Epigenome-wide association study reveals decreased average methylation levels years before breast cancer diagnosis

Exploring cross-tissue DNA methylation patterns: blood-brain CpGs as potential neurodegenerative disease biomarkers

The difficulty of obtaining samples from certain human tissues has led to efforts to find accessible sources to analyze molecular markers derived from DNA. In this study, we look for DNA methylation patterns in blood samples and its association with the brain methylation pattern in neurodegenerative disorders. Using data from methylation databases, we selected 18,293 CpGs presenting correlated methylation levels between blood and brain (bb-CpGs) and compare their methylation level between blood samples from patients with neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, Multiple Sclerosis, and X Fragile Syndrome) and healthy controls. Sixty-four bb-CpGs presented significant distinct methylation levels in patients, being: nine for Alzheimer's disease, nine for Parkinson's disease, 28 for Multiple Sclerosis, and 18 for Fragile X Syndrome. Similar differences in methylation pattern for the nine Alzheimer's bb-CpGs was also observed when comparing brain tissue from patients vs. controls. The genomic regions of some of these 64 bb-CpGs are placed close to or inside genes previously associated with the respective condition. Our findings support the rationale of using blood DNA as a surrogate of brain tissue to analyze changes in CpG methylation level in patients with neurodegenerative diseases, opening the possibility for characterizing new biomarkers.

Methylation marks in blood DNA reveal breast cancer risk in patients fulfilling hereditary disease criteria

Less than 15-20% of patients who meet the criteria for hereditary breast and ovarian cancer (HBOC) carry pathogenic coding genetic mutations, implying that other molecular mechanisms may contribute to the increased risk of this condition. DNA methylation in peripheral blood has been suggested as a potential epigenetic marker for the risk of breast cancer (BC). We aimed to discover methylation marks in peripheral blood associated with BC in 231 pre-treatment BC patients meeting HBOC criteria, testing negative for coding pathogenic variants, and 156 healthy controls, through methylation analysis by targeted bisulfite sequencing on 18 tumor suppressor gene promoters (330 CpG sites). We found i) hypermethylation in EPCAM (17 CpG sites; p = 0.017) and RAD51C (27 CpG sites; p = 0.048); ii) hypermethylation in 36 CpG-specific sites (FDR q < 0.05) in the BC patients; iii) four specific CpG sites were associated with a higher risk of BC (FDR q < 0.01, Bonferroni p < 0.001): cg89786999-FANCI (OR = 1.65; 95% CI:1.2-2.2), cg23652916-PALB2 (OR = 2.83; 95% CI:1.7-4.7), cg47630224-MSH2 (OR = 4.17; 95% CI:2.1-8.5), and cg47596828-EPCAM (OR = 1.84; 95% CI:1.5-2.3). Validation of cg47630224-MSH2 methylation in one Australian cohort showed an association with 3-fold increased BC risk (AUC: 0.929; 95% CI: 0.904-0.955). Our findings suggest that four DNA methylation CpG sites may be associated with a higher risk of BC, potentially serving as biomarkers in patients without detectable coding mutations.

Circulating tumor DNA: from discovery to clinical application in breast cancer

Breast cancer (BC) stands out as the cancer with the highest incidence of morbidity and mortality among women worldwide, and its incidence rate is currently trending upwards. Improving the efficiency of breast cancer diagnosis and treatment is crucial, as it can effectively reduce the disease burden. Circulating tumor DNA (ctDNA) originates from the release of tumor cells and plays a pivotal role in the occurrence, development, and metastasis of breast cancer. In recent years, the widespread application of high-throughput analytical technology has made ctDNA a promising biomarker for early cancer detection, monitoring minimal residual disease, early recurrence monitoring, and predicting treatment outcomes. ctDNA-based approaches can effectively compensate for the shortcomings of traditional screening and monitoring methods, which fail to provide real-time information and prospective guidance for breast cancer diagnosis and treatment. This review summarizes the applications of ctDNA in various aspects of breast cancer, including screening, diagnosis, prognosis, treatment, and follow-up. It highlights the current research status in this field and emphasizes the potential for future large-scale clinical applications of ctDNA-based approaches.

DNA methylation at quantitative trait loci (mQTLs) varies with cell type and nonheritable factors and may improve breast cancer risk assessment

To individualise breast cancer (BC) prevention, markers to follow a person's changing environment and health extending beyond static genetic risk scores are required. Here, we analysed cervical and breast DNA methylation (n = 1848) and single nucleotide polymorphisms (n = 1442) and demonstrate that a linear combination of methylation levels at 104 BC-associated methylation quantitative trait loci (mQTL) CpGs, termed the WID™-qtBC index, can identify women with breast cancer in hormone-sensitive tissues (AUC = 0.71 [95% CI: 0.65-0.77] in cervical samples). Women in the highest combined risk group (high polygenic risk score and WID™-qtBC) had a 9.6-fold increased risk for BC [95% CI: 4.7-21] compared to the low-risk group and tended to present at more advanced stages. Importantly, the WID™-qtBC is influenced by non-genetic BC risk factors, including age and body mass index, and can be modified by a preventive pharmacological intervention, indicating an interaction between genome and environment recorded at the level of the epigenome. Our findings indicate that methylation levels at mQTLs in relevant surrogate tissues could enable integration of heritable and non-heritable factors for improved disease risk stratification.

Analytical sensitivity of a method is critical in detection of low-level BRCA1 constitutional epimutation

Recent reports based on a substantial number of cases, warrant need for population-based research to determine implications of constitutional methylation of tumor suppressor genes such as BRCA1 occurring in healthy tissue in the prediction of cancer. However, the detection of the constitutional methylation in DNA extracted from blood has already been shown to be technologically challenging, mainly because epimutations appear to be present in blood at a very low level. The analytical sensitivity required for low-level methylation detection can be provided by NGS, but this technique is still labor and cost-intensive. We assessed if PCR-based MS-HRM and BeadChip microarray technologies, which are standardized and cost-effective technologies for methylation changes screening, provide a sufficient level of analytical sensitivity for constitutional BRCA1 methylation detection in blood samples. The study included whole blood samples from 67 healthy women, 35 with previously confirmed and 32 with no detectable BRCA1 promoter methylation for which we performed both MS-HRM based BRCA1 gene methylation screening and genome wide methylation profiling with EPIC microarray. Our results shown, that low-level BRCA1 methylation can be effectively detected in DNA extracted from blood by PCR-based MS-HRM. At the same time, EPIC microarray does not provide conclusive results to unambiguously determine the presence of BRCA1 constitutional methylation in MS-HRM epimutation positive samples. The analytical sensitivity of MS-HRM is sufficient to detect low level BRCA1 constitutional epimutation in DNA extracted from blood and BeadChip technology-based microarrays appear not to provide that level of analytical sensitivity.

Plasma One-Carbon Metabolism-Related Micronutrients and the Risk of Breast Cancer: Involvement of DNA Methylation

Findings of epidemiologic studies focusing on the association between one-carbon metabolism-related micronutrients and breast cancer risk, along with the involvement of DNA methylation, have been inconsistent and incomprehensive. We conducted a case-control study in China including 107 paired participants and comprehensively detected 12 plasma one-carbon metabolism-related micronutrients. Genomic DNA methylation was measured using an 850 K chip and differential methylation probes (DMPs) were identified. Multivariate logistic regression was performed to estimate the associations between plasma micronutrients and the odds of breast cancer. The mediation of selected DMPs in micronutrient breast cancer associations was examined using mediation analyses. An inverse association of plasma folate, methionine cycling-related micronutrients (methionine, S-adenosylmethionine, and S-adenosylhomocysteine), and all micronutrients in the choline metabolism and enzymatic factor groups, and a positive association of methionine cycling-related cysteine with breast cancer risk were observed. Nine micronutrients (methionine, cysteine, SAM, folate, choline, betaine, P5P, vitamins B2, and B12) were related to global or probe-specific methylation levels (p < 0.05). The selected DMPs mediated the micronutrient breast cancer associations with an average mediation proportion of 36.43%. This study depicted comprehensive associations between circulating one-carbon metabolism-related micronutrients and breast cancer risk mediated by DNA methylation.

Dual roles of demethylation in cancer treatment and cardio-function recovery

There are no effective therapeutic targets or strategies that simultaneously inhibit tumour growth and promote cardiac function recovery. Here, we analyzed targets for cancer treatments and cardiac repair, with demethylation emerging as a common factor in these candidate lists. As DNA methyltransferase 1 (DNMT1) majorly responds to methylation, a natural compound library is screened, identifying dioscin as a novel agent targeted at DNMT1, widely used for heart diseases. Dioscin was found to reduce DNMT activities and inhibits growth in breast cancer cells. Combined with analyses of RNA-seq and MeDIP-seq, the promoters of antioxidant genes were demethylated after dioscin, recruiting NRF2 and elevating their expression. In Nrf2 knockout mice, the cardiac protection role of dioscin was blocked by Nrf2-loss. Furthermore, in tumour-bearing mice with hypertrophy, dioscin was observed to inhibit tumour growth and alleviate cardiac injury simultaneously. This study is the first to identify dioscin as a novel demethylation agent with dual functions of anti-cancer and cardio-protection.

