Abstract 2087: Breast tissue-specific DNA methylation levels predicted by genetic variants in association with breast cancer

2021 ◽  
Author(s):  
Chunyan He ◽  
James Castle ◽  
Nan Lin ◽  
Jinpeng Liu ◽  
Chi Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Genetic Variants ◽  
Breast Tissue ◽  
Tissue Specific

scholarly journals Accelerated aging in normal breast tissue of women with breast cancer

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Shoghag Panjarian ◽  
Jozef Madzo ◽  
Kelsey Keith ◽  
Carolyn M. Slater ◽  
Carmen Sapienza ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Anomaly Detection ◽  
Breast Tissue ◽  
Preventive Intervention ◽  
Normal Breast Tissue ◽  
Accelerated Aging ◽  
Normal Breast ◽  
Age Related ◽  
Unsupervised Anomaly Detection

Abstract Background DNA methylation alterations have similar patterns in normal aging tissue and in cancer. In this study, we investigated breast tissue-specific age-related DNA methylation alterations and used those methylation sites to identify individuals with outlier phenotypes. Outlier phenotype is identified by unsupervised anomaly detection algorithms and is defined by individuals who have normal tissue age-dependent DNA methylation levels that vary dramatically from the population mean. Methods We generated whole-genome DNA methylation profiles (GSE160233) on purified epithelial cells and used publicly available Infinium HumanMethylation 450K array datasets (TCGA, GSE88883, GSE69914, GSE101961, and GSE74214) for discovery and validation. Results We found that hypermethylation in normal breast tissue is the best predictor of hypermethylation in cancer. Using unsupervised anomaly detection approaches, we found that about 10% of the individuals (39/427) were outliers for DNA methylation from 6 DNA methylation datasets. We also found that there were significantly more outlier samples in normal-adjacent to cancer (24/139, 17.3%) than in normal samples (15/228, 5.2%). Additionally, we found significant differences between the predicted ages based on DNA methylation and the chronological ages among outliers and not-outliers. Additionally, we found that accelerated outliers (older predicted age) were more frequent in normal-adjacent to cancer (14/17, 82%) compared to normal samples from individuals without cancer (3/17, 18%). Furthermore, in matched samples, we found that the epigenome of the outliers in the pre-malignant tissue was as severely altered as in cancer. Conclusions A subset of patients with breast cancer has severely altered epigenomes which are characterized by accelerated aging in their normal-appearing tissue. In the future, these DNA methylation sites should be studied further such as in cell-free DNA to determine their potential use as biomarkers for early detection of malignant transformation and preventive intervention in breast cancer.

scholarly journals Age-related DNA methylation in paired normal and tumour breast tissue in Chinese breast cancer patients

Epigenetics ◽  
2020 ◽  
pp. 1-15
Author(s):  
Maeve Kiely ◽  
Lap Ah Tse ◽  
Hela Koka ◽  
Difei Wang ◽  
Priscilla Lee ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Cancer Patients ◽  
Breast Tissue ◽  
Breast Cancer Patients ◽  
Age Related

scholarly journals DNA Methylation and Breast Cancer Risk: An Epigenome-Wide Study of Normal Breast Tissue and Blood

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3088 ◽  
Author(s):  
Kaoutar Ennour-Idrissi ◽  
Dzevka Dragic ◽  
Elissar Issa ◽  
Annick Michaud ◽  
Sue-Ling Chang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Breast Tissue ◽  
Normal Breast Tissue ◽  
Normal Breast ◽  
Breast Epithelium ◽  
Normal Breast Epithelium ◽  
Differentially Methylated Genes

Differential DNA methylation is a potential marker of breast cancer risk. Few studies have investigated DNA methylation changes in normal breast tissue and were largely confounded by cancer field effects. To detect methylation changes in normal breast epithelium that are causally associated with breast cancer occurrence, we used a nested case–control study design based on a prospective cohort of patients diagnosed with a primary invasive hormone receptor-positive breast cancer. Twenty patients diagnosed with a contralateral breast cancer (CBC) were matched (1:1) with 20 patients who did not develop a CBC on relevant risk factors. Differentially methylated Cytosine-phosphate-Guanines (CpGs) and regions in normal breast epithelium were identified using an epigenome-wide DNA methylation assay and robust linear regressions. Analyses were replicated in two independent sets of normal breast tissue and blood. We identified 7315 CpGs (FDR < 0.05), 52 passing strict Bonferroni correction (p < 1.22 × 10−7) and 43 mapping to known genes involved in metabolic diseases with significant enrichment (p < 0.01) of pathways involving fatty acids metabolic processes. Four differentially methylated genes were detected in both site-specific and regions analyses (LHX2, TFAP2B, JAKMIP1, SEPT9), and three genes overlapped all three datasets (POM121L2, KCNQ1, CLEC4C). Once validated, the seven differentially methylated genes distinguishing women who developed and who did not develop a sporadic breast cancer could be used to enhance breast cancer risk-stratification, and allow implementation of targeted screening and preventive strategies that would ultimately improve breast cancer prognosis.

