Genome-Wide DNA Methylation Profiling in 40 Breast Cancer Cell Lines

With state-of-the-art microarray technologies now available for whole genome CpG island (CGI) methylation profiling, there is a need to develop statistical models that are specifically geared toward the analysis of such data. In this article, we propose a Gamma-Normal-Gamma (GNG) mixture model for describing three groups of CGI loci: hypomethylated, undifferentiated, and hypermethylated, from a single methylation microarray. This model was applied to study the methylation signatures of three breast cancer cell lines: MCF7, T47D, and MDAMB361. Biologically interesting and interpretable results are obtained, which highlights the heterogeneity nature of the three cell lines. This underlies the premise for the need of analyzing each of the microarray slides individually as opposed to pooling them together for a single analysis. Our comparisons with the fitted densities from the Normal-Uniform (NU) mixture model in the literature proposed for gene expression analysis show an improved goodness of fit of the GNG model over the NU model. Although the GNG model was proposed in the context of single-slide methylation analysis, it can be readily adapted to analyze multi-slide methylation data as well as other types of microarray data.

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Hemimethylation Patterns in Breast Cancer Cell Lines

Cancer Informatics ◽

10.1177/1176935119872959 ◽

2019 ◽

Vol 18 ◽

pp. 117693511987295 ◽

Cited By ~ 1

Author(s):

Shuying Sun ◽

Yu Ri Lee ◽

Brittany Enfield

Keyword(s):

Breast Cancer ◽

Dna Methylation ◽

Tumor Growth ◽

Cell Lines ◽

Cancer Cell ◽

Breast Cancer Cell ◽

Cancer Cell Lines ◽

Breast Cancer Cell Lines ◽

Entire Genome ◽

Cpg Sites

DNA methylation is an epigenetic event that involves adding a methyl group to the cytosine (C) site, especially the one that pairs with a guanine (G) site (ie, CG or CpG site), in a human genome. This event plays an important role in both cancerous and normal cell development. Previous studies often assume symmetric methylation on both DNA strands. However, asymmetric methylation, or hemimethylation (methylation that occurs only on 1 DNA strand), does exist and has been reported in several studies. Due to the limitation of previous DNA methylation sequencing technologies, researchers could only study hemimethylation on specific genes, but the overall genomic hemimethylation landscape remains relatively unexplored. With the development of advanced next-generation sequencing techniques, it is now possible to measure methylation levels on both forward and reverse strands at all CpG sites in an entire genome. Analyzing hemimethylation patterns may potentially reveal regions related to undergoing tumor growth. For our research, we first identify hemimethylated CpG sites in breast cancer cell lines using Wilcoxon signed rank tests. We then identify hemimethylation patterns by grouping consecutive hemimethylated CpG sites based on their methylation states, methylation “M” or unmethylation “U.” These patterns include regular (or consecutive) hemimethylation clusters (eg, “MMM” on one strand and “UUU” on another strand) and polarity (or reverse) clusters (eg, “MU” on one strand and “UM” on another strand). Our results reveal that most hemimethylation clusters are the polarity type, and hemimethylation does occur across the entire genome with notably higher numbers in the breast cancer cell lines. The lengths or sizes of most hemimethylation clusters are very short, often less than 50 base pairs. After mapping hemimethylation clusters and sites to corresponding genes, we study the functions of these genes and find that several of the highly hemimethylated genes may influence tumor growth or suppression. These genes may also indicate a progressing transition to a new tumor stage.

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Translational disequilibrium as an interference marker to study miRNA and methylation silencing of APRIN, a stem cell regulator in breast cancer microchimerism

Journal of Clinical Oncology ◽

10.1200/jco.2009.27.15_suppl.11109 ◽

2009 ◽

Vol 27 (15_suppl) ◽

pp. 11109-11109

Author(s):

P. Geck ◽

V. Denes ◽

M. Pilichowska ◽

A. Makarovskiy ◽

G. A. Carpinito

Keyword(s):