The association between CTSZ methylation in peripheral blood and breast cancer in Chinese women

Purpose

Previous studies have shown that DNA methylation in peripheral blood may be associated with breast cancer (BC). To explore the association between the methylation level of the Cathepsin Z (CTSZ) gene in peripheral blood and BC, we conducted a case-control study in the Chinese population.

Methods

Peripheral blood samples were collected from 567 BC cases, 635 healthy controls, and 303 benign breast disease (BBD) cases. DNA extraction and bisulfite-specific PCR amplification were performed for all samples. The methylation levels of seven sites of the CTSZ gene were quantitatively determined by Mass spectrometry. The odds ratios (ORs) of CpG sites were evaluated for BC risk using per 10% reduction and quartiles analyses by logistic regression.

Results

Our analysis showed that five out of the seven CpG sites exhibited significant associations with hypomethylation of CTSZ and BC, compared to healthy controls. The highest OR was for Q2 of CTSZ_CpG_1 (OR: 1.62, P=0.006), particularly for early-stage breast cancer in the case of per 10% reduction of CTSZ_CpG_1 (OR: 1.20, P=0.003). We also found that per 10% reduction of CTSZ_CpG_5 (OR: 1.39, P=0.004) and CTSZ_CpG_7,8 (OR: 1.35, P=0.005) were associated with increased BC risk. Our study also revealed that four out of seven CpG sites were linked to increased BC risk in women under 50 years of age, compared to healthy controls. The highest OR was for per 10% reduction of CTSZ_CpG_1 (OR: 1.47, P<0.001). Additionally, we found that BC exhibited lower methylation levels than BBD at CTSZ_CpG_4 (OR for Q1: 2.18, P<0.001) and CTSZ_CpG_7,8 (OR for Q1: 2.01, P=0.001). Furthermore, we observed a correlation between methylation levels and tumor stage, ER, and HER2 status in BC patients.

Conclusion

Overall, our findings suggest that altered CTSZ methylation levels in peripheral blood may be associated with breast cancer, particularly in young women, and may serve as a potential biomarker for early-stage BC.

Multimodal analysis of genome-wide methylation, copy number aberrations, and end motif signatures enhances detection of early-stage breast cancer

Introduction

Breast cancer causes the most cancer-related death in women and is the costliest cancer in the US regarding medical service and prescription drug expenses. Breast cancer screening is recommended by health authorities in the US, but current screening efforts are often compromised by high false positive rates. Liquid biopsy based on circulating tumor DNA (ctDNA) has emerged as a potential approach to screen for cancer. However, the detection of breast cancer, particularly in early stages, is challenging due to the low amount of ctDNA and heterogeneity of molecular subtypes.

Methods

Here, we employed a multimodal approach, namely Screen for the Presence of Tumor by DNA Methylation and Size (SPOT-MAS), to simultaneously analyze multiple signatures of cell free DNA (cfDNA) in plasma samples of 239 nonmetastatic breast cancer patients and 278 healthy subjects.

Results

We identified distinct profiles of genome-wide methylation changes (GWM), copy number alterations (CNA), and 4-nucleotide oligomer (4-mer) end motifs (EM) in cfDNA of breast cancer patients. We further used all three signatures to construct a multi-featured machine learning model and showed that the combination model outperformed base models built from individual features, achieving an AUC of 0.91 (95% CI: 0.87-0.95), a sensitivity of 65% at 96% specificity.

Discussion

Our findings showed that a multimodal liquid biopsy assay based on analysis of cfDNA methylation, CNA and EM could enhance the accuracy for the detection of early- stage breast cancer.

Identification of novel susceptibility methylation loci for pancreatic cancer in a two-phase epigenome-wide association study

The role of DNA methylation and its interplay with gene expression in the susceptibility to pancreatic cancer (PanC) remains largely unexplored. To fill in this gap, we conducted an integrative two-phase epigenome-wide association study (EWAS) of PanC using genomic DNA from 44 cases and 556 controls (20 local controls and 536 public controls in the Framingham Heart Study) in phase 1 and 23 cases and 22 controls in phase 2. We validated the findings using pre-diagnostic blood samples from 13 cases and 26 controls in the Women's Health Initiative (WHI) Study. We further examined gene expression in peripheral leukocytes of 47 cases and 31 controls involved in the methylation study using RNA sequencing and performed bidirectional Mendelian randomization (MR) analysis using existing single nucleotide polymorphism (SNP) data. In phase 1, we identified 2776 significantly differentially methylated CpG sites (DMPs) and 154 significantly differentially methylated regions (DMRs). In phase 2, we validated six DMPs (in or near AIM2, DGKA, STK39, and TNFSF8) and three DMRs (in or near nc886, LY6G5C, and HLA-DPB1). The DMR near nc886 was further validated in the WHI samples (P = 6.69 × 10-5). MR analysis suggested that the CpG sites cg00308130 and cg16684184 for nc886 and cg16875057 for STK39 were causally related to PanC susceptibility and that PanC influenced methylation of cg15354065 for DGKA. This first integrative EWAS of PanC provides novel insights into the role of DNA methylation and its interplay with SNPs and gene expression in the disease susceptibility.

A Deep Survival EWAS approach estimating risk profile based on pre-diagnostic DNA methylation: An application to breast cancer time to diagnosis

Previous studies for cancer biomarker discovery based on pre-diagnostic blood DNA methylation (DNAm) profiles, either ignore the explicit modeling of the Time To Diagnosis (TTD), or provide inconsistent results. This lack of consistency is likely due to the limitations of standard EWAS approaches, that model the effect of DNAm at CpG sites on TTD independently. In this work, we aim to identify blood DNAm profiles associated with TTD, with the aim to improve the reliability of the results, as well as their biological meaningfulness. We argue that a global approach to estimate CpG sites effect profile should capture the complex (potentially non-linear) relationships interplaying between sites. To prove our concept, we develop a new Deep Learning-based approach assessing the relevance of individual CpG Islands (i.e., assigning a weight to each site) in determining TTD while modeling their combined effect in a survival analysis scenario. The algorithm combines a tailored sampling procedure with DNAm sites agglomeration, deep non-linear survival modeling and SHapley Additive exPlanations (SHAP) values estimation to aid robustness of the derived effects profile. The proposed approach deals with the common complexities arising from epidemiological studies, such as small sample size, noise, and low signal-to-noise ratio of blood-derived DNAm. We apply our approach to a prospective case-control study on breast cancer nested in the EPIC Italy cohort and we perform weighted gene-set enrichment analyses to demonstrate the biological meaningfulness of the obtained results. We compared the results of Deep Survival EWAS with those of a traditional EWAS approach, demonstrating that our method performs better than the standard approach in identifying biologically relevant pathways.

Blood-derived DNAm profiles could be exploited as new biomarkers for cancer risk stratification and possibly, early detection. This is of particular interest since blood is a convenient tissue to assay for constitutional methylation and its collection is non-invasive. Exploiting pre-diagnostic blood DNAm data opens the further opportunity to investigate the association of DNAm at baseline on cancer risk, modeling the relationship between sites’ methylation and the Time to Diagnosis. Previous studies mostly provide inconsistent results likely due to the limitations of standard EWAS approaches, that model the effect of DNAm at CpG sites on TTD independently. In this work we argue that an approach to estimate single CpG sites’ effect while modeling their combined effect on the survival outcome is needed, and we claim that such approach should capture the complex (potentially non-linear) relationships interplaying between sites. We prove this concept by developing a novel approach to analyze a prospective case-control study on breast cancer nested in the EPIC Italy cohort. A weighted gene set enrichment analysis confirms that our approach outperforms standard EWAS in identifying biologically meaningful pathways.