scholarly journals Tissue Specific DNA Methylation in Normal Human Breast Epithelium and in Breast Cancer

PLoS ONE ◽  
2014 ◽  
Vol 9 (3) ◽  
pp. e91805 ◽  
Author(s):  
Ayelet Avraham ◽  
Sean Soonweng Cho ◽  
Ronit Uhlmann ◽  
Mia Leonov Polak ◽  
Judith Sandbank ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Human Breast ◽  
Breast Epithelium ◽  
Tissue Specific ◽  
Normal Human Breast ◽  
Normal Human

Are dietary factors involved in the association of CDH4 methylation and breast cancer risk?

2021 ◽  
pp. 1-36
Author(s):  
Nannan Zhang ◽  
Liangliang Li ◽  
Zhiping Long ◽  
Jinghang Du ◽  
Shuo Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Breast Tissue ◽  
Dietary Habits ◽  
Peripheral Blood Leukocyte ◽  
Dietary Factors ◽  
Interactive Effects ◽  
Breast Cancers ◽  
Newly Diagnosed ◽  
Specific Pcr

Abstract DNA methylation is one of the most important epigenetic modifications in breast cancer (BC) development, and long-term dietary habits have been shown to alter DNA methylation. Cadherin-4 (CDH4, a member of the cadherin family) encodes Ca2+-dependent cell-cell adhesion glycoproteins. We conducted a case-control study (380 newly-diagnosed breast cancers and 439 cancer-free controls) to explore the relationship of CDH4 methylation in peripheral blood leukocyte DNA (PBL), as well as its combined and interactive effects with dietary factors and lifestyle on BC risk. A case-only study (335 newly-diagnosed breast cancers) was conducted to analyze the association between CDH4 methylation in breast tissue DNA and dietary factors. CDH4 methylation were detected using quantitative methylation specific PCR (qMSP). Unconditional logistic regressions were used to analyze the association of CDH4 methylation in PBL DNA and BC risk. Cross-over analysis and unconditional logistic regression were used to calculate the combined and interactive effects between CDH4 methylation in PBL DNA and dietary factors in BC. CDH4 hypermethylation was significantly associated with increased BC risk in PBL DNA (ORadjusted (ORadj)= 2.70, 95% confidence interval (CI)= 1.90-3.83, P<0.001). CDH4 hypermethylation also showed significant combined effects with the consumption of <500 g/week vegetables (ORadj=4.33, 95% CI=2.63-7.10), ≤3 times/week allium vegetables (ORadj=7.00, 95% CI=4.17-11.77), <3 times/week fish (ORadj=7.92, 95% CI=3.79-16.53), <3 times/week milk (ORadj=6.30, 95% CI=3.41-11.66), >3 times/week overnight food (ORadj=4.63, 95% CI=2.69-7.99), ≥250 g/week pork (ORadj=5.59, 95% CI=2.94-10.62), and <1 time/month physical activity (ORadj=4.72, 95% CI=2.87-7.76). Moreover, consuming milk ≥ 1 times/month was significantly related with decreased risk of CDH4 methylation (OR=0.61, 95% CI=0.38-0.99) in breast tissue. Our findings may provide direct guidance on the dietary intake for specific methylated carriers to decrease their risk for developing BC.

scholarly journals DNA methylation differences at regulatory elements are associated with the cancer risk factor age in normal breast tissue

2017 ◽  
Author(s):  
Kevin C. Johnson ◽  
E. Andres Houseman ◽  
Jessica E. King ◽  
Brock C. Christensen
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Dna Methylation ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Breast Tissue ◽  
Normal Breast ◽  
Cancer Risk Factors ◽  
Age Related ◽  
Breast Cancer Risk Factors

AbstractBackgroundThe underlying biological mechanisms through which epidemiologically defined breast cancer risk factors contribute to disease risk remain poorly understood. Identification of the molecular changes associated with cancer risk factors in normal tissues may aid in determining the earliest events of carcinogenesis and informing cancer prevention strategies.ResultsHere we investigated the impact cancer risk factors have on the normal breast epigenome by analyzing DNA methylation genome-wide (Infinium 450K array) in cancer-free women from the Susan G. Komen Tissue Bank (n = 100). We tested the relation of established breast cancer risk factors: age, body mass index, parity, and family history of disease with DNA methylation adjusting for potential variation in cell-type proportions. We identified 787 CpG sites that demonstrated significant associations (Q-value < 0.01) with subject age. Notably, DNA methylation was not strongly associated with the other evaluated breast cancer risk factors. Age-related DNA methylation changes are primarily increases in methylation enriched at breast epithelial cell enhancer regions (P = 7.1E-20), and binding sites of chromatin remodelers (MYC and CTCF). We validated the age-related associations in two independent populations of normal breast tissue (n = 18) and normal-adjacent to tumor tissue (n = 97). The genomic regions classified as age-related were more likely to be regions altered in cancer in both pre-invasive (n = 40, P=3.0E-03) and invasive breast tumors (n = 731, P=1.1E-13).ConclusionsDNA methylation changes with age occur at regulatory regions, and are further exacerbated in cancer suggesting that age influences breast cancer risk in part through its contribution to epigenetic dysregulation in normal breast tissue.

