The dynamics and prognostic potential of DNA methylation changes at stem cell gene loci in women's cancer

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.

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.

Epigenetic study of early breast cancer (EBC) based on DNA methylation and gene integration analysis

Breast cancer (BC) is one of the leading causes of cancer-related deaths in women. The purpose of this study is to identify key molecular markers related to the diagnosis and prognosis of early breast cancer (EBC). The data of mRNA, lncRNA and DNA methylation were downloaded from The Cancer Genome Atlas (TCGA) dataset for identification of differentially expressed mRNAs (DEmRNAs), differentially expressed lncRNAs (DElncRNAs) and DNA methylation analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyzes were used to identify the biological functions of DEmRNAs. The correlation analysis between DNA methylation and DEmRNAs was carried out. Then, diagnostic analysis and prognostic analysis of identified DEmRNAs and DElncRNAs were also performed in the TCGA database. Subsequently, methylation state verification for identified DEmRNAs was performed in the GSE32393 dataset. In addition, real-time polymerase chain reaction (RT-PCR) in vitro verification of genes was performed. Finally, AC093110.1 was overexpressed in human BC cell line MCF-7 to verify cell proliferation and migration. In this study, a total of 1633 DEmRNAs, 750 DElncRNAs and 8042 differentially methylated sites were obtained, respectively. In the Venn analysis, 11 keys DEmRNAs (ALDH1L1, SPTBN1, MRGPRF, CAV2, HSPB6, PITX1, WDR86, PENK, CACNA1H, ALDH1A2 and MME) were we found. ALDH1A2, ALDH1L1, HSPB6, MME, MRGPRF, PENK, PITX1, SPTBN1, WDR86 and CAV2 may be considered as potential diagnostic gene biomarkers in EBC. Strikingly, CAV2, MME, AC093110.1 and AC120498.6 were significantly actively correlated with survival. Methylation state of identified DEmRNAs in GSE32393 dataset was consistent with the result in TCGA. AC093110.1 can affect the proliferation and migration of MCF-7. ALDH1A2, ALDH1L1, HSPB6, MME, MRGPRF, PENK, PITX1, SPTBN1, WDR86 and CAV2 may be potential diagnostic gene biomarkers of EBC. Strikingly, CAV2, MME, AC093110.1 and AC120498.6 were significantly actively correlated with survival. The identification of these genes can help in the early diagnosis and treatment of EBC. In addition, AC093110.1 can regulate SPTBN1 expression and play an important role in cell proliferation and migration, which provides clues to clarify the regulatory mechanism of EBC.

A Prognostic Model for Breast Cancer Based on Cancer Incidence-Related DNA Methylation Pattern

Breast cancer (BC) is the most diagnosed cancer and the leading cause of cancer-related deaths in women. The purpose of this study was to develop a prognostic model based on BC-related DNA methylation pattern. A total of 361 BC incidence-related probes (BCIPs) were differentially methylated in blood samples from women at high risk of BC and BC tissues. Twenty-nine of the 361 BCIPs that significantly correlated with BC outcomes were selected to establish the BCIP score. BCIP scores based on BC-related DNA methylation pattern were developed to evaluate the mortality risk of BC. The correlation between overall survival and BCIP scores was assessed using Kaplan-Meier, univariate, and multivariate analyses. In BC, the BCIP score was significantly correlated with malignant BC characteristics and poor outcomes. Furthermore, we assessed the BCIP score-related gene expression profile and observed that genes with expressions associated with the BCIP score were involved in the process of cancer immunity according to GO and KEGG analyses. Using the ESTIMATE and CIBERSORT algorithms, we discovered that BCIP scores were negatively correlated with both T cell infiltration and immune checkpoint inhibitor response markers in BC tissues. Finally, a nomogram comprising the BCIP score and BC prognostic factors was used to establish a prognostic model for patients with BC, while C-index and calibration curves were used to evaluate the effectiveness of the nomogram. A nomogram comprising the BCIP score, tumor size, lymph node status, and molecular subtype was developed to quantify the survival probability of patients with BC. Collectively, our study developed the BCIP score, which correlated with poor outcomes in BC, to portray the variation in DNA methylation pattern related to BC incidence.

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.

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.

A decade of epigenetic change in aging twins: Genetic and environmental contributions to longitudinal DNA methylation

BACKGROUND

Epigenetic changes may result from the interplay of environmental exposures and genetic influences and contribute to differences in age-related disease, disability, and mortality risk. However, the etiologies contributing to stability and change in DNA methylation have rarely been examined longitudinally.

METHODS

We considered DNA methylation in whole blood leukocyte DNA across a 10-year span in two samples of same-sex aging twins: (a) Swedish Adoption Twin Study of Aging (SATSA; N = 53 pairs, 53% female; 62.9 and 72.5 years, SD = 7.2 years); (b) Longitudinal Study of Aging Danish Twins (LSADT; N = 43 pairs, 72% female, 76.2 and 86.1 years, SD=1.8 years). Joint biometrical analyses were conducted on 358,836 methylation probes in common. Bivariate twin models were fitted, adjusting for age, sex, and country.

RESULTS

Overall, results suggest genetic contributions to DNA methylation across 358,836 sites tended to be small and lessen across 10 years (broad heritability M = 23.8% and 18.0%) but contributed to stability across time while person-specific factors explained emergent influences across the decade. Aging-specific sites identified from prior EWAS and methylation age clocks were more heritable than background sites. The 5037 sites that showed the greatest heritable/familial-environmental influences (p < 1E-07) were enriched for immune and inflammation pathways while 2020 low stability sites showed enrichment in stress-related pathways.

CONCLUSIONS

Across time, stability in methylation is primarily due to genetic contributions, while novel experiences and exposures contribute to methylation differences. Elevated genetic contributions at age-related methylation sites suggest that adaptions to aging and senescence may be differentially impacted by genetic background.

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.