Identification of NHLRC1 as a Novel AKT Activator from a Lung Cancer Epigenome-Wide Association Study (EWAS)

Changes in DNA methylation identified by epigenome-wide association studies (EWAS) have been recently linked to increased lung cancer risk. However, the cellular effects of these differentially methylated positions (DMPs) are often unclear. Therefore, we investigated top differentially methylated positions identified from an EWAS study. This included a putative regulatory region of NHLRC1. Hypomethylation of this gene was recently linked with decreased survival rates in lung cancer patients. HumanMethylation450 BeadChip array (450K) analysis was performed on 66 lung cancer case-control pairs from the European Prospective Investigation into Cancer and Nutrition Heidelberg lung cancer EWAS (EPIC HD) cohort. DMPs identified in these pre-diagnostic blood samples were then investigated for differential DNA methylation in lung tumor versus adjacent normal lung tissue from The Cancer Genome Atlas (TCGA) and replicated in two independent lung tumor versus adjacent normal tissue replication sets with MassARRAY. The EPIC HD top hypermethylated DMP cg06646708 was found to be a hypomethylated region in multiple data sets of lung tumor versus adjacent normal tissue. Hypomethylation within this region caused increased mRNA transcription of the closest gene NHLRC1 in lung tumors. In functional assays, we demonstrate attenuated proliferation, viability, migration, and invasion upon NHLRC1 knock-down in lung cancer cells. Furthermore, diminished AKT phosphorylation at serine 473 causing expression of pro-apoptotic AKT-repressed genes was detected in these knock-down experiments. In conclusion, this study demonstrates the powerful potential for discovery of novel functional mechanisms in oncogenesis based on EWAS DNA methylation data. NHLRC1 holds promise as a new prognostic biomarker for lung cancer survival and prognosis, as well as a target for novel treatment strategies in lung cancer patients.

Methylation-based markers of aging and lifestyle-related factors and risk of breast cancer: a pooled analysis of four prospective studies

BACKGROUND

DNA methylation in blood may reflect adverse exposures accumulated over the lifetime and could therefore provide potential improvements in the prediction of cancer risk. A substantial body of research has shown associations between epigenetic aging and risk of disease, including cancer. Here we aimed to study epigenetic measures of aging and lifestyle-related factors in association with risk of breast cancer.

METHODS

Using data from four prospective case-control studies nested in three cohorts of European ancestry participants, including a total of 1,655 breast cancer cases, we calculated three methylation-based measures of lifestyle factors (body mass index [BMI], tobacco smoking and alcohol consumption) and seven measures of epigenetic aging (Horvath-based, Hannum-based, PhenoAge and GrimAge). All measures were regression-adjusted for their respective risk factors and expressed per standard deviation (SD). Odds ratios (OR) and 95% confidence intervals (CI) were calculated using conditional or unconditional logistic regression and pooled using fixed-effects meta-analysis. Subgroup analyses were conducted by age at blood draw, time from blood sample to diagnosis, oestrogen receptor-positivity status and tumour stage.

RESULTS

None of the measures of epigenetic aging were associated with risk of breast cancer in the pooled analysis: Horvath 'age acceleration' (AA): OR per SD = 1.02, 95%CI: 0.95-1.10; AA-Hannum: OR = 1.03, 95%CI:0.95-1.12; PhenoAge: OR = 1.01, 95%CI: 0.94-1.09 and GrimAge: OR = 1.03, 95%CI: 0.94-1.12, in models adjusting for white blood cell proportions, body mass index, smoking and alcohol consumption. The BMI-adjusted predictor of BMI was associated with breast cancer risk, OR per SD = 1.09, 95%CI: 1.01-1.17. The results for the alcohol and smoking methylation-based predictors were consistent with a null association. Risk did not appear to substantially vary by age at blood draw, time to diagnosis or tumour characteristics.

CONCLUSION

We found no evidence that methylation-based measures of aging, smoking or alcohol consumption were associated with risk of breast cancer. A methylation-based marker of BMI was associated with risk and may provide insights into the underlying associations between BMI and breast cancer.

Advances of Epigenetic Biomarkers and Epigenome Editing for Early Diagnosis in Breast Cancer

Epigenetic modifications are known to regulate cell phenotype during cancer progression, including breast cancer. Unlike genetic alterations, changes in the epigenome are reversible, thus potentially reversed by epi-drugs. Breast cancer, the most common cause of cancer death worldwide in women, encompasses multiple histopathological and molecular subtypes. Several lines of evidence demonstrated distortion of the epigenetic landscape in breast cancer. Interestingly, mammary cells isolated from breast cancer patients and cultured ex vivo maintained the tumorigenic phenotype and exhibited aberrant epigenetic modifications. Recent studies indicated that the therapeutic efficiency for breast cancer regimens has increased over time, resulting in reduced mortality. Future medical treatment for breast cancer patients, however, will likely depend upon a better understanding of epigenetic modifications. The present review aims to outline different epigenetic mechanisms including DNA methylation, histone modifications, and ncRNAs with their impact on breast cancer, as well as to discuss studies highlighting the central role of epigenetic mechanisms in breast cancer pathogenesis. We propose new research areas that may facilitate locus-specific epigenome editing as breast cancer therapeutics.

Pre-diagnostic DNA methylation in blood leucocytes in cutaneous melanoma; a nested case–control study within the Norwegian Women and Cancer cohort

The prognosis of cutaneous melanoma depends on early detection, and good biomarkers for melanoma risk may provide a valuable tool to detect melanoma development at a pre-clinical stage. By studying the epigenetic profile in pre-diagnostic blood samples of melanoma cases and cancer free controls, we aimed to identify DNA methylation sites conferring melanoma risk. DNA methylation was measured at 775,528 CpG sites using the Illumina EPIC array in whole blood in incident melanoma cases (n = 183) and matched cancer-free controls (n = 183) in the Norwegian Women and Cancer cohort. Phenotypic information and ultraviolet radiation exposure were obtained from questionnaires. Epigenome wide association (EWAS) was analyzed in future melanoma cases and controls with conditional logistic regression, with correction for multiple testing using the false discovery rate (FDR). We extended the analysis by including a public data set on melanoma (GSE120878), and combining these different data sets using a version of covariate modulated FDR (AdaPT). The analysis on future melanoma cases and controls did not identify any genome wide significant CpG sites (0.85 ≤ p_adj ≤ 0.99). In the restricted AdaPT analysis, 7 CpG sites were suggestive at the FDR level of 0.15. These CpG sites may potentially be used as pre-diagnostic biomarkers of melanoma risk.

Emerging Roles of Long Noncoding RNAs in Breast Cancer Epigenetics and Epitranscriptomics

Breast carcinogenesis is a multistep process that involves both genetic and epigenetic changes. Epigenetics refers to reversible changes in gene expression that are not accompanied by changes in gene sequence. In breast cancer (BC), dysregulated epigenetic changes, such as DNA methylation and histone modifications, are accompanied by epitranscriptomic changes, in particular adenine to inosine modifications within RNA molecules. Factors that trigger these phenomena are largely unknown, but there is evidence for widespread participation of long noncoding RNAs (lncRNAs) that already have been linked to virtually any aspect of BC biology, making them promising biomarkers and therapeutic targets in BC patients. Here, we provide a systematic review of known and possible roles of lncRNAs in epigenetic and epitranscriptomic processes, along with methods and tools to study them, followed by a brief overview of current challenges regarding the use of lncRNAs in medical applications.

NOS2 and S-nitrosothiol signaling induces DNA hypomethylation and LINE-1 retrotransposon expression

Inducible nitric oxide synthase (NOS2) produces high local concentrations of nitric oxide (NO), and its expression is associated with inflammation, cellular stress signals, and cellular transformation. Additionally, NOS2 expression results in aggressive cancer cell phenotypes and is correlated with poor outcomes in patients with breast cancer. DNA hypomethylation, especially of noncoding repeat elements, is an early event in carcinogenesis and is a common feature of cancer cells. In addition to altered gene expression, DNA hypomethylation results in genomic instability via retrotransposon activation. Here, we show that NOS2 expression and associated NO signaling results in substantial DNA hypomethylation in human cell lines by inducing the degradation of DNA (cytosine-5)–methyltransferase 1 (DNMT1) protein. Similarly, NOS2 expression levels were correlated with decreased DNA methylation in human breast tumors. NOS2 expression and NO signaling also resulted in long interspersed noncoding element 1 (LINE-1) retrotransposon hypomethylation, expression, and DNA damage. DNMT1 degradation was mediated by an NO/p38-MAPK/lysine acetyltransferase 5–dependent mechanism. Furthermore, we show that this mechanism is required for NO-mediated epithelial transformation. Therefore, we conclude that NOS2 and NO signaling results in DNA damage and malignant cellular transformation via an epigenetic mechanism.

Cell-type heterogeneity: Why we should adjust for it in epigenome and biomarker studies

Most studies aiming to identify epigenetic biomarkers do so from complex tissues that are composed of many different cell-types. By definition, these cell-types vary substantially in terms of their epigenetic profiles. This cell-type specific variation among healthy cells is completely independent of the variation associated with disease, yet it dominates the epigenetic variability landscape. While cell-type composition of tissues can change in disease and this may provide accurate and reproducible biomarkers, not adjusting for the underlying cell-type heterogeneity may seriously limit the sensitivity and precision to detect disease-relevant biomarkers or hamper our understanding of such biomarkers. Given that computational and experimental tools for tackling cell-type heterogeneity are available, we here stress that future epigenetic biomarker studies should aim to provide estimates of underlying cell-type fractions for all samples in the study, and to identify biomarkers before and after adjustment for cell-type heterogeneity, in order to obtain a more complete and unbiased picture of the biomarker-landscape. This is critical, not only to improve reproducibility and for the eventual clinical application of such biomarkers, but importantly, to also improve our molecular understanding of disease itself.