Breast Cancer ◽

Dna Methylation ◽

Stem Cell ◽

Cell Lines ◽

Cancer Cell ◽

Breast Cancer Cell ◽

Cancer Cell Lines ◽

Breast Cancer Cell Lines ◽

Fetal Microchimerism ◽

Clinical Breast

11109 Background: Gene silencing is universally observed in cancer and involves promoter DNA methylation. We found that a cohesin-related stem cell regulator, APRIN (Pds5B) was silenced in breast cancer clinical samples. Surprisingly, in 40% of these samples DNA methylation was not involved. Furthermore, in some breast cancer cell lines the APRIN protein was silenced without transcript downregulation or promoter methylation. This “translational disequilibrium” has been frequently reported with other proteins, but without mechanistic explanations. Recent results with RNA interference indicate that gene repression through microRNAs (typically mismatched) is mostly translational without transcript degradation. We propose, therefore, that the puzzling translational disequilibrium phenomenon is a new form of epigenetic silencing by miRNA mechanisms. We aim (i) to verify miRNA epigenetics of APRIN silencing in breast cancer cell lines; (ii) to study clinical breast cancer samples for methylation vs. miRNAs mechanisms in APRIN translational disequilibrium; and (iii) to investigate if miRNA silencing of APRIN affects a fetal embryonic stem cell pool in breast cancer (microchimerism). Methods: (i) We used miRNA mimics and miRNA inhibitors in breast cancer cell lines to verify specific miRNA involvement in APRIN silencing. (ii) We used immunohistochemistry with bisulfite converted DNA for methylation and microdissected RNA for microRNA interference studies from 56 clinical breast cancer samples. (iii) We used Y-chromosome markers on microdissected DNA for fetal microchimerism studies. Results: (i) We found that in breast cancer cell lines with APRIN translational disequilibrium a set of microRNAs correlate with APRIN silencing. (ii) We found miRNA related mechanisms in about 35 percent of breast cancer samples where APRIN was silenced and (iii) APRIN may specifically affect stem cells of fetal origin in the mother's mammary gland and contribute to cancer. Conclusions: The novel miRNA-based mechanism maybe a new epigenetic factor of gene silencing in cancer. We experimentally confirmed a set of APRIN specific miRNAs and established preliminary correlations with fetal microchimerism in breast cancer. No significant financial relationships to disclose.

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DNA methylation-dependent silencing of CST6 in human breast cancer cell lines

Laboratory Investigation ◽

10.1038/labinvest.3700485 ◽

2006 ◽

Vol 86 (12) ◽

pp. 1233-1242 ◽

Cited By ~ 32

Author(s):

Ashley G Rivenbark ◽

Wendell D Jones ◽

William B Coleman

Keyword(s):

Breast Cancer ◽

Dna Methylation ◽

Cell Lines ◽

Cancer Cell ◽

Breast Cancer Cell ◽

Human Breast Cancer ◽

Cancer Cell Lines ◽

Human Breast Cancer Cell ◽

Breast Cancer Cell Lines ◽

Human Breast

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DNA methylation profiling of breast cancer cell lines along the epithelial mesenchymal spectrum - implications for the choice of circulating tumour DNA methylation markers

10.1101/182600 ◽

2017 ◽

Author(s):

Anh Viet-Phuong Le ◽

Marcin Szaumkessel ◽

Tuan Zea Tan ◽

Jean-Paul Thiery ◽

Erik W Thompson ◽

...

Keyword(s):

Breast Cancer ◽

Dna Methylation ◽

Cell Lines ◽

Cancer Cell ◽

Breast Cancer Cell ◽

Residual Disease ◽

Methylation Status ◽

Mesenchymal Cell ◽

Cancer Cell Lines ◽

Breast Cancer Cell Lines

AbstractEpithelial-mesenchymal plasticity (EMP) is a dynamic process whereby epithelial carcinoma cells reversibly acquire morphological and invasive characteristics typical of mesenchymal cells, which facilitates metastasis. Understanding the methylation differences between epithelial and mesenchymal states may assist in the identification of optimal DNA methylation biomarkers for the blood-based monitoring of cancer. Methylation-sensitive high-resolution melting (MS-HRM) was used to examine the promoter methylation status of a panel of established and novel markers in a range of breast cancer cell lines spanning the epithelial-mesenchymal spectrum. Pyrosequencing was used to validate the MS-HRM results. The results indicate an overall distinction in methylation between epithelial and mesenchymal phenotypes. The mesenchymal expression markers VIM, DKK3 and CRABP1 were methylated in the majority of epithelial breast cancer cell lines while methylation of the epithelial expression markers GRHL2, MIR200C and CDH1 was restricted to mesenchymal cell lines. We also examined EMP association of several methylation markers that have been used to assess minimal residual disease. Markers such as AKR1B1 and APC methylation proved to be selective for epithelial breast cell lines, however RASSF1A, RARß, TWIST1 and SFRP2 methylation was seen in both epithelial and mesenchymal cell lines, supporting their suitability for a multi-marker panel.