Potential Genes Related to Levofloxacin Resistance in Mycobacterium tuberculosis Based on Transcriptome and Methylome Overlap Analysis

Drug-resistant Mycobacterium tuberculosis (M. tuberculosis) has become an increasingly serious public health problem and has complicated tuberculosis (TB) treatment. Levofloxacin (LOF) is an ideal anti-tuberculosis drug in clinical applications. However, the detailed molecular mechanisms of LOF-resistant M. tuberculosis in TB treatment have not been revealed. Our study performed transcriptome and methylome sequencing to investigate the potential biological characteristics of LOF resistance in M. tuberculosis H37Rv. In the transcriptome analysis, 953 differentially expressed genes (DEGs) were identified; 514 and 439 DEGs were significantly downregulated and upregulated in the LOF-resistant group and control group, respectively. The KEGG pathway analysis revealed that 97 pathways were enriched in this study. In the methylome analysis, 239 differentially methylated genes (DMGs) were identified; 150 and 89 DMGs were hypomethylated and hypermethylated in the LOF-resistant group and control group, respectively. The KEGG pathway analysis revealed that 74 pathways were enriched in this study. The overlap study suggested that 25 genes were obtained. It was notable that nine genes expressed downregulated mRNA and upregulated methylated levels, including pgi, fadE4, php, cyp132, pckA, rpmB1, pfkB, acg, and ctpF, especially cyp132, pckA, and pfkB, which were vital in LOF-resistant M. tuberculosis H37Rv. The overlapping genes between transcriptome and methylome could be essential for studying the molecular mechanisms of LOF-resistant M. tuberculosis H37Rv. These results may provide informative evidence for TB treatment with LOF.

Association Study Between Methylation in the Promoter Regions of cGAS, MAVS, and TRAF3 Genes and the Risk of Cervical Precancerous Lesions and Cervical Cancer in a Southern Chinese Population

A case-control study was used to explore the association between the methylation status in the promoter regions of the cGAS, MAVS, and TRAF3 genes and the diseases of cervical precancerous lesions (CPL) and cervical cancer (CC) in a Southern Chinese population, and to further explore their interaction effects with high-risk human papillomavirus (hrHPV) infection and environmental factors in these diseases. The study protocol was approved by the ethics committee of The First Affiliated Hospital of Jinan University, and this study was performed in 97 healthy controls, 75 patients with CPL and 33 patients with CC, while each participant has read and signed the informed consent forms before enrolment. The promoter methylation status genes were detected from the bisulfite-treated DNA by the bisulfite sequencing PCR (BSP) technique, which was carried out using MethPrimer. The cGAS, MAVS, and TRAF3 promoter methylation levels in CPL (CPL _cGAS = 35.40%, CPL _MAVS = 24.26%, and CPL _TRAF3 = 96.76%) were significantly higher than those in the control (Control _cGAS = 31.87%, Control _MAVS = 21.16%, and Control _TRAF3 = 96.26%, P_cGAS < 0.001, P_MAVS < 0.001, and P_TRAF3 = 0.001); however, there was no significant differences between the CC and control. In the logistic regression model with adjusted covariates, compared with the individuals whose cGAS methylation levels were less than or equal to 31.87%, the women with the levels more than 31.87% increased the risk of CPL by 2.49 times (OR^a = 2.49, 95% CI = 1.31-4.75, P ^a = 0.006). The women with MAVS methylation levels above 21.16% were 1.97 times more likely to have CPL than the those with the levels less than 21.16% (OR^a = 1.97, 95% CI = 1.06-3.69, P ^a = 0.033). A synergistic interaction was found between hrHPV and gene promoter methylation levels of cGAS and MAVS in CPL; however, no potential interaction was observed in CC. The promoter methylation levels in cGAS, MAVS, and TRAF3 genes are higher in CPL than in control, indicating that hypermethylation might be an early event in the progression of cervical intraepithelial neoplasia (CIN). The interaction between the promoter methylation levels in cGAS and MAVS genes and hrHPV infection might play a role in the development of CPL.

Development of Tissue-Specific Age Predictors Using DNA Methylation Data

DNA methylation patterns have been shown to change throughout the normal aging process. Several studies have found epigenetic aging markers using age predictors, but these studies only focused on blood-specific or tissue-common methylation patterns. Here, we constructed nine tissue-specific age prediction models using methylation array data from normal samples. The constructed models predict the chronological age with good performance (mean absolute error of 5.11 years on average) and show better performance in the independent test than previous multi-tissue age predictors. We also compared tissue-common and tissue-specific aging markers and found that they had different characteristics. Firstly, the tissue-common group tended to contain more positive aging markers with methylation values that increased during the aging process, whereas the tissue-specific group tended to contain more negative aging markers. Secondly, many of the tissue-common markers were located in Cytosine-phosphate-Guanine (CpG) island regions, whereas the tissue-specific markers were located in CpG shore regions. Lastly, the tissue-common CpG markers tended to be located in more evolutionarily conserved regions. In conclusion, our prediction models identified CpG markers that capture both tissue-common and tissue-specific characteristics during the aging process.

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.

Methylation and ovarian cancer: Can DNA methylation be of diagnostic use?

Ovarian cancer is a silent killer and, due to late diagnosis and frequent chemo resistance in patients, the primary cause of fatality amongst the various types of gynecological cancer. The discovery of a specific and sensitive biomarker for ovarian cancer could improve early diagnosis, thereby saving lives. Biomarkers could also improve treatment, by predicting which patients will benefit from specific treatment strategies. DNA methylation is an epigenetic mechanism, and 'methylation imbalance' is characteristic of cancer. Previous research suggests that changes in DNA methylation can be used diagnostically, and that they may predict resistance to treatment. This paper gives an up-to-date overview of research investigating the potential of DNA methylation-based markers for diagnostics, prognostics, screening and prediction of drug resistance for ovarian cancer patients. DNA methylation cancer-biomarkers may be useful for cancer treatment, particularly since they are chemically stable and since cancer-associated changes in methylation typically precedes tumor growth. DNA methylation markers could improve diagnosis and treatment and might even be used for screening in the future. Furthermore, DNA methylation biomarkers could facilitate the development of precision medicine. However, at this point no biomarkers for ovarian cancer have a sufficient combination of sensitivity and specificity in a clinical setting. A reason for this is that most studies have focused on a single or a few methylation sites. More large screenings and genome-wide studies must be performed to increase the chance of identifying a DNA methylation marker which can identify ovarian cancer.