The WID-BC-index identifies women with primary poor prognostic breast cancer based on DNA methylation in cervical samples

Genetic and non-genetic factors contribute to breast cancer development. An epigenome-based signature capturing these components in easily accessible samples could identify women at risk. Here, we analyse the DNA methylome in 2,818 cervical, 357 and 227 matched buccal and blood samples respectively, and 42 breast tissue samples from women with and without breast cancer. Utilising cervical liquid-based cytology samples, we develop the DNA methylation-based Women’s risk IDentification for Breast Cancer index (WID-BC-index) that identifies women with breast cancer with an AUROC (Area Under the Receiver Operator Characteristic) of 0.84 (95% CI: 0.80–0.88) and 0.81 (95% CI: 0.76–0.86) in internal and external validation sets, respectively. CpGs at progesterone receptor binding sites hypomethylated in normal breast tissue of women with breast cancer or in BRCA mutation carriers are also hypomethylated in cervical samples of women with poor prognostic breast cancer. Our data indicate that a systemic epigenetic programming defect is highly prevalent in women who develop breast cancer. Further studies validating the WID-BC-index may enable clinical implementation for monitoring breast cancer risk.

Breast cancer is most commonly diagnosed via a needle biopsy. In this study, the authors show that cervical samples from women with breast cancer have a methylation signature different to that of healthy controls.

Methylation biomarkers of polybrominated diphenyl ethers (PBDEs) and association with breast cancer risk at the time of menopause

BACKGROUND

Exposure to polybrominated diphenyl ethers (PBDEs) may influence risk of developing post-menopausal breast cancer. Although mechanisms are poorly understood, epigenetic regulation of gene expression may play a role.

OBJECTIVES

To identify DNA methylation (DNAm) changes associated with PBDE serum levels and test the association of these biomarkers with breast cancer risk.

METHODS

We studied 397 healthy women (controls) and 133 women diagnosed with breast cancer (cases) between ages 40 and 58 years who participated in the California Teachers Study. PBDE levels were measured in blood. Infinium Human Methylation EPIC Bead Chips were used to measure DNAm. Using multivariable linear regression models, differentially methylated CpG sites (DMSs) and regions (DMRs) associated with serum PBDE levels were identified using controls. For top-ranked DMSs and DMRs, targeted next-generation bisulfite sequencing was used to measure DNAm for 133 invasive breast cancer cases and 301 age-matched controls. Conditional logistic regression was used to evaluate associations between DMSs and DMRs and breast cancer risk.

RESULTS

We identified 15 DMSs and 10 DMRs statistically significantly associated with PBDE levels (FDR < 0.05). Methylation changes in a DMS at BMP8B and DMRs at TP53 and A2M-AS1 were statistically significantly (FDR < 0.05) associated with breast cancer risk.

CONCLUSION

We show for the first time that serum PBDE levels are associated with differential methylation and that PBDE-associated DNAm changes in blood are associated with breast cancer risk.

Breast cancer risk in relation to ambient concentrations of nitrogen dioxide and particulate matter: results of a population-based case-control study corrected for potential selection bias (the CECILE study)

BACKGROUND

There is only scant evidence that air pollution increases the risk of breast cancer.

OBJECTIVES

We investigated this relationship for three air pollutants: nitrogen dioxide (NO₂) and particulate matter with an aerodynamical diameter below 10 µm (PM₁₀) and 2.5 µm (PM_2.5).

METHODS

We conducted a population-based case-control study on breast cancer in two French départements, including 1,229 women diagnosed with breast cancer in 2005-2007 and 1,316 control women frequency-matched on age. Concentrations of NO₂, PM₁₀ and PM_2.5 at participants' addresses occupied during the last 10 years were assessed using a chemistry transport model. Odds ratios (OR) and 95% confidence intervals (95% CI) were estimated using multivariable logistic regression models where each woman was assigned a weight depending on her probability of selection into the study.

RESULTS

The OR for breast cancer per 10-µg/m³ increase in NO₂ was 1.11 (95% CI, 0.98, 1.26), and 1.41 (95% CI 1.07, 1.86) in the highest exposure quintile (Q5), compared to the first. The ORs per 10-µg/m³ NO₂ did not markedly differ between pre- (OR 1.09, 95% CI 0.89, 1.35)) and post-menopausal women (OR 1.14, 95% CI 0.97, 1.33)), but the OR was substantially higher for hormone-receptor positive (ER+/PR+) breast tumor subtypes (OR 1.15, 95% CI 1.00, 1.31) than for ER-/PR- tumors (OR 0.95, 95% CI 0.72, 1.26). Breast cancer risk was not associated with either PM₁₀ (OR per 1 µg/m³ 1.01, 95% CI, 0.96, 1.06) or PM_2.5 (OR per 1 µg/m³ 1.02, 95% CI 0.95, 1.08), regardless of the menopausal status or of the breast tumor subtype.

DISCUSSION

Our study provides evidence that NO₂ exposure, a marker of traffic-related air pollutants, may be associated with an increased risk of breast cancer, particularly ER+/PR+ tumors.

Connecting the epigenome, metabolome and proteome for a deeper understanding of disease

Epigenome-wide association studies (EWAS) identify genes that are dysregulated by the studied clinical endpoints, thereby indicating potential new diagnostic biomarkers, drug targets and therapy options. Combining EWAS with deep molecular phenotyping, such as approaches enabled by metabolomics and proteomics, allows further probing of the underlying disease-associated pathways. For instance, methylation of the TXNIP gene is associated robustly with prevalent type 2 diabetes and further with metabolites that are short-term markers of glycaemic control. These associations reflect TXNIP's function as a glucose uptake regulator by interaction with the major glucose transporter GLUT1 and suggest that TXNIP methylation can be used as a read-out for the organism's exposure to glucose stress. Another case is the association between DNA methylation of the AHRR and F2RL3 genes with smoking and a protein that is involved in the reprogramming of the bronchial epithelium. These examples show that associations between DNA methylation and intermediate molecular traits can open new windows into how the body copes with physiological challenges. This knowledge, if carefully interpreted, may indicate novel therapy options and, together with monitoring of the methylation state of specific methylation sites, may in the future allow the early diagnosis of impending disease. It is essential for medical practitioners to recognize the potential that this field holds in translating basic research findings to clinical practice. In this review, we present recent advances in the field of EWAS with metabolomics and proteomics and discuss both the potential and the challenges of translating epigenetic associations, with deep molecular phenotypes, to biomedical applications.

Gene-associated methylation status of ST14 as a predictor of survival and hormone receptor positivity in breast Cancer

Background

Genomic profiles of specific gene sets have been established to guide personalized treatment and prognosis for patients with breast cancer (BC). However, epigenomic information has not yet been applied in a clinical setting. ST14 encodes matriptase, a proteinase that is widely expressed in BC with reported prognostic value.

Methods

In this present study, we evaluated the effect of ST14 DNA methylation (DNAm) on overall survival (OS) of patients with BC as a representative example to promote the use of the epigenome in clinical decisions. We analyzed publicly available genomic and epigenomic data from 1361 BC patients. Methylation was characterized by the β-value from CpG probes based on sequencing with the Illumina Human 450 K platform.

Results

A high mean DNAm (β > 0.6779) across 34 CpG probes for ST14, as the gene-associated methylation (GAM) pattern, was associated with a longer OS after adjusting age, stage, histology and molecular features in Cox model (p value < 0.001). A high GAM status was also associated with a higher XBP1 expression level and higher proportion of hormone-positive BC (p value < 0.001). Pathway analysis revealed that altered GAM was related to matrisome-associated pathway.

Conclusions

Here we show the potential role of ST14 DNAm in BC prognosis and warrant further study.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08645-3.

Blood DNA methylation profiles improve breast cancer prediction

Although blood DNA methylation (DNAm) profiles are reported to be associated with breast cancer incidence, they have not been widely used in breast cancer risk assessment. Among a breast cancer case–cohort of 2774 women (1551 cases) in the Sister Study, we used candidate CpGs and DNAm estimators of physiologic characteristics to derive a methylation‐based breast cancer risk score, mBCRS. Overall, 19 CpGs and five DNAm estimators were selected using elastic net regularization to comprise mBCRS. In a test set, higher mBCRS was positively associated with breast cancer incidence, showing similar strength to the polygenic risk score (PRS) based on 313 single nucleotide polymorphisms (313 SNPs). Area under the curve for breast cancer prediction was 0.60 for self‐reported risk factors (RFs), 0.63 for PRS, and 0.63 for mBCRS. Adding mBCRS to PRS and RFs improved breast cancer prediction from 0.66 to 0.71. mBCRS findings were replicated in a nested case–control study within the EPIC‐Italy cohort. These results suggest that mBCRS, a risk score derived using blood DNAm, can be used to enhance breast cancer prediction.

