clinical settings. Until addressed, clinical implementation as a diagnostic measure is a far way to go.

Developing and validating the Personal Risk of Oncogenic Human Papillomavirus infection score in US Women

BACKGROUND

Determining risk scores for genital high-risk human papillomavirus (HRHPV) infection in women will support more efficient cervical cancer screening strategies.

OBJECTIVE

We developed and validated point scores to predict the likelihood of any genital HRHPV infection in women.

METHODS

We conducted the cross-sectional analysis in 2017 and used data from the 2005-14 US National Health and Nutrition Examination Survey (7337 women aged 25-59 years; 6300 women aged 30-59 years). Predictors were reproductive health practices, risk behaviors and demographic variables. The outcome was a positive result for any of the 21 genital HRHPV genotypes. The 2005-12 cohorts were used as training and testing sets to develop scores that best classified women into three risk groups: low risk (<20%), average risk (20-30%) and high risk (>30%). The 2013-14 cohort was used to validate the final scores.

RESULTS

Two-point scores with six self-reported variables were created to predict any HRHPV risks for the two age groups: the Personal Risk of Oncogenic HPV (PRO-HPV25) for women aged 25-59 years old and PRO-HPV30 for women aged 30-59 years old. The scores were successfully prospectively validated, with good calibration with regards to the predicted and observed rates of HRHPV infection. The scores had fair discrimination (c-statistics: 0.67-0.68).

CONCLUSION

The PRO-HPV risk scores can identify groups at low, average and high risk of genital HRHPV infection. This information can be used to prioritize women for cervical cancer screening in low-resource settings or to personalize screening intervals.

Longitudinal study of leukocyte DNA methylation and biomarkers for cancer risk in older adults

Background

Changes in DNA methylation over the course of life may provide an indicator of risk for cancer. We explored longitudinal changes in CpG methylation from blood leukocytes, and likelihood of future cancer diagnosis.

Methods

Peripheral blood samples were obtained at baseline and at follow-up visit from 20 participants in the Health, Aging and Body Composition prospective cohort study. Genome-wide CpG methylation was assayed using the Illumina Infinium Human MethylationEPIC (HM850K) microarray.

Results

Global patterns in DNA methylation from CpG-based analyses showed extensive changes in cell composition over time in participants who developed cancer. By visit year 6, the proportion of CD8+ T-cells decreased (p-value = 0.02), while granulocytes cell levels increased (p-value = 0.04) among participants diagnosed with cancer compared to those who remained cancer-free (cancer-free vs. cancer-present: 0.03 ± 0.02 vs. 0.003 ± 0.005 for CD8+ T-cells; 0.52 ± 0.14 vs. 0.66 ± 0.09 for granulocytes). Epigenome-wide analysis identified three CpGs with suggestive p-values ≤10^− 5 for differential methylation between cancer-free and cancer-present groups, including a CpG located in MTA3, a gene linked with metastasis. At a lenient statistical threshold (p-value ≤3 × 10^− 5), the top 10 cancer-associated CpGs included a site near RPTOR that is involved in the mTOR pathway, and the candidate tumor suppressor genes REC8, KCNQ1, and ZSWIM5. However, only the CpG in RPTOR (cg08129331) was replicated in an independent data set. Analysis of within-individual change from baseline to Year 6 found significant correlations between the rates of change in methylation in RPTOR, REC8 and ZSWIM5, and time to cancer diagnosis.

Conclusion

The results show that changes in cellular composition explains much of the cross-sectional and longitudinal variation in CpG methylation. Additionally, differential methylation and longitudinal dynamics at specific CpGs could provide powerful indicators of cancer development and/or progression. In particular, we highlight CpG methylation in the RPTOR gene as a potential biomarker of cancer that awaits further validation.

Electronic supplementary material

The online version of this article (10.1186/s40364-019-0161-3) contains supplementary material, which is available to authorized users.

pwrEWAS: a user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS)

BACKGROUND

When designing an epigenome-wide association study (EWAS) to investigate the relationship between DNA methylation (DNAm) and some exposure(s) or phenotype(s), it is critically important to assess the sample size needed to detect a hypothesized difference with adequate statistical power. However, the complex and nuanced nature of DNAm data makes direct assessment of statistical power challenging. To circumvent these challenges and to address the outstanding need for a user-friendly interface for EWAS power evaluation, we have developed pwrEWAS.

RESULTS

The current implementation of pwrEWAS accommodates power estimation for two-group comparisons of DNAm (e.g. case vs control, exposed vs non-exposed, etc.), where methylation assessment is carried out using the Illumina Human Methylation BeadChip technology. Power is calculated using a semi-parametric simulation-based approach in which DNAm data is randomly generated from beta-distributions using CpG-specific means and variances estimated from one of several different existing DNAm data sets, chosen to cover the most common tissue-types used in EWAS. In addition to specifying the tissue type to be used for DNAm profiling, users are required to specify the sample size, number of differentially methylated CpGs, effect size(s) (Δβ), target false discovery rate (FDR) and the number of simulated data sets, and have the option of selecting from several different statistical methods to perform differential methylation analyses. pwrEWAS reports the marginal power, marginal type I error rate, marginal FDR, and false discovery cost (FDC). Here, we demonstrate how pwrEWAS can be applied in practice using a hypothetical EWAS. In addition, we report its computational efficiency across a variety of user settings.

CONCLUSION

Both under- and overpowered studies unnecessarily deplete resources and even risk failure of a study. With pwrEWAS, we provide a user-friendly tool to help researchers circumvent these risks and to assist in the design and planning of EWAS.

AVAILABILITY

The web interface is written in the R statistical programming language using Shiny (RStudio Inc., 2016) and is available at https://biostats-shinyr.kumc.edu/pwrEWAS/ . The R package for pwrEWAS is publicly available at GitHub ( https://github.com/stefangraw/pwrEWAS ).

DNA methylation variability in Alzheimer's disease

DNA methylation plays a critical role in brain aging and Alzheimer's disease (AD). While prior studies have largely focused on testing mean DNA methylation, DNA methylation instability (quantified by DNA methylation variability) may also affect disease susceptibility. Using DNA methylation data collected by the Religious Orders Study and the Rush Memory and Aging Project, we identified 249 and 115 variably methylated probes (VMPs) associated with amyloid-β and neurofibrillary tangles, respectively. These VMPs clustered into 133 and 14 regions, respectively. Notably, we found that most of these VMPs did not overlap with differentially methylated probes, indicating that VMPs and differentially methylated probes may capture different sets of genes associated with AD pathology. Overall, our results demonstrated that DNA methylation instability affects AD neuropathology and highlights the importance of testing methylation variability in epigenetic research.