Tet methylcytosine dioxygenase 1 promotes hypoxic gene induction and cell migration in colon cancer

Ten-eleven translocation 1 (TET1), a widely reported DNA demethylation protein, has been associated with tumorigenesis and metastasis. However, whether TET1 is an oncogene or tumor suppressor gene has been controversial; the mechanism of how TET1 affects cancer progression remains unclear. The current study aims to investigate how TET1 is changed in the tumor microenvironment and to explore the mechanisms of how TET1 affects colon cancer progression. Because hypoxia prevails on solid tumors, we established an important connection between hypoxia and DNA demethylation in tumorigenesis. By qPCR and RNA interference (RNAi) technology, we found that hypoxia increased TET1 expression with a hypoxia-inducible factor-1-alpha (HIF-1α)-dependent manner. By CHIP-qPCR and pyrosequencing technology, we demonstrated that TET1 regulated the target gene expression of HIF-1α through HIF-1α binding to hypoxia-responsive elements (HREs), and HIF-1α binding to HREs depended on CpG methylation levels. By Cell Counting Kit-8 (CCK-8) and transwell assay, we showed that loss of TET1 did not affect cell proliferation but inhibited migration. We also identified two novel gene mutants of TET1 in 120 paired tumor/normal tissue specimens by DNA sequencing and found that TET1 E2082K mutant blocked the TET1-enhanced cell migration. Our results showed that the downregulation of TET1 rescued the abnormally high levels of gene expression resulting from hypoxia in tumors and reduced the migration activity of tumor cells, suggesting a therapeutic role by interference with TET1 in colon cancer treatment. By demonstrating that hypoxia upregulated TET1 and that TET1 drove HIF-1α-responsive genes, we showed that an epigenetic mechanism and tumor microenvironment-driven models coexisted and mutually affected colon cancer.

MC1R CpG island regulates MC1R expression and is methylated in a subset of melanoma tumours

Melanocortin 1 receptor (MC1R) is a G protein-coupled receptor expressed in melanocytes where it plays an important role in skin pigmentation and in the UV response, and has implications in melanoma development. Here we show that methylation of a CpG island (CGI) within the MC1R gene can control expression of MC1R in melanoma. This CGI overlaps with a potential enhancer region, and is unmethylated in normal melanocytes but highly methylated in other skin cells, suggesting a melanocyte specific function. Analysis showed that MC1R was the only gene significantly differentially expressed by methylation of this region. Within several data sets, this region is methylated in a subset of melanoma tumours (55%-74% of tumours) and results in reduced MC1R expression and significantly longer overall survival.

Identification of DAPK1 Promoter Hypermethylation as a Biomarker for Intra-Epithelial Lesion and Cervical Cancer: A Meta-Analysis of Published Studies, TCGA, and GEO Datasets

Background: Promoter hypermethylation in death-associated protein kinase 1 (DAPK1) gene has been long linked to cervical neoplasia, but the established results remained controversial. Here, we performed a meta-analysis to assess the associations of DAPK1 promoter hypermethylation with low-grade intra-epithelial lesion (HSIL), high-grade intra-epithelial lesion (HSIL), cervical cancer (CC), and clinicopathological features of CC.

Methods: Published studies with qualitative methylation data were initially searched from PubMed, Web of Science, EMBASE, and China National Knowledge Infrastructure databases (up to March 2018). Then, quantitative methylation datasets, retrieved from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, were pooled to validate the results of published studies.

Results: In a meta-analysis of 37 published studies, DAPK1 promoter hypermethylation progressively increased the risk of LSIL by 2.41-fold (P = 0.012), HSIL by 7.62-fold (P < 0.001), and CC by 23.17-fold (P < 0.001). Summary receiver operating characteristic curves suggested a potential diagnostic value of DAPK1 promoter hypermethylation in CC, with a large area-under-the-curve of 0.83, a high specificity of 97%, and a moderate sensitivity of 59%. There were significant impacts of DAPK1 promoter hypermethylation on histological type (odds ratio (OR) = 3.53, P < 0.001) and FIGO stage of CC (OR = 2.15, P = 0.003). Then, a pooled analysis of nine TCGA and GEO datasets, covering 13 CPG sites within DAPK1 promoter, identified eight CC-associated sites, six sites with diagnostic values for CC (pooled specificities: 74–90%; pooled sensitivities: 70–81%), nine loci associated with the histological type of CC, and all 13 loci with down-regulated effects on DAPK1 mRNA expression.

Conclusion: The meta-analysis suggests that DAPK1 promoter hypermethylation is significantly associated with the disease severity of cervical neoplasia. DAPK1 methylation detection exhibits a promising ability to discriminate CC from cancer-free controls.

Accounting for tumor purity improves cancer subtype classification from DNA methylation data

Motivation

Tumor sample classification has long been an important task in cancer research. Classifying tumors into different subtypes greatly benefits therapeutic development and facilitates application of precision medicine on patients. In practice, solid tumor tissue samples obtained from clinical settings are always mixtures of cancer and normal cells. Thus, the data obtained from these samples are mixed signals. The 'tumor purity', or the percentage of cancer cells in cancer tissue sample, will bias the clustering results if not properly accounted for.

Results

In this article, we developed a model-based clustering method and an R function which uses DNA methylation microarray data to infer tumor subtypes with the consideration of tumor purity. Simulation studies and the analyses of The Cancer Genome Atlas data demonstrate improved results compared with existing methods.

Availability and implementation

InfiniumClust is part of R package InfiniumPurify , which is freely available from CRAN ( https://cran.r-project.org/web/packages/InfiniumPurify/index.html ).