Circulating cell-free DNA-based methylation patterns for breast cancer diagnosis

Mammography is used to detect breast cancer (BC), but its sensitivity is limited, especially for dense breasts. Circulating cell-free DNA (cfDNA) methylation tests is expected to compensate for the deficiency of mammography. We derived a specific panel of markers based on computational analysis of the DNA methylation profiles from The Cancer Genome Atlas (TCGA). Through training (n = 160) and validation set (n = 69), we developed a diagnostic prediction model with 26 markers, which yielded a sensitivity of 89.37% and a specificity of 100% for differentiating malignant disease from normal lesions [AUROC = 0.9816 (95% CI: 96.09-100%), and AUPRC = 0.9704 (95% CI: 94.54–99.46%)]. A simplified 4-marker model including cg23035715, cg16304215, cg20072171, and cg21501525 had a similar diagnostic power [AUROC = 0.9796 (95% CI: 95.56–100%), and AUPRC = 0.9220 (95% CI: 91.02–94.37%)]. We found that a single cfDNA methylation marker, cg23035715, has a high diagnostic power [AUROC = 0.9395 (95% CI: 89.72–99.27%), and AUPRC = 0.9111 (95% CI: 88.45–93.76%)], with a sensitivity of 84.90% and a specificity of 93.88%. In an independent testing dataset (n = 104), the obtained diagnostic prediction model discriminated BC patients from normal controls with high accuracy [AUROC = 0.9449 (95% CI: 90.07–98.91%), and AUPRC = 0.8640 (95% CI: 82.82–89.98%)]. We compared the diagnostic power of cfDNA methylation and mammography. Our model yielded a sensitivity of 94.79% (95% CI: 78.72–97.87%) and a specificity of 98.70% (95% CI: 86.36–100%) for differentiating malignant disease from normal lesions [AUROC = 0.9815 (95% CI: 96.75–99.55%), and AUPRC = 0.9800 (95% CI: 96.6–99.4%)], with better diagnostic power and had better diagnostic power than that of using mammography [AUROC = 0.9315 (95% CI: 89.95–96.34%), and AUPRC = 0.9490 (95% CI: 91.7–98.1%)]. In addition, hypermethylation profiling provided insights into lymph node metastasis stratifications (p < 0.05). In conclusion, we developed and tested a cfDNA methylation model for BC diagnosis with better performance than mammography.

Prediagnostic White Blood Cell DNA Methylation and Risk of Breast Cancer in the Prostate Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) Cohort

BACKGROUND

White blood cell (WBC) DNA may contain methylation patterns that are associated with subsequent breast cancer risk. Using a high-throughput array and samples collected, on average, 1.3 years prior to diagnosis, a case-cohort analysis nested in the prospective Sister Study identified 250 individual CpG sites that were differentially methylated between breast cancer cases and noncases. We examined five of the top 40 CpG sites in a case-control study nested in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) Cohort.

METHODS

We investigated the associations between prediagnostic WBC DNA methylation in 297 breast cancer cases and 297 frequency-matched controls. Two WBC DNA specimens from each participant were used: a proximate sample collected 1 to 2.9 years and a distant sample collected 4.2-7.3 years prior to diagnosis in cases or the comparable timepoints in controls. WBC DNA methylation level was measured using targeted bisulfite amplification sequencing. We used logistic regression to obtain ORs and 95% confidence intervals (CI).

RESULTS

A one-unit increase in percent methylation in ERCC1 in proximate WBC DNA was associated with increased breast cancer risk (adjusted OR = 1.29; 95% CI, 1.06-1.57). However, a one-unit increase in percent methylation in ERCC1 in distant WBC DNA was inversely associated with breast cancer risk (adjusted OR = 0.83; 95% CI, 0.69-0.98). None of the other ORs met the threshold for statistical significance.

CONCLUSIONS

There was no convincing pattern between percent methylation in the five CpG sites and breast cancer risk.

IMPACT

The link between prediagnostic WBC DNA methylation marks and breast cancer, if any, is poorly understood.

Association of mammographic density with blood DNA methylation

Background: Altered DNA methylation may be an intermediate phenotype between breast cancer risk factors and disease. Mammographic density is a strong risk factor for breast cancer. However, no studies to date have identified an epigenetic signature of mammographic density. We performed an epigenome-wide association study of mammographic density.

Methods: White blood cell DNA methylation was measured for 385 postmenopausal women using the Illumina Infinium MethylationEPIC BeadChip array. Differential methylation was assessed using genome-wide, probe-level, and regional analyses. We implemented a resampling-based approach to improve the stability of our findings.

Results: On average, women with elevated mammographic density exhibited DNA hypermethylation within CpG islands and gene promoters compared to women with lower mammographic density. We identified 250 CpG sites for which DNA methylation was significantly associated with mammographic density. The top sites were located within genes associated with cancer, including HDLBP, TGFB2, CCT4, and PAX8, and were more likely to be located in regulatory regions of the genome. We also identified differential DNA methylation in 37 regions, including within the promoters of PAX8 and PF4, a gene involved in the regulation of angiogenesis. Overall, our results paint a picture of epigenetic dysregulation associated with mammographic density.

Conclusion: Mammographic density is associated with differential DNA methylation throughout the genome, including within genes associated with cancer. Our results suggest the potential involvement of several genes in the biological mechanisms behind differences in breast density between women. Further studies are warranted to explore these potential mechanisms and potential links to breast cancer risk.

Discovery of novel DNA methylation biomarkers for non-invasive sporadic breast cancer detection in the Latino population

Human diversity is one of the main pitfalls in the development of robust worldwide biomarkers in oncology. Epigenetic variability across human populations is associated with different genetic backgrounds, as well as variable lifestyles and environmental exposures, each of which should be investigated. To identify potential non-invasive biomarkers of sporadic breast cancer in the Uruguayan population, we studied genome-wide DNA methylation using Illumina methylation arrays in leukocytes of 22 women with sporadic breast cancer and 10 healthy women in a case-control study. We described a panel of 38 differentially methylated CpG positions that was able to cluster breast cancer patients (BCP) and controls, and that also recapitulated methylation differences in 12 primary breast tumors and their matched normal breast tissue. Moving forward, we simplified the detection method to improve its applicability in a clinical setting and used an independent well-characterized cohort of 80 leukocyte DNA samples from BCP and 80 healthy controls to validate methylation results at specific cancer-related genes. Our investigations identified methylation at CYFIP1 as a novel epigenetic biomarker candidate for sporadic breast cancer in the Uruguayan population. These results provide a proof-of-concept for the design of larger studies aimed at validating biomarker panels for the Latin American population.

Pubertal mammary gland development is a key determinant of adult mammographic density

Mammographic density refers to the radiological appearance of fibroglandular and adipose tissue on a mammogram of the breast. Women with relatively high mammographic density for their age and body mass index are at significantly higher risk for breast cancer. The association between mammographic density and breast cancer risk is well-established, however the molecular and cellular events that lead to the development of high mammographic density are yet to be elucidated. Puberty is a critical time for breast development, where endocrine and paracrine signalling drive development of the mammary gland epithelium, stroma, and adipose tissue. As the relative abundance of these cell types determines the radiological appearance of the adult breast, puberty should be considered as a key developmental stage in the establishment of mammographic density. Epidemiological studies have pointed to the significance of pubertal adipose tissue deposition, as well as timing of menarche and thelarche, on adult mammographic density and breast cancer risk. Activation of hypothalamic-pituitary axes during puberty combined with genetic and epigenetic molecular determinants, together with stromal fibroblasts, extracellular matrix, and immune signalling factors in the mammary gland, act in concert to drive breast development and the relative abundance of different cell types in the adult breast. Here, we discuss the key cellular and molecular mechanisms through which pubertal mammary gland development may affect adult mammographic density and cancer risk.

Epigenome-wide DNA methylation and risk of breast cancer: a systematic review

BACKGROUND

DNA methylation is a potential biomarker for early detection of breast cancer. However, robust evidence of a prospective relationship between DNA methylation patterns and breast cancer risk is still lacking. The objective of this study is to provide a systematic analysis of the findings of epigenome-wide DNA methylation studies on breast cancer risk, in light of their methodological strengths and weaknesses.

METHODS

We searched major databases (MEDLINE, EMBASE, Web of Science, CENTRAL) from inception up to 30th June 2019, for observational or intervention studies investigating the association between epigenome-wide DNA methylation (using the HM450k or EPIC BeadChip), measured in any type of human sample, and breast cancer risk. A pre-established protocol was drawn up following the Cochrane Reviews rigorous methodology. Study selection, data abstraction, and risk of bias assessment were performed by at least two investigators. A qualitative synthesis and systematic comparison of the strengths and weaknesses of studies was performed.

