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Epigenetic mapping and functional analysis in a breast cancer metastasis model using whole-genome promoter tiling microarrays.

ABSTRACT: INTRODUCTION: Breast cancer metastasis is a complex, multi-step biological process. Genetic mutations along with epigenetic alterations in the form of DNA methylation patterns and histone modifications contribute to metastasis-related gene expression changes and genomic instability. So far, these epigenetic contributions to breast cancer metastasis have not been well characterized, and there is only a limited understanding of the functional mechanisms affected by such epigenetic alterations. Furthermore, no genome-wide assessments have been undertaken to identify altered DNA methylation patterns in the context of metastasis and their effects on specific functional pathways or gene networks. METHODS: We have used a human gene promoter tiling microarray platform to analyze a cell line model of metastasis to lymph nodes composed of a poorly metastatic MDA-MB-468GFP human breast adenocarcinoma cell line and its highly metastatic variant (468LN). Gene networks and pathways associated with metastasis were identified, and target genes associated with epithelial-mesenchymal transition were validated with respect to DNA methylation effects on gene expression. RESULTS: We integrated data from the tiling microarrays with targets identified by Ingenuity Pathways Analysis software and observed epigenetic variations in genes implicated in epithelial-mesenchymal transition and with tumor cell migration. We identified widespread genomic hypermethylation and hypomethylation events in these cells and we confirmed functional associations between methylation status and expression of the CDH1, CST6, EGFR, SNAI2 and ZEB2 genes by quantitative real-time PCR. Our data also suggest that the complex genomic reorganization present in cancer cells may be superimposed over promoter-specific methylation events that are responsible for gene-specific expression changes. CONCLUSION: This is the first whole-genome approach to identify genome-wide and gene-specific epigenetic alterations, and the functional consequences of these changes, in the context of breast cancer metastasis to lymph nodes. This approach allows the development of epigenetic signatures of metastasis to be used concurrently with genomic signatures to improve mapping of the evolving molecular landscape of metastasis and to permit translational approaches to target epigenetically regulated molecular pathways related to metastatic progression.

SUBMITTER: Rodenhiser DI

PROVIDER: S-EPMC2575535 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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Epigenetic mapping and functional analysis in a breast cancer metastasis model using whole-genome promoter tiling microarrays.

Rodenhiser David I DI Andrews Joseph J Kennette Wendy W Sadikovic Bekim B Mendlowitz Ariel A Tuck Alan B AB Chambers Ann F AF

Breast cancer research : BCR 20080718 4

<h4>Introduction</h4>Breast cancer metastasis is a complex, multi-step biological process. Genetic mutations along with epigenetic alterations in the form of DNA methylation patterns and histone modifications contribute to metastasis-related gene expression changes and genomic instability. So far, these epigenetic contributions to breast cancer metastasis have not been well characterized, and there is only a limited understanding of the functional mechanisms affected by such epigenetic alteratio ...[more]

PMID: 18638373

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Project description:Whole genome tiling arrays provide a high resolution platform for profiling of genetic, epigenetic, and gene expression polymorphisms. In this study we surveyed natural genomic variation in cytosine methylation among Arabidopsis thaliana wild accessions Columbia (Col) and Vancouver (Van) by comparing hybridization intensity difference between genomic DNA digested with either methylation-sensitive (HpaII) or -insensitive (MspI) restriction enzyme. Single Feature Polymorphisms (SFPs) were assayed on a full set of 1,683,620 unique features of Arabidopsis Tiling Array 1.0F (Affymetrix), while constitutive and polymorphic CG methylation were assayed on a subset of 54,519 features, which contain a 5'CCGG3' restriction site. 138,552 SFPs (1% FDR) were identified across enzyme treatments, which preferentially accumulated in pericentromeric regions. Our study also demonstrates that at least 8% of all analyzed CCGG sites were constitutively methylated across the two strains, while about 10% of all analyzed CCGG sites were differentially methylated between the two strains. Within euchromatin arms, both constitutive and polymorphic CG methylation accumulated in central regions of genes but under-represented toward the 5' and 3' ends of the coding sequences. Nevertheless, polymorphic methylation occurred much more frequently in gene ends than constitutive methylation. Inheritance of methylation polymorphisms in reciprocal F1 hybrids was predominantly additive, with F1 plants generally showing levels of methylation intermediate between the parents. By comparing gene expression profiles, using matched tissue samples, we found that magnitude of methylation polymorphism immediately upstream or downstream of the gene was inversely correlated with the degree of expression variation for that gene. In contrast, methylation polymorphism within genic region showed weak positive correlation with expression variation. Our results demonstrated extensive genetic and epigenetic polymorphisms between Arabidopsis accessions and suggested a possible relationship between natural CG methylation variation and gene expression variation.