Contact

hao.wu@emory.edu or xqzheng@shnu.edu.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

A polycomb-mediated epigenetic field defect precedes invasive cervical carcinoma

Human papillomavirus (HPV)-associated cervical carcinoma is preceded by stages of cervical intra-epithelial neoplasia (CIN) that can variably progress to malignancy. Understanding the different molecular processes involved in the progression of pre-malignant CIN is critical to the development of improved predictive and interventional capabilities. We tested the role of regulators of transcription in both the development and the progression of HPV-associated CIN, performing the most comprehensive genomic survey to date of DNA methylation in HPV-associated cervical neoplasia, testing ~2 million loci throughout the human genome in biopsies from 78 HPV+ women, identifying changes starting in early CIN and maintained through carcinogenesis. We identified loci at which DNA methylation is consistently altered, beginning early in the course of neoplastic disease and progressing with disease advancement. While the loss of DNA methylation occurs mostly at intergenic regions, acquisition of DNA methylation is at sites involved in transcriptional regulation, with strong enrichment for targets of polycomb repression. Using an independent cohort from The Cancer Genome Atlas, we validated the loci with increased DNA methylation and found that these regulatory changes were associated with locally decreased gene expression. Secondary validation using immunohistochemistry showed that the progression of neoplasia was associated with increasing polycomb protein expression specifically in the cervical epithelium. We find that perturbations of genomic regulatory processes occur early and persist in cervical carcinoma. The results indicate a polycomb-mediated epigenetic field defect in cervical neoplasia that may represent a target for early, topical interventions using polycomb inhibitors.

Epigenome-based cancer risk prediction: rationale, opportunities and challenges

The incidence of cancer is continuing to rise and risk-tailored early diagnostic and/or primary prevention strategies are urgently required. The ideal risk-predictive test should: integrate the effects of both genetic and nongenetic factors and aim to capture these effects using an approach that is both biologically stable and technically reproducible; derive a score from easily accessible biological samples that acts as a surrogate for the organ in question; and enable the effectiveness of risk-reducing measures to be monitored. Substantial evidence has accumulated suggesting that the epigenome and, in particular, DNA methylation-based tests meet all of these requirements. However, the development and implementation of DNA methylation-based risk-prediction tests poses considerable challenges. In particular, the cell type specificity of DNA methylation and the extensive cellular heterogeneity of the easily accessible surrogate cells that might contain information relevant to less accessible tissues necessitates the use of novel methods in order to account for these confounding issues. Furthermore, the engagement of the scientific community with health-care professionals, policymakers and the public is required in order to identify and address the organizational, ethical, legal, social and economic challenges associated with the routine use of epigenetic testing.

Association of multiple genetic variants with breast cancer susceptibility in the Han Chinese population

We selected 13 tag single nucleotide polymorphisms (tSNPs) to investigate whether they were associated with breast cancer risk in the Chinese Han population. Upon statistical analyses of clinical data from 551 patients and 577 controls, we found that six of the 13 SNPs were associated with breast cancer; namely, rs4973768(Odds ratio (OR) = 1.30, 95% confidence interval (CI) =1.01-1.67), rs981782(OR =1.30, 95% CI=1.01-1.66), rs1432679(OR =0.84, 95% CI=0.70-0.99), rs10759243(OR=1.30, 95%CI=1.09-1.55), rs10822013(OR =1.18, 95% CI=1.00-1.39) and rs704010(OR =1.63, 95% CI=1.04-2.56). When stratified based on breast cancer subtype, our analyses revealed that three SNPs (rs981782, rs10759243 and rs704010) correlated with ER+ breast cancer, while another three (rs4973768, rs1432679 and rs10822013) correlated with ER- breast cancer. We obtained similar results while investigating the correlation of SNPs with PR status or clinical stage. Our results suggest that associations identified between SNPs and breast cancer through genome-wide association studies (GWAS) may not always be generalizable across races.

ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis

For transcriptomic analysis, there are numerous microarray-based genomic data, especially those generated for cancer research. The typical analysis measures the difference between a cancer sample-group and a matched control group for each transcript or gene. Association rule mining is used to discover interesting item sets through rule-based methodology. Thus, it has advantages to find causal effect relationships between the transcripts. In this work, we introduce two new rule-based similarity measures-weighted rank-based Jaccard and Cosine measures-and then propose a novel computational framework to detect condensed gene co-expression modules ( C o n G E M s) through the association rule-based learning system and the weighted similarity scores. In practice, the list of evolved condensed markers that consists of both singular and complex markers in nature depends on the corresponding condensed gene sets in either antecedent or consequent of the rules of the resultant modules. In our evaluation, these markers could be supported by literature evidence, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and Gene Ontology annotations. Specifically, we preliminarily identified differentially expressed genes using an empirical Bayes test. A recently developed algorithm-RANWAR-was then utilized to determine the association rules from these genes. Based on that, we computed the integrated similarity scores of these rule-based similarity measures between each rule-pair, and the resultant scores were used for clustering to identify the co-expressed rule-modules. We applied our method to a gene expression dataset for lung squamous cell carcinoma and a genome methylation dataset for uterine cervical carcinogenesis. Our proposed module discovery method produced better results than the traditional gene-module discovery measures. In summary, our proposed rule-based method is useful for exploring biomarker modules from transcriptomic data.

Searching for new biomarkers in ovarian cancer patients: Rationale and design of a retrospective study under the Mermaid III project

Ovarian cancer is a silent killer and, due to late diagnosis, the primary cause of death amongst gynecological cancers, killing approximately 376 women annually in Denmark. The discovery of a specific and sensitive biomarker for ovarian cancer could improve early diagnosis, but also treatment, by predicting which patients will benefit from specific treatment strategies. The Mermaid III project is consisting of 3 parts including "Early detection, screening and long-term survival," "Biomarkers and/or prognostic markers" and "The infection theory." The present paper gives an overview of the part regarding biomarkers and/or prognostic markers, with a focus on rationale and design. The study described has 3 major branches: microRNAs, epigenetics and Next Generation Sequencing. Tissue and blood from ovarian cancer patients, already enrolled in the prospective ongoing pelvic mass cohort, will be examined. Relevant microRNAs and DNA methylation patterns will be investigated using array technology. Patient exomes will be fully sequenced, and identified genetic variations will be validated with Next Generation Sequencing. In all cases, data will be correlated with clinical information on the patient, in order to identify possible biomarkers. A thorough investigation of biomarkers in ovarian cancer, including large numbers of different markers, has never been done before. Besides from improving diagnosis and treatment, other outcomes could be markers for screening, knowledge of the molecular aspects of cancer and the discovery of new drugs. Moreover, biomarkers are a prerequisite for the development of precision medicine. This study will attack the ovarian cancer problem from several angles, thereby increasing the chance of successfully contributing to saving lives.