RESULTS

Overall, 20 studies using the HM450k BeadChip were included, 17 of which had measured blood-derived DNA methylation. There was a consistent trend toward an association of global blood-derived DNA hypomethylation and higher epigenetic age with higher risk of breast cancer. The strength of associations was modest for global hypomethylation and relatively weak for most of epigenetic age algorithms. Differences in length of follow-up periods may have influenced the ability to detect associations, as studies reporting follow-up periods shorter than 10 years were more likely to observe an association with global DNA methylation. Probe-wise differential methylation analyses identified between one and 806 differentially methylated CpGs positions in 10 studies. None of the identified differentially methylated sites overlapped between studies. Three studies used breast tissue DNA and suffered major methodological issues that precludes any conclusion. Overall risk of bias was critical mainly because of incomplete control of confounding. Important issues relative to data preprocessing could have limited the consistency of results.

CONCLUSIONS

Global DNA methylation may be a short-term predictor of breast cancer risk. Further studies with rigorous methodology are needed to determine spatial distribution of DNA hypomethylation and identify differentially methylated sites associated with risk of breast cancer.

PROSPERO REGISTRATION NUMBER

CRD42020147244.

A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets

Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible relationship between human disease and epigenetic variability. DNA samples from peripheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylation differences related to a particular phenotype. Since information on the cell-type composition of the sample is generally not available and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-type heterogeneity in EWAS. In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linear mixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variable analysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied a multilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimated methylation differences according to major study characteristics. While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASher resulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-type heterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results based on real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher and SmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimated methylation differences and runtime.

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.

Blood DNA Methylation and Breast Cancer: A Prospective Case-Cohort Analysis in the Sister Study

BACKGROUND

Peripheral blood DNA methylation may be associated with breast cancer, but studies of candidate genes and global and genome-wide DNA methylation have been inconsistent.

METHODS

We performed an epigenome-wide study using Infinium HumanMethylation450 BeadChips with prospectively collected blood DNA samples from the Sister Study (1552 cases, 1224 subcohort). Differentially methylated cytosine-phosphate-guanine sites (dmCpGs) were identified using case-cohort proportional hazard models and replicated using deposited data from European Prospective Investigation into Cancer and Nutrition in Italy (EPIC-Italy) (n = 329). The correlation between methylation and time to diagnosis was examined using robust linear regression. Causal or consequential relationships of methylation to breast cancer were examined by Mendelian randomization using OncoArray 500 K single-nucleotide polymorphism data. All statistical tests were two-sided.

RESULTS

We identified 9601 CpG markers associated with invasive breast cancer (false discovery rate = q < 0.01), with 510 meeting a strict Bonferroni correction threshold (10-7). A total of 2095 of these CpGs replicated in the independent EPIC-Italy dataset, including 144 meeting the Bonferroni threshold. Sister Study women who developed ductal carcinoma in situ had methylation similar to noncases. Most (1501, 71.6%) dmCpGs showed lower methylation in invasive cases. In case-only analysis, methylation was statistically significantly associated (false discovery rate = q < 0.05) with time to diagnosis for 892 (42.6%) of the dmCpGs. Analyses based on genetic association suggest that methylation differences are likely a consequence rather than a cause of breast cancer. Pathway analysis shows enrichment of breast cancer-related gene pathways, and dmCpGs are overrepresented in known breast cancer susceptibility genes.

CONCLUSIONS

Our findings suggest that the DNA methylation profile of blood starts to change in response to invasive breast cancer years before the tumor is clinically detected.

Lifetime Ultraviolet Radiation Exposure and DNA Methylation in Blood Leukocytes: The Norwegian Women and Cancer Study

Ultraviolet radiation (UVR) exposure is a leading cause of skin cancers and an ubiquitous environmental exposure. However, the molecular mechanisms relating UVR exposure to melanoma is not fully understood. We aimed to investigate if lifetime UVR exposure could be robustly associated to DNA methylation (DNAm). We assessed DNAm in whole blood in three data sets (n = 183, 191, and 125) from the Norwegian Woman and Cancer cohort, using Illumina platforms. We studied genome-wide DNAm, targeted analyses of CpG sites indicated in the literature, global methylation, and accelerated aging. Lifetime history of UVR exposure (residential ambient UVR, sunburns, sunbathing vacations and indoor tanning) was collected by questionnaires. We used one data set for discovery and the other two for replication. One CpG site showed a genome-wide significant association to cumulative UVR exposure (cg01884057) (pnominal = 3.96e-08), but was not replicated in any of the two replication sets (pnominal ≥ 0.42). Two CpG sites (cg05860019, cg00033666) showed suggestive associations with the other UVR exposures. We performed extensive analyses of the association between long-term UVR exposure and DNAm. There was no indication of a robust effect of past UVR exposure on DNAm.

Genetically Predicted Levels of DNA Methylation Biomarkers and Breast Cancer Risk: Data From 228 951 Women of European Descent

BACKGROUND

DNA methylation plays a critical role in breast cancer development. Previous studies have identified DNA methylation marks in white blood cells as promising biomarkers for breast cancer. However, these studies were limited by low statistical power and potential biases. Using a new methodology, we investigated DNA methylation marks for their associations with breast cancer risk.

METHODS

Statistical models were built to predict levels of DNA methylation marks using genetic data and DNA methylation data from HumanMethylation450 BeadChip from the Framingham Heart Study (n = 1595). The prediction models were validated using data from the Women's Health Initiative (n = 883). We applied these models to genomewide association study (GWAS) data of 122 977 breast cancer patients and 105 974 controls to evaluate if the genetically predicted DNA methylation levels at CpG sites (CpGs) are associated with breast cancer risk. All statistical tests were two-sided.

RESULTS

Of the 62 938 CpG sites CpGs investigated, statistically significant associations with breast cancer risk were observed for 450 CpGs at a Bonferroni-corrected threshold of P less than 7.94 × 10-7, including 45 CpGs residing in 18 genomic regions, that have not previously been associated with breast cancer risk. Of the remaining 405 CpGs located within 500 kilobase flaking regions of 70 GWAS-identified breast cancer risk variants, the associations for 11 CpGs were independent of GWAS-identified variants. Integrative analyses of genetic, DNA methylation, and gene expression data found that 38 CpGs may affect breast cancer risk through regulating expression of 21 genes.

CONCLUSION

Our new methodology can identify novel DNA methylation biomarkers for breast cancer risk and can be applied to other diseases.

Integrating DNA methylation measures to improve clinical risk assessment: are we there yet? The case of BRCA1 methylation marks to improve clinical risk assessment of breast cancer

Current risk prediction models estimate the probability of developing breast cancer over a defined period based on information such as family history, non-genetic breast cancer risk factors, genetic information from high and moderate risk breast cancer susceptibility genes and, over the past several years, polygenic risk scores (PRS) from more than 300 common variants. The inclusion of additional data such as PRS improves risk stratification, but it is anticipated that the inclusion of epigenetic marks could further improve model performance accuracy. Here, we present the case for including information on DNA methylation marks to improve the accuracy of these risk prediction models, and consider how this approach contrasts genetic information, as identifying DNA methylation marks associated with breast cancer risk differs inherently according to the source of DNA, approaches to the measurement of DNA methylation, and the timing of measurement. We highlight several DNA-methylation-specific challenges that should be considered when incorporating information on DNA methylation marks into risk prediction models, using BRCA1, a highly penetrant breast cancer susceptibility gene, as an example. Only after careful consideration of study design and DNA methylation measurement will prospective performance of the incorporation of information regarding DNA methylation marks into risk prediction models be valid.

Whole Exome and Transcriptome RNA-Sequencing Model for the Diagnosis of Prostate Cancer

In our previous study, we developed a genome-wide DNA methylation model for the diagnosis of prostate cancer, and we pointed out that a considerable average error is associated with the current method for the diagnosis of prostate cancer, which is predicated on pathological assessment of biopsied tissue. In this study, we utilized whole exome and transcriptome RNA-sequencing (RNA-seq) data that were derived from 468 tumor samples and 51 normal samples of prostatic tissue, and we analyzed over 20,000 genes per sample. We were able to develop a mathematical model that classified tumor tissue versus normal tissue with a high accuracy. The overall sensitivity was 97.01%, and the overall specificity was 94.12%. The input variables to the model were the mRNA expression values of the following nine genes: ANGPT1, MED21, AOX1, PLP2, HPN, HPN-AS1, EPHA10, NKX2-3, and LRFN1. The model was validated with unknown samples, with a 10-fold cross-validation, and a leave-one-out cross-validation. We present here a genomic model, based on a whole exome and transcriptome RNA-seq analysis of biopsied prostatic tissue, that could be utilized in the diagnosis of prostate cancer.