Epigenetic control of breast cancer susceptibility.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 1560-1560
Author(s):  
Natascia Marino ◽  
Rana German ◽  
Nakshatri Harikrishna ◽  
Ram Podicheti ◽  
Ashley Vode ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
High Risk ◽  
Breast Cancer Risk ◽  
Breast Tissue ◽  
Cancer Susceptibility ◽  
Breast Cancer Susceptibility ◽  
Average Risk ◽  
Coding Regions

1560 Background: Epigenetic mechanisms such as DNA methylation are important regulators of gene expression and are frequently dysregulated early in breast carcinogenesis. The relationship between DNA methylation aberrations in normal breast tissue and breast cancer risk remains unclear. Methods: Disease-free breast tissue cores donated by 71 high-risk (Tyrer-Cuzick lifetime risk ≥20%) and 79 average-risk women were obtained from the Komen Tissue Bank and processed for whole methylome (Diagenode's MethylCap Library and single-end 75-bp sequencing on Illumina Nextseq) and whole transcriptome (Illumina Nextseq) profiling. Reads from RNA-seq data were aligned to the human genome reference, GRCh38.p12 using STAR v.2.5.2b and tested for differential gene expression using DESeq2 ver. 1.24.0. For DNA methylation data, difference of variation in deduplicated read coverage among 250-bp fixed sized bins spanning CpG islands between high- and average-risk libraries was computed as z-ratios to identify differentially methylated regions. Pathway analysis was performed using IPA v06_01. Results: We identified 1355 CpGs that were differentially methylated between high- and average-risk breast tissues (ΔZ > 0.5, FDR < 0.05). Hypomethylated CpGs were overrepresented in high-risk tissue and were found predominantly (68%) in non-coding regions. Hypermethylated CpG sites were found equally in the gene body and non-coding regions. Transcriptomic analysis identified 112 differentially expressed genes (fold change≥2, FDR < 0.05), involved in chemokines signaling, metabolism and estrogen biosynthesis. Among those, FAM83A (logfc = 2.3, FDR = 0.004) was previously described as epigenetically dysregulated in multiple cancers and transforms breast epithelial cell in vitro. Methylation-expression correlations revealed 11 epigenetically regulated genes including cellular transformation-associated BMPR1B. Two hypomethylated/upregulated long non-coding RNAs were also identified in high-risk breasts. Conclusions: This is the first gene expression/DNA methylation analysis of normal breasts from women at either high or average risk of breast cancer. Our discovery of epigenetically regulated genes associated with breast cancer risk provides an opportunity to mechanistically dissect breast cancer susceptibility and risk-associated molecular alterations. Unlike the current focus of identifying germline mutations or single nucleotide polymorphisms responsible for higher risk, our studies reveal an epigenetic mechanism, which is not discernable through simple genomic sequencing.

scholarly journals A deep embedded refined clustering approach for breast cancer distinction based on DNA methylation

2021 ◽  
Author(s):  
Rocío del Amor ◽  
Adrián Colomer ◽  
Carlos Monteagudo ◽  
Valery Naranjo
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Deep Learning ◽  
Dimensionality Reduction ◽  
Error Rate ◽  
Breast Tissue ◽  
Cpg Island ◽  
Learning System ◽  
Methylation Data ◽  
Tissue Samples

AbstractEpigenetic alterations have an important role in the development of several types of cancer. Epigenetic studies generate a large amount of data, which makes it essential to develop novel models capable of dealing with large-scale data. In this work, we propose a deep embedded refined clustering method for breast cancer differentiation based on DNA methylation. In concrete, the deep learning system presented here uses the levels of CpG island methylation between 0 and 1. The proposed approach is composed of two main stages. The first stage consists in the dimensionality reduction of the methylation data based on an autoencoder. The second stage is a clustering algorithm based on the soft assignment of the latent space provided by the autoencoder. The whole method is optimized through a weighted loss function composed of two terms: reconstruction and classification terms. To the best of the authors’ knowledge, no previous studies have focused on the dimensionality reduction algorithms linked to classification trained end-to-end for DNA methylation analysis. The proposed method achieves an unsupervised clustering accuracy of 0.9927 and an error rate (%) of 0.73 on 137 breast tissue samples. After a second test of the deep-learning-based method using a different methylation database, an accuracy of 0.9343 and an error rate (%) of 6.57 on 45 breast tissue samples are obtained. Based on these results, the proposed algorithm outperforms other state-of-the-art methods evaluated under the same conditions for breast cancer classification based on DNA methylation data.

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