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.

DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing

The ability to estimate the age of the donor from recovered biological material at a crime scene can be of substantial value in forensic investigations. Aging can be complex and is associated with various molecular modifications in cells that accumulate over a person's lifetime including epigenetic patterns. The aim of this study was to use age-specific DNA methylation patterns to generate an accurate model for the prediction of chronological age using data from whole blood. In total, 45 age-associated CpG sites were selected based on their reported age coefficients in a previous extensive study and investigated using publicly available methylation data obtained from 1156 whole blood samples (aged 2-90 years) analysed with Illumina's genome-wide methylation platforms (27K/450K). Applying stepwise regression for variable selection, 23 of these CpG sites were identified that could significantly contribute to age prediction modelling and multiple regression analysis carried out with these markers provided an accurate prediction of age (R2=0.92, mean absolute error (MAE)=4.6 years). However, applying machine learning, and more specifically a generalised regression neural network model, the age prediction significantly improved (R2=0.96) with a MAE=3.3 years for the training set and 4.4 years for a blind test set of 231 cases. The machine learning approach used 16 CpG sites, located in 16 different genomic regions, with the top 3 predictors of age belonged to the genes NHLRC1, SCGN and CSNK1D. The proposed model was further tested using independent cohorts of 53 monozygotic twins (MAE=7.1 years) and a cohort of 1011 disease state individuals (MAE=7.2 years). Furthermore, we highlighted the age markers' potential applicability in samples other than blood by predicting age with similar accuracy in 265 saliva samples (R2=0.96) with a MAE=3.2 years (training set) and 4.0 years (blind test). In an attempt to create a sensitive and accurate age prediction test, a next generation sequencing (NGS)-based method able to quantify the methylation status of the selected 16 CpG sites was developed using the Illumina MiSeq® platform. The method was validated using DNA standards of known methylation levels and the age prediction accuracy has been initially assessed in a set of 46 whole blood samples. Although the resulted prediction accuracy using the NGS data was lower compared to the original model (MAE=7.5years), it is expected that future optimization of our strategy to account for technical variation as well as increasing the sample size will improve both the prediction accuracy and reproducibility.

Maintaining cell identity: PRC2-mediated regulation of transcription and cancer

Enhancer of zeste homologue 2 (EZH2), the catalytic subunit of Polycomb repressive complex 2 (PRC2), has attracted broad research attention in the past few years because of its involvement in the development and maintenance of many types of cancer and the use of specific EZH2 inhibitors in clinical trials. Several observations show that PRC2 can have both oncogenic and tumour-suppressive functions. We propose that these apparently opposing roles of PRC2 in cancer are a consequence of the molecular function of the complex in maintaining, rather than specifying, the transcriptional repression state of its several thousand target genes.

Genetic alterations and epigenetic alterations of cancer-associated fibroblasts

Cancer-associated fibroblasts (CAFs) are one major type of component identified in the tumor microenvironment. Studies have focused on the genetic and epigenetic status of CAFs, since they are critical in tumor progression and differ phenotypically and functionally from normal fibroblasts. The present review summarizes the recent achievements in understanding the gene profiles of CAFs and pays special attention to their possible epigenetic alterations. A total of 7 possible genetic alterations and epigenetic changes in CAFs are discussed, including gene differential expression, karyotype analysis, gene copy number variation, loss of heterozygosis, allelic imbalance, microsatellite instability, post-transcriptional control and DNA methylation. These genetic and epigenetic characteristics are hypothesized to provide a deep understanding of CAFs and a perspective on their clinical significance.

Epigenetic changes as prognostic predictors in endometrial carcinomas

Endometrial carcinoma is one of the most frequent gynecological malignancies of the female. The diagnostic and prognostic markers for the high-risk subgroups with unfavorable prognosis are under intense debate worldwide, and, therefore, the aim of this study was to identify new potential DNA methylation markers for the high-risk groups. We used the Illumina Infinium HumanMethylation450 BeadChip to analyze the DNA methylation pattern and investigated its association with clinicopathological features important for defining the high-risk (FIGO-grade 3) and low-risk (FIGO-grade 1) groups of patients with endometrial cancer (n = 31 and n = 39, respectively). We identified specific DNA methylation signature in high-risk endometrial tumors, and potential molecular biomarker genes (TBX2, CHST11, and NID2) associated with unfavorable clinical predictive and prognostic factors.

Multilayer OMIC Data in Medullary Thyroid Carcinoma Identifies the STAT3 Pathway as a Potential Therapeutic Target in RETM918T Tumors

Purpose: Medullary thyroid carcinoma (MTC) is a rare disease with few genetic drivers, and the etiology specific to each known susceptibility mutation remains unknown. Exploiting multilayer genomic data, we focused our interest on the role of aberrant DNA methylation in MTC development.Experimental Design: We performed genome-wide DNA methylation profiling assessing more than 27,000 CpGs in the largest MTC series reported to date, comprising 48 molecularly characterized tumors. mRNA and miRNA expression data were available for 33 and 31 tumors, respectively. Two human MTC cell lines and 101 paraffin-embedded MTCs were used for validation.Results: The most distinctive methylome was observed for RET^M918T-related tumors. Integration of methylation data with mRNA and miRNA expression data identified genes negatively regulated by promoter methylation. These in silico findings were confirmed in vitro for PLCB2, DKK4, MMP20, and miR-10a, -30a, and -200c. The mutation-specific aberrant methylation of PLCB2, DKK4, and MMP20 was validated in 25 independent MTCs by bisulfite pyrosequencing. The methylome and transcriptome data underscored JAK/Stat pathway involvement in RET^M918T MTCs. Immunostaining [immunohistochemistry (IHC)] for the active form of signaling effector STAT3 was performed in a series of 101 MTCs. As expected, positive IHC was associated with RET^M918T-bearing tumors (P < 0.02). Pharmacologic inhibition of STAT3 activity increased the sensitivity to vandetanib of the RET^M918T-positive MTC cell line, MZ-CRC-1.Conclusions: Multilayer OMIC data analysis uncovered methylation hallmarks in genetically defined MTCs and revealed JAK/Stat signaling effector STAT3 as a potential therapeutic target for the treatment of RET^M918T MTCs. Clin Cancer Res; 23(5); 1334-45. ©2016 AACR.