Individual and joint performance of DNA methylation profiles, genetic risk score and environmental risk scores for predicting breast cancer risk

DNA methylation patterns in the blood, genetic risk scores (GRSs), and environmental risk factors can potentially improve breast cancer (BC) risk prediction. We assessed the individual and joint predictive performance of methylation, GRS, and environmental risk factors for BC incidence in a prospective cohort study. In a cohort of 5462 women aged 50–75 from Germany, 101 BC cases were identified during 14 years of follow‐up and were compared to 263 BC‐free controls in a nested case–control design. Three previously suggested methylation risk scores (MRSs) based on methylation of 423, 248, and 131 cytosine‐phosphate‐guanine (CpG) loci, and a GRS based on the risk alleles from 269 recently identified single nucleotide polymorphisms were constructed. Additionally, multiple previously proposed environmental risk scores (ERSs) were built based on environmental variables. Areas under the receiver operating characteristic curves (AUCs) were estimated for evaluating BC risk prediction performance. MRS and ERS showed limited accuracy in predicting BC incidence, with AUCs ranging from 0.52 to 0.56 and from 0.52 to 0.59, respectively. The GRS predicted BC incidence with a higher accuracy (AUC = 0.61). Adjusted odds ratios per standard deviation increase (95% confidence interval) were 1.07 (0.84–1.36) and 1.40 (1.09–1.80) for the best performing MRS and ERS, respectively, and 1.48 (1.16–1.90) for the GRS. A full risk model combining the MRS, GRS, and ERS predicted BC incidence with the highest accuracy (AUC = 0.64) and might be useful for identifying high‐risk populations for BC screening.

DNA methylation-based biological age, genome-wide average DNA methylation, and conventional breast cancer risk factors

DNA methylation-based biological age (DNAm age), as well as genome-wide average DNA methylation, have been reported to predict breast cancer risk. We aimed to investigate the associations between these DNA methylation-based risk factors and 18 conventional breast cancer risk factors for disease-free women. A sample of 479 individuals from the Australian Mammographic Density Twins and Sisters was used for discovery, a sample of 3354 individuals from the Melbourne Collaborative Cohort Study was used for replication, and meta-analyses pooling results from the two studies were conducted. DNAm age based on three epigenetic clocks (Hannum, Horvath and Levine) and genome-wide average DNA methylation were calculated using the HumanMethylation 450 K BeadChip assay data. The DNAm age measures were positively associated with body mass index (BMI), smoking, alcohol drinking and age at menarche (all nominal P < 0.05). Genome-wide average DNA methylation was negatively associated with smoking and number of live births, and positively associated with age at first live birth (all nominal P < 0.05). The association of DNAm age with BMI was also evident in within-twin-pair analyses that control for familial factors. This study suggests that some lifestyle and hormonal risk factors are associated with these DNA methylation-based breast cancer risk factors, and the observed associations are unlikely to be due to familial confounding but are likely causal. DNA methylation-based risk factors could interplay with conventional risk factors in modifying breast cancer risk.

Acceleration in the DNA methylation age in breast cancer tumours from very young women

Breast cancer in very young women (≤35 years; BCVY) presents more aggressive and complex biological features than their older counterparts (BCO). Our aim was to evaluate methylation differences between BCVY and BCO and their DNA epigenetic age. EPIC and 450k Illumina methylation arrays were used in 67 breast cancer tumours, including 32 from BCVY, for methylation study and additionally we analysed their epigenetic age. We identified 2 219 CpG sites differently-methylated in BCVY vs. BCO (FDR < 0.05; β-value difference ± 0.1). The signature showed a general hypomethylation profile with a selective small hypermethylation profile located in open-sea regions in BCVY against BCO and normal tissue. Strikingly, BCVY presented a significant increased epigenetic age-acceleration compared with older women. The affected genes were enriched for pathways in neuronal-system pathways, cell communication, and matrix organisation. Validation in an independent sample highlighted consistent higher expression of HOXD9, and PCDH10 genes in BCVY. Regions implicated in the hypermethylation profile were involved in Notch signalling pathways, the immune system or DNA repair. We further validated HDAC5 expression in BCVY. We have identified a DNA methylation signature that is specific to BCVY and have shown that epigenetic age-acceleration is increased in BCVY.

BRCA1 promoter methylation in peripheral blood is associated with the risk of triple-negative breast cancer

Methylation of the promoter of the BRCA1 gene in DNA derived from peripheral blood cells is a possible risk factor for breast cancer. It is not clear if this association is restricted to certain types of breast cancer or is a general phenomenon. We evaluated BRCA1 methylation status in peripheral blood cells from 942 breast cancer patients and from 500 controls. We also assessed methylation status in 262 paraffin-embedded breast cancer tissues. Methylation status was assessed using methylation-sensitive high-resolution melting and was categorized as positive or negative. BRCA1 methylation in peripheral blood cells was strongly associated with the risk of triple-negative breast cancer (TNBC) (odds ratio [OR] 4.70; 95% confidence interval [CI]: 3.13-7.07; p < 0.001), but not of estrogen-receptor positive breast cancer (OR 0.80; 95% CI: 0.46-1.42; p = 0.46). Methylation was also overrepresented among patients with high-grade cancers (OR 4.53; 95% CI: 2.91-7.05; p < 0.001) and medullary cancers (OR 3.08; 95% CI: 1.38-6.88; p = 0.006). Moreover, we detected a significant concordance of BRCA1 promoter methylation in peripheral blood and paired tumor tissue (p < 0.001). We found that BRCA1 promoter methylation in peripheral blood cells is associated with approximately five times greater risk of TNBC. We propose that BRCA1 methylation in blood-derived DNA could be a novel biomarker of increased breast cancer susceptibility, in particular for triple-negative tumors.

Genome-Wide DNA Methylation Model for the Diagnosis of Prostate Cancer

Prostate cancer is the most prevalent and the second most lethal malignancy among males in the United States of America. Its diagnosis is almost entirely predicated upon histopathological analysis of the biopsied tissue, and it is associated with a substantial average error. Using genome-wide DNA methylation data derived from 469 prostatic tumor tissue samples and 50 normal prostatic tissue samples and interrogating over 485 000 CpG sites per sample (spanning across gene promoters, CpG islands, shores, shelves, gene bodies, and intergenic and other areas), we were able to develop a mathematical model that classified with a high accuracy (overall sensitivity = 95.31% and overall specificity = 94.00%) tumor tissue versus normal tissue. The methylation β values of five CpG sites, corresponding to the genes LINC01091, RPS15,  SNORA10, and two unknown DNA areas in chromosome 1, provided the input to the model. The model was validated with unknown samples, as well as with a sixfold cross-validation and a leave-one-out cross-validation. This study presents a novel genomic model based on genome-wide DNA methylation analysis of biopsied prostatic tissue that could aid in the diagnosis of prostate cancer and help advance the transition to genomic medicine.

Association between long-term air pollution exposure and DNA methylation: The REGICOR study

INTRODUCTION

Limited evidence suggests that epigenetic mechanisms may partially mediate the adverse effects of air pollution on health. Our aims were to identify new genomic loci showing differential DNA methylation associated with long-term exposure to air pollution and to replicate loci previously identified in other studies.

METHODS

A two-stage epigenome-wide association study was designed: 630 individuals from the REGICOR study were included in the discovery and 454 participants of the EPIC-Italy study in the validation stage. DNA methylation was assessed using the Infinium HumanMethylation450 BeadChip. NOX, NO2, PM10, PM2.5, PMcoarse, traffic intensity and traffic load exposure were measured according to the ESCAPE protocol. A systematic review was undertaken to identify those cytosine-phosphate-guanine (CpGs) associated with air pollution in previous studies and we screened for them in the discovery study.

RESULTS

In the discovery stage of the epigenome-wide association study, 81 unique CpGs were associated with air pollution (p-value <10^-5) but none of them were validated in the replication sample. Furthermore, we identified 15 CpGs in the systematic review showing differential methylation with a p-value fulfilling the Bonferroni criteria and 1673 CpGs fulfilling the false discovery rate criteria, all of which were related to PM_2.5 or NO₂. None of them was replicated in the discovery study, in which the top hits were located in an intergenic region on chromosome 1 (cg10893043, p-value = 6.79·10^-5) and in the LRRC45 and PXK genes (cg05088605, p-value = 2.15·10^-04; cg16560256, p-value = 2.23·10^-04).

CONCLUSIONS

Neither new genomic loci associated with long-term air pollution were identified, nor previously identified loci were replicated. Continued efforts to test this potential association are warranted.