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DNA Methylation Profiling of Breast Cancer Cell Lines along the Epithelial Mesenchymal Spectrum—Implications for the Choice of Circulating Tumour DNA Methylation Markers

International Journal of Molecular Sciences ◽

10.3390/ijms19092553 ◽

2018 ◽

Vol 19 (9) ◽

pp. 2553 ◽

Cited By ~ 7

Author(s):

Anh Le ◽

Marcin Szaumkessel ◽

Tuan Tan ◽

Jean-Paul Thiery ◽

Erik Thompson ◽

...

Keyword(s):

Breast Cancer ◽

Dna Methylation ◽

Cell Lines ◽

Cancer Cell ◽

Breast Cancer Cell ◽

Methylation Status ◽

Mesenchymal Cell ◽

Cancer Cell Lines ◽

Breast Cancer Cell Lines ◽

Circulating Tumour Dna

(1) Background: Epithelial–mesenchymal plasticity (EMP) is a dynamic process whereby epithelial carcinoma cells reversibly acquire morphological and invasive characteristics typical of mesenchymal cells. Identifying the methylation differences between epithelial and mesenchymal states may assist in the identification of optimal DNA methylation biomarkers for the blood-based monitoring of cancer. (2) Methods: Methylation-sensitive high-resolution melting (MS-HRM) was used to examine the promoter methylation status of a panel of established and novel markers in a range of breast cancer cell lines spanning the epithelial–mesenchymal spectrum. Pyrosequencing was used to validate the MS-HRM results. (3) Results: VIM, DKK3, and CRABP1 were methylated in the majority of epithelial breast cancer cell lines, while methylation of GRHL2, MIR200C, and CDH1 was restricted to mesenchymal cell lines. Some markers that have been used to assess minimal residual disease such as AKR1B1 and APC methylation proved to be specific for epithelial breast cell lines. However, RASSF1A, RARβ, TWIST1, and SFRP2 methylation was seen in both epithelial and mesenchymal cell lines, supporting their suitability for a multimarker panel. (4) Conclusions: Profiling DNA methylation shows a distinction between epithelial and mesenchymal phenotypes. Understanding how DNA methylation varies between epithelial and mesenchymal phenotypes may lead to more rational selection of methylation-based biomarkers for circulating tumour DNA analysis.

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DNA Methylation Heterogeneity Patterns in Breast Cancer Cell Lines

Cancer Informatics ◽

10.4137/cin.s40300 ◽

2016 ◽

Vol 15s4 ◽

pp. CIN.S40300

Author(s):

Sunny Tian ◽

Karina Bertelsmann ◽

Linda Yu ◽

Shuying Sun

Keyword(s):

Breast Cancer ◽

Dna Methylation ◽

Cell Lines ◽

Cancer Cell ◽

Breast Cancer Cell ◽

Cancer Cell Lines ◽

Breast Cancer Cell Lines ◽

Hub Genes ◽

Methylation State ◽

Methylation Patterns

Heterogeneous DNA methylation patterns are linked to tumor growth. In order to study DNA methylation heterogeneity patterns for breast cancer cell lines, we comparatively study four metrics: variance, I2 statistic, entropy, and methylation state. Using the categorical metric methylation state, we select the two most heterogeneous states to identify genes that directly affect tumor suppressor genes and high- or moderate-risk breast cancer genes. Utilizing the Gene Set Enrichment Analysis software and the ConsensusPath Database visualization tool, we generate integrated gene networks to study biological relations of heterogeneous genes. This analysis has allowed us to contribute 19 potential breast cancer biomarker genes to cancer databases by locating “hub genes” – heterogeneous genes of significant biological interactions, selected from numerous cancer modules. We have discovered a considerable relationship between these hub genes and heterogeneously methylated oncogenes. Our results have many implications for further heterogeneity analyses of methylation patterns and early detection of breast cancer susceptibility.

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