Reference-free deconvolution of DNA methylation data and mediation by cell composition effects

Background

Recent interest in reference-free deconvolution of DNA methylation data has led to several supervised methods, but these methods do not easily permit the interpretation of underlying cell types.

Results

We propose a simple method for reference-free deconvolution that provides both proportions of putative cell types defined by their underlying methylomes, the number of these constituent cell types, as well as a method for evaluating the extent to which the underlying methylomes reflect specific types of cells. We demonstrate these methods in an analysis of 23 Infinium data sets from 13 distinct data collection efforts; these empirical evaluations show that our algorithm can reasonably estimate the number of constituent types, return cell proportion estimates that demonstrate anticipated associations with underlying phenotypic data; and methylomes that reflect the underlying biology of constituent cell types.

Conclusions

Our methodology permits an explicit quantitation of the mediation of phenotypic associations with DNA methylation by cell composition effects. Although more work is needed to investigate functional information related to estimated methylomes, our proposed method provides a novel and useful foundation for conducting DNA methylation studies on heterogeneous tissues lacking reference data.

Electronic supplementary material

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The identification of age-associated cancer markers by an integrative analysis of dynamic DNA methylation changes

As one of the most widely studied epigenetic modifications, DNA methylation has an important influence on human traits and cancers. Dynamic variations in DNA methylation have been reported in malignant neoplasm and aging; however, the mechanisms remain poorly understood. By constructing an age-associated and cancer-related weighted network (ACWN) based on the correlation of the methylation level and the protein-protein interaction, we found that DNA methylation changes associated with age were closely related to the occurrence of cancer. Additional analysis of 102 module genes mined from the ACWN revealed discrimination based on two main patterns. One pattern involved methylation levels that increased with aging and were higher in cancer patients compared with normal controls (HH pattern). The other pattern involved methylation levels that decreased with aging and were lower in cancer compared with normal (LL pattern). Upon incorporation with gene expression levels, 25 genes were filtered based on negative regulation by DNA methylation. These genes were regarded as potential cancer risk markers that were influenced by age in the process of carcinogenesis. Our results will facilitate further studies regarding the impact of the epigenetic effects of aging on diseases and will aid in the development of tailored cancer preventive strategies.

Mining the epigenetic landscape of tissue polarity in search of new targets for cancer therapy

The epigenetic nature of cancer encourages the development of inhibitors of epigenetic pathways. Yet, the clinical use for solid tumors of approved epigenetic drugs is meager. We argue that this situation might improve upon understanding the coinfluence between epigenetic pathways and tissue architecture. We present emerging information on the epigenetic control of the polarity axis, a central feature of epithelial architecture created by the orderly distribution of multiprotein complexes at cell-cell and cell-extracellular matrix contacts and altered upon cancer onset (with apical polarity loss), invasive progression (with basolateral polarity loss) and metastatic development (with basoapical polarity imbalance). This information combined with the impact of polarity-related proteins on epigenetic mechanisms of cancer enables us to envision how to guide the choice of drugs specific for distinct epigenetic modifiers, in order to halt cancer development and counter the consequences of polarity alterations.

Intra-Gene DNA Methylation Variability Is a Clinically Independent Prognostic Marker in Women's Cancers

We introduce a novel per-gene measure of intra-gene DNA methylation variability (IGV) based on the Illumina Infinium HumanMethylation450 platform, which is prognostic independently of well-known predictors of clinical outcome. Using IGV, we derive a robust gene-panel prognostic signature for ovarian cancer (OC, n = 221), which validates in two independent data sets from Mayo Clinic (n = 198) and TCGA (n = 358), with significance of p = 0.004 in both sets. The OC prognostic signature gene-panel is comprised of four gene groups, which represent distinct biological processes. We show the IGV measurements of these gene groups are most likely a reflection of a mixture of intra-tumour heterogeneity and transcription factor (TF) binding/activity. IGV can be used to predict clinical outcome in patients individually, providing a surrogate read-out of hard-to-measure disease processes.

An integrative pan-cancer-wide analysis of epigenetic enzymes reveals universal patterns of epigenomic deregulation in cancer

Background

One of the most important recent findings in cancer genomics is the identification of novel driver mutations which often target genes that regulate genome-wide chromatin and DNA methylation marks. Little is known, however, as to whether these genes exhibit patterns of epigenomic deregulation that transcend cancer types.

Results

Here we conduct an integrative pan-cancer-wide analysis of matched RNA-Seq and DNA methylation data across ten different cancer types. We identify seven tumor suppressor and eleven oncogenic epigenetic enzymes which display patterns of deregulation and association with genome-wide cancer DNA methylation patterns, which are largely independent of cancer type. In doing so, we provide evidence that genome-wide cancer hyper- and hypo- DNA methylation patterns are independent processes, controlled by distinct sets of epigenetic enzyme genes. Using causal network modeling, we predict a number of candidate drivers of cancer DNA hypermethylation and hypomethylation. Finally, we show that the genomic loci whose DNA methylation levels associate most strongly with expression of these putative drivers are highly consistent across cancer types.

Conclusions

This study demonstrates that there exist universal patterns of epigenomic deregulation that transcend cancer types, and that intra-tumor levels of genome-wide DNA hypomethylation and hypermethylation are controlled by distinct processes.