Early Epigenetic Markers for Precision Medicine

Over the last years, epigenetic changes, including DNA methylation and histone modifications detected in early tumorigenesis and cancer progression, have been proposed as biomarkers for cancer detection, tumor prognosis, and prediction to treatment response. Importantly for the clinical use of DNA methylation biomarkers, specific methylation signatures can be detected in many body fluids including serum/plasma samples. Several of these potential epigenetic biomarkers detected in women's cancers, colorectal cancers, prostate, pancreatic, gastric, and lung cancers are discussed. Studies conducted in breast cancer, for example, found that aberrant methylation detection of several genes in serum DNA and genome-wide epigenetic change could be used for early breast cancer diagnosis and prediction of breast cancer risk. In colorectal cancers, numerous studies have been conducted to identify specific methylation markers important for CRC detection and in fact clinical assays evaluating the methylation status of SEPT19 gene and vimentin, became commercially available. Furthermore, some epigenetic changes detected in gastric washes have been suggested as potential circulating noninvasive biomarkers for the early detection of gastric cancers. For the early detection of prostate cancer, few epigenetic markers have shown a better sensitivity and specificity than serum PSA, indicating that the inclusion of these markers together with current screening tools, could improve early diagnosis and may reduce unnecessary repeat biopsies. Similarly, in pancreatic cancers, abnormal DNA methylation of several genes including NPTX2, have been suggested as a diagnostic biomarker. Epigenetic dysregulation was also observed in several tumor suppressor genes and miRNAs in lung cancer patients, suggesting the important role of these changes in cancer initiation and progression. In conclusion, epigenetic changes detected in biological fluids could play an essential role in the early detection of several cancer types and this may have a great impact for the cancer precision medicine field.

Blood DNA methylation and breast cancer risk: a meta-analysis of four prospective cohort studies

BACKGROUND

Environmental and genetic factors play an important role in the etiology of breast cancer. Several small blood-based DNA methylation studies have reported risk associations with methylation at individual CpGs and average methylation levels; however, these findings require validation in larger prospective cohort studies. To investigate the role of blood DNA methylation on breast cancer risk, we conducted a meta-analysis of four prospective cohort studies, including a total of 1663 incident cases and 1885 controls, the largest study of blood DNA methylation and breast cancer risk to date.

METHODS

We assessed associations with methylation at 365,145 CpGs present in the HumanMethylation450 (HM450K) Beadchip, after excluding CpGs that did not pass quality controls in all studies. Each of the four cohorts estimated odds ratios (ORs) and 95% confidence intervals (CI) for the association between each individual CpG and breast cancer risk. In addition, each study assessed the association between average methylation measures and breast cancer risk, adjusted and unadjusted for cell-type composition. Study-specific ORs were combined using fixed-effect meta-analysis with inverse variance weights. Stratified analyses were conducted by age at diagnosis (< 50, ≥ 50), estrogen receptor (ER) status (+/-), and time since blood collection (< 5, 5-10, > 10 years). The false discovery rate (q value) was used to account for multiple testing.

RESULTS

The average age at blood draw ranged from 52.2 to 62.2 years across the four cohorts. Median follow-up time ranged from 6.6 to 8.4 years. The methylation measured at individual CpGs was not associated with breast cancer risk (q value > 0.59). In addition, higher average methylation level was not associated with risk of breast cancer (OR = 0.94, 95% CI = 0.85, 1.05; P = 0.26; P for study heterogeneity = 0.86). We found no evidence of modification of this association by age at diagnosis (P = 0.17), ER status (P = 0.88), time since blood collection (P = 0.98), or CpG location (P = 0.98).

CONCLUSIONS

Our data indicate that DNA methylation measured in the blood prior to breast cancer diagnosis in predominantly postmenopausal women is unlikely to be associated with substantial breast cancer risk on the HM450K array. Larger studies or with greater methylation coverage are needed to determine if associations exist between blood DNA methylation and breast cancer risk.

Epigenome-wide association study for lifetime estrogen exposure identifies an epigenetic signature associated with breast cancer risk

BACKGROUND

It is well established that estrogens and other hormonal factors influence breast cancer susceptibility. We hypothesized that a woman's total lifetime estrogen exposure accumulates changes in DNA methylation, detectable in the blood, which could be used in risk assessment for breast cancer.

METHODS

An estimated lifetime estrogen exposure (ELEE) model was defined using epidemiological data from EPIC-Italy (n = 31,864). An epigenome-wide association study (EWAS) of ELEE was performed using existing Illumina HumanMethylation450K Beadchip (HM450K) methylation data obtained from EPIC-Italy blood DNA samples (n = 216). A methylation index (MI) of ELEE based on 31 CpG sites was developed using HM450K data from EPIC-Italy and the Generations Study and evaluated for association with breast cancer risk in an independent dataset from the Generations Study (n = 440 incident breast cancer cases matched to 440 healthy controls) using targeted bisulfite sequencing. Lastly, a meta-analysis was conducted including three additional cohorts, consisting of 1187 case-control pairs.

RESULTS

We observed an estimated 5% increase in breast cancer risk per 1-year longer ELEE (OR = 1.05, 95% CI 1.04-1.07, P = 3 × 10-12) in EPIC-Italy. The EWAS identified 694 CpG sites associated with ELEE (FDR Q < 0.05). We report a DNA methylation index (MI) associated with breast cancer risk that is validated in the Generations Study targeted bisulfite sequencing data (ORQ4_vs_Q1 = 1.77, 95% CI 1.07-2.93, P = 0.027) and in the meta-analysis (ORQ4_vs_Q1 = 1.43, 95% CI 1.05-2.00, P = 0.024); however, the correlation between the MI and ELEE was not validated across study cohorts.

CONCLUSION

We have identified a blood DNA methylation signature associated with breast cancer risk in this study. Further investigation is required to confirm the interaction between estrogen exposure and DNA methylation in the blood.

Association between ambient air pollution and breast cancer risk: The multiethnic cohort study

Previous studies using different exposure methods to assess air pollution and breast cancer risk among primarily whites have been inconclusive. Air pollutant exposures of particulate matter and oxides of nitrogen were estimated by kriging (NO_x , NO₂ , PM₁₀ , PM_2.5 ), land use regression (LUR, NO_x , NO₂ ) and California Line Source Dispersion model (CALINE4, NO_x , PM_2.5 ) for 57,589 females from the Multiethnic Cohort, residing largely in Los Angeles County from recruitment (1993-1996) through 2010. Cox proportional hazards models were used to examine the associations between time-varying air pollution and breast cancer incidence adjusting for confounding factors. Stratified analyses were conducted by race/ethnicity and distance to major roads. Among all women, breast cancer risk was positively but not significantly associated with NO_x (per 50 parts per billion [ppb]) and NO₂ (per 20 ppb) determined by kriging and LUR and with PM_2.5 and PM₁₀ (per 10 μg/m³ ) determined by kriging. However, among women who lived within 500 m of major roads, significantly increased risks were observed with NO_x (hazard ratio [HR] = 1.35, 95% confidence interval [95% CI]: 1.02-1.79), NO₂ (HR = 1.44, 95% CI: 1.04-1.99), PM₁₀ (HR = 1.29, 95% CI: 1.07-1.55) and PM_2.5 (HR = 1.85, 95% CI: 1.15-2.99) determined by kriging and NO_x (HR = 1.21, 95% CI:1.01-1.45) and NO₂ (HR = 1.26, 95% CI: 1.00-1.59) determined by LUR. No overall associations were observed with exposures assessed by CALINE4. Subgroup analyses suggested stronger associations of NO_x and NO₂ among African Americans and Japanese Americans. Further studies of multiethnic populations to confirm the effects of air pollution, particularly near-roadway exposures, on the risk of breast cancer is warranted.

DNA Methylation Contributes to the Differential Expression Levels of Mecp2 in Male Mice Neurons and Astrocytes

Methyl CpG binding protein-2 (MeCP2) isoforms (E1 and E2) are important epigenetic regulators in brain cells. Accordingly, MeCP2 loss- or gain-of-function mutation causes neurodevelopmental disorders, including Rett syndrome (RTT), MECP2 duplication syndrome (MDS), and autism spectrum disorders (ASD). Within different types of brain cells, highest MeCP2 levels are detected in neurons and the lowest in astrocytes. However, our current knowledge of Mecp2/MeCP2 regulatory mechanisms remains largely elusive. It appears that there is a sex-dependent effect in X-linked MeCP2-associated disorders, as RTT primarily affects females, whereas MDS is found almost exclusively in males. This suggests that Mecp2 expression levels in brain cells might be sex-dependent. Here, we investigated the sex- and cell type-specific expression of Mecp2 isoforms in male and female primary neurons and astrocytes isolated from the murine forebrain. Previously, we reported that DNA methylation of six Mecp2 regulatory elements correlated with Mecp2 levels in the brain. We now show that in male brain cells, DNA methylation is significantly correlated with the transcript expression of these two isoforms. We show that both Mecp2 isoforms are highly expressed in male neurons compared to male astrocytes, with Mecp2e1 expressed at higher levels than Mecp2e2. Our data indicate that higher DNA methylation at the Mecp2 regulatory element(s) is associated with lower levels of Mecp2 isoforms in male astrocytes compared to male neurons.