Electronic supplementary material

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DNA methylation as a promising landscape: A simple blood test for breast cancer prediction

Breast cancer is the most common malignancy among women worldwide. Risk assessment is one of the main services delivered by cancer clinics. Biomarker analysis on different tissues including the peripheral blood can provide crucial information. One of the potential epigenetic biomarkers (epimarkers) is introduced as the peripheral blood DNA methylation pattern. This study was conducted to evaluate the potential value of peripheral blood epimarkers as an accessible tool to predict the risk of breast cancer development. WBC's DNA was the focus of several case-control studies at both genome wide and candidate gene levels to reveal epigenetic changes accounting for predisposition to breast cancer, leading to suggest that ATM, TITF1, SFRP1, NUP155, NEUROD1, ZNF217, DBC2, DOK7 and ESR1 genes and the LINE1, Alu and Sat2 DNA elements could be considered as the potential epimarkers. To address that by which mechanisms WBC's DNA methylation patterns could be linked to the propensity to breast cancer, several contemplations have been offered. Constitutional epimutation during embryonic life, and methylation changes secondary to either environmental exposures or tumor-mediated immune response, are the two main mechanisms. One can deduce that epimarkers based on their potential properties or regulatory impacts on cancer-related genes may be employed for risk prediction, prognosis, and survival inferences that are highly required for breast cancer management toward personalized medicine.

Cell-composition effects in the analysis of DNA methylation array data: a mathematical perspective

Background

The impact of cell-composition effects in analysis of DNA methylation data is now widely appreciated. With the availability of a reference data set consisting of DNA methylation measurements on isolated cell types, it is possible to impute cell proportions and adjust for them, but there is increasing interest in methods that adjust for cell composition effects when reference sets are incomplete or unavailable.

Results

In this article we present a theoretical basis for one such method, showing that the total effect of a phenotype on DNA methylation can be decomposed into orthogonal components, one representing the effect of phenotype on proportions of major cell types, the other representing either subtle effects in composition or global effects at focused loci, and that it is possible to separate these two types of effects in a finite data set. We demonstrate this principle empirically on nine DNA methylation data sets, showing that the first few principal components generally contain a majority of the information on cell-type present in the data, but that later principal components nevertheless contain information about a small number of loci that may represent more focused associations. We also present a new method for determining the number of linear terms to interpret as cell-mixture effects and demonstrate robustness to the choice of this parameter.

Conclusions

Taken together, our work demonstrates that reference-free algorithms for cell-mixture adjustment can produce biologically valid results, separating cell-mediated epigenetic effects (i.e. apparent effects arising from differences in cell composition) from those that are not cell mediated, and that in general the interpretation of associations evident from DNA methylation should be carefully considered.

Electronic supplementary material

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Comparative methylome analysis identifies new tumour subtypes and biomarkers for transformation of nephrogenic rests into Wilms tumour

BACKGROUND

Wilms tumours (WTs) are characterised by several hallmarks that suggest epimutations such as aberrant DNA methylation are involved in tumour progression: loss of imprinting at 11p15, lack of recurrent mutations and formation of nephrogenic rests (NRs), which are lesions of retained undifferentiated embryonic tissue that can give rise to WTs.

METHODS

To identify such epimutations, we performed a comprehensive methylome analysis on 20 matched trios of micro-dissected WTs, NRs and surrounding normal kidneys (NKs) using Illumina Infinium HumanMethylation450 Bead Chips and functionally validated findings using RNA sequencing.

RESULTS

Comparison of NRs with NK revealed prominent tissue biomarkers: 629 differentially methylated regions, of which 55% were hypermethylated and enriched for domains that are bivalent in embryonic stem cells and for genes expressed during development (P = 2.49 × 10(-5)). Comparison of WTs with NRs revealed two WT subgroups; group-2 WTs and NRs were epigenetically indistinguishable whereas group-1 WTs showed an increase in methylation variability, hypomethylation of renal development genes, hypermethylation and relative loss of expression of cell adhesion genes and known and potential new WT tumour suppressor genes (CASP8, H19, MIR195, RB1 and TSPAN32) and was strongly associated with bilateral disease (P = 0.032). Comparison of WTs and NRs to embryonic kidney highlighted the significance of polycomb target methylation in Wilms tumourigenesis.

CONCLUSIONS

Methylation levels vary during cancer evolution. We have described biomarkers related to WT evolution from its precursor NRs which may be useful to differentiate between these tissues for patients with bilateral disease.

Normal cell-type epigenetics and breast cancer classification: a case study of cell mixture-adjusted analysis of DNA methylation data from tumors

Historically, breast cancer classification has relied on prognostic subtypes. Thus, unlike hematopoietic cancers, breast tumor classification lacks phylogenetic rationale. The feasibility of phylogenetic classification of breast tumors has recently been demonstrated based on estrogen receptor (ER), androgen receptor (AR), vitamin D receptor (VDR) and Keratin 5 expression. Four hormonal states (HR0–3) comprising 11 cellular subtypes of breast cells have been proposed. This classification scheme has been shown to have relevance to clinical prognosis. We examine the implications of such phylogenetic classification on DNA methylation of both breast tumors and normal breast tissues by applying recently developed deconvolution algorithms to three DNA methylation data sets archived on Gene Expression Omnibus. We propose that breast tumors arising from a particular cell-of-origin essentially magnify the epigenetic state of their original cell type. We demonstrate that DNA methylation of tumors manifests patterns consistent with cell-specific epigenetic states, that these states correspond roughly to previously posited normal breast cell types, and that estimates of proportions of the underlying cell types are predictive of tumor phenotypes. Taken together, these findings suggest that the epigenetics of breast tumors is ultimately based on the underlying phylogeny of normal breast tissue.

Characterization of age signatures of DNA methylation in normal and cancer tissues from multiple studies

Background

DNA methylation (DNAm) levels can be used to predict the chronological age of tissues; however, the characteristics of DNAm age signatures in normal and cancer tissues are not well studied using multiple studies.

Results

We studied approximately 4000 normal and cancer samples with multiple tissue types from diverse studies, and using linear and nonlinear regression models identified reliable tissue type-invariant DNAm age signatures. A normal signature comprising 127 CpG loci was highly enriched on the X chromosome. Age-hypermethylated loci were enriched for guanine–and-cytosine-rich regions in CpG islands (CGIs), whereas age-hypomethylated loci were enriched for adenine–and-thymine-rich regions in non-CGIs. However, the cancer signature comprised only 26 age-hypomethylated loci, none on the X chromosome, and with no overlap with the normal signature. Genes related to the normal signature were enriched for aging-related gene ontology terms including metabolic processes, immune system processes, and cell proliferation. The related gene products of the normal signature had more than the average number of interacting partners in a protein interaction network and had a tendency not to interact directly with each other. The genomic sequences of the normal signature were well conserved and the age-associated DNAm levels could satisfactorily predict the chronological ages of tissues regardless of tissue type. Interestingly, the age-associated DNAm increases or decreases of the normal signature were aberrantly accelerated in cancer samples.

Conclusion

These tissue type-invariant DNAm age signatures in normal and cancer can be used to address important questions in developmental biology and cancer research.

Electronic supplementary material

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Poised epigenetic states and acquired drug resistance in cancer

Epigenetic events, which are somatically inherited through cell division, are potential drivers of acquired drug resistance in cancer. The high rate of epigenetic change in tumours generates diversity in gene expression patterns that can rapidly evolve through drug selection during treatment, leading to the development of acquired resistance. This will potentially confound stratified chemotherapy decisions that are solely based on mutation biomarkers. Poised epigenetic states in tumour cells may drive multistep epigenetic fixation of gene expression during the acquisition of drug resistance, which has implications for clinical strategies to prevent the emergence of drug resistance.

Comparative DNA methylome analysis of endometrial carcinoma reveals complex and distinct deregulation of cancer promoters and enhancers

BACKGROUND

Aberrant DNA methylation is a hallmark of many cancers. Classically there are two types of endometrial cancer, endometrioid adenocarcinoma (EAC), or Type I, and uterine papillary serous carcinoma (UPSC), or Type II. However, the whole genome DNA methylation changes in these two classical types of endometrial cancer is still unknown.

RESULTS

Here we described complete genome-wide DNA methylome maps of EAC, UPSC, and normal endometrium by applying a combined strategy of methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylation-sensitive restriction enzyme digestion sequencing (MRE-seq). We discovered distinct genome-wide DNA methylation patterns in EAC and UPSC: 27,009 and 15,676 recurrent differentially methylated regions (DMRs) were identified respectively, compared with normal endometrium. Over 80% of DMRs were in intergenic and intronic regions. The majority of these DMRs were not interrogated on the commonly used Infinium 450K array platform. Large-scale demethylation of chromosome X was detected in UPSC, accompanied by decreased XIST expression. Importantly, we discovered that the majority of the DMRs harbored promoter or enhancer functions and are specifically associated with genes related to uterine development and disease. Among these, abnormal methylation of transposable elements (TEs) may provide a novel mechanism to deregulate normal endometrium-specific enhancers derived from specific TEs.

CONCLUSIONS

DNA methylation changes are an important signature of endometrial cancer and regulate gene expression by affecting not only proximal promoters but also distal enhancers.

DNA methylation age of human tissues and cell types

Background

It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure.

Results

I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance.

Conclusions

I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.

Pan-cancer patterns of DNA methylation

The comparison of DNA methylation patterns across cancer types (pan-cancer methylome analyses) has revealed distinct subgroups of tumors that share similar methylation patterns. Integration of these data with the wealth of information derived from cancer genome profiling studies performed by large international consortia has provided novel insights into the cellular aberrations that contribute to cancer development. There is evidence that genetic mutations in epigenetic regulators (such as DNMT3, IDH1/2 or H3.3) mediate or contribute to these patterns, although a unifying molecular mechanism underlying the global alterations of DNA methylation has largely been elusive. Knowledge gained from pan-cancer methylome analyses will aid the development of diagnostic and prognostic biomarkers, improve patient stratification and the discovery of novel druggable targets for therapy, and will generate hypotheses for innovative clinical trial designs based on methylation subgroups rather than on cancer subtypes. In this review, we discuss recent advances in the global profiling of tumor genomes for aberrant DNA methylation and the integration of these data with cancer genome profiling data, highlight potential mechanisms leading to different methylation subgroups, and show how this information can be used in basic research and for translational applications. A remaining challenge is to experimentally prove the functional link between observed pan-cancer methylation patterns, the associated genetic aberrations, and their relevance for the development of cancer.

The dynamics of DNA methylation covariation patterns in carcinogenesis

Recently it has been observed that cancer tissue is characterised by an increased variability in DNA methylation patterns. However, how the correlative patterns in genome-wide DNA methylation change during the carcinogenic progress has not yet been explored. Here we study genome-wide inter-CpG correlations in DNA methylation, in addition to single site variability, during cervical carcinogenesis. We demonstrate how the study of changes in DNA methylation covariation patterns across normal, intra-epithelial neoplasia and invasive cancer allows the identification of CpG sites that indicate the risk of neoplastic transformation in stages prior to neoplasia. Importantly, we show that the covariation in DNA methylation at these risk CpG loci is maximal immediately prior to the onset of cancer, supporting the view that high epigenetic diversity in normal cells increases the risk of cancer. Consistent with this, we observe that invasive cancers exhibit increased covariation in DNA methylation at the risk CpG sites relative to normal tissue, but lower levels relative to pre-cancerous lesions. We further show that the identified risk CpG sites undergo preferential DNA methylation changes in relation to human papilloma virus infection and age. Results are validated in independent data including prospectively collected samples prior to neoplastic transformation. Our data are consistent with a phase transition model of carcinogenesis, in which epigenetic diversity is maximal prior to the onset of cancer. The model and algorithm proposed here may allow, in future, network biomarkers predicting the risk of neoplastic transformation to be identified.

DNA methylation is a covalent modification of DNA which can regulate how active genes are. DNA methylation is altered at many genomic loci in cancer cells, leading to widespread functional disruption. Importantly, DNA methylation alterations across the genome are seen even in early carcinogenesis. Although the pattern of DNA methylation change during carcinogenesis has been studied at individual genomic loci, no study has yet analysed how these patterns change at a systems-level, specifically how do DNA methylation patterns at pairs of genomic sites change during disease progression. Doing so can shed light on how the epigenetic diversity of cell populations changes during the carcinogenic process. This study performs a systems-level analysis of the dynamic changes in DNA methylation correlation pattern during cervical carcinogenesis, demonstrating that epigenetic diversity is maximal just prior to the onset of cancer. Importantly, this supports the view that the risk of cancer development is closely related to an increase in epigenetic diversity in apparently healthy cells. In addition, the study provides a computational algorithm which successfully identifies the altered genomic sites confering the risk of cervical cancer.