Project description:BackgroundMicroRNAs (miRNAs) and trans-acting small-interfering RNAs (tasi-RNAs) are small (20-22 nt long) RNAs (smRNAs) generated from hairpin secondary structures or antisense transcripts, respectively, that regulate gene expression by Watson-Crick pairing to a target mRNA and altering expression by mechanisms related to RNA interference. The high sequence homology of plant miRNAs to their targets has been the mainstay of miRNA prediction algorithms, which are limited in their predictive power for other kingdoms because miRNA complementarity is less conserved yet transitive processes (production of antisense smRNAs) are active in eukaryotes. We hypothesize that antisense transcription and associated smRNAs are biomarkers which can be computationally modeled for gene discovery.Principal findingsWe explored rice (Oryza sativa) sense and antisense gene expression in publicly available whole genome tiling array transcriptome data and sequenced smRNA libraries (as well as C. elegans) and found evidence of transitivity of MIRNA genes similar to that found in Arabidopsis. Statistical analysis of antisense transcript abundances, presence of antisense ESTs, and association with smRNAs suggests several hundred Arabidopsis 'orphan' hypothetical genes are non-coding RNAs. Consistent with this hypothesis, we found novel Arabidopsis homologues of some MIRNA genes on the antisense strand of previously annotated protein-coding genes. A Support Vector Machine (SVM) was applied using thermodynamic energy of binding plus novel expression features of sense/antisense transcription topology and siRNA abundances to build a prediction model of miRNA targets. The SVM when trained on targets could predict the "ancient" (deeply conserved) class of validated Arabidopsis MIRNA genes with an accuracy of 84%, and 76% for "new" rapidly-evolving MIRNA genes.ConclusionsAntisense and smRNA expression features and computational methods may identify novel MIRNA genes and other non-coding RNAs in plants and potentially other kingdoms, which can provide insight into antisense transcription, miRNA evolution, and post-transcriptional gene regulation.

Project description:BACKGROUND:We have previously identified genome-wide DNA methylation changes in a cell line model of breast cancer metastasis. These complex epigenetic changes that we observed, along with concurrent karyotype analyses, have led us to hypothesize that complex genomic alterations in cancer cells (deletions, translocations and ploidy) are superimposed over promoter-specific methylation events that are responsible for gene-specific expression changes observed in breast cancer metastasis. METHODOLOGY/PRINCIPAL FINDINGS:We undertook simultaneous high-resolution, whole-genome analyses of MDA-MB-468GFP and MDA-MB-468GFP-LN human breast cancer cell lines (an isogenic, paired lymphatic metastasis cell line model) using Affymetrix gene expression (U133), promoter (1.0R), and SNP/CNV (SNP 6.0) microarray platforms to correlate data from gene expression, epigenetic (DNA methylation), and combination copy number variant/single nucleotide polymorphism microarrays. Using Partek Software and Ingenuity Pathway Analysis we integrated datasets from these three platforms and detected multiple hypomethylation and hypermethylation events. Many of these epigenetic alterations correlated with gene expression changes. In addition, gene dosage events correlated with the karyotypic differences observed between the cell lines and were reflected in specific promoter methylation patterns. Gene subsets were identified that correlated hyper (and hypo) methylation with the loss (or gain) of gene expression and in parallel, with gene dosage losses and gains, respectively. Individual gene targets from these subsets were also validated for their methylation, expression and copy number status, and susceptible gene pathways were identified that may indicate how selective advantage drives the processes of tumourigenesis and metastasis. CONCLUSIONS/SIGNIFICANCE:Our approach allows more precisely profiling of functionally relevant epigenetic signatures that are associated with cancer progression and metastasis.

Project description:IntroductionDNA methylation-induced silencing of genes encoding tumor suppressors is common in many types of cancer, but little is known about how such epigenetic silencing can contribute to tumor metastasis. The PRKD1 gene encodes protein kinase D1 (PKD1), a serine/threonine kinase that is expressed in cells of the normal mammary gland, where it maintains the epithelial phenotype by preventing epithelial-to-mesenchymal transition.MethodsThe status of PRKD1 promoter methylation was analyzed by reduced representation bisulfite deep sequencing, methylation-specific PCR (MSP-PCR) and in situ MSP-PCR in invasive and noninvasive breast cancer lines, as well as in humans in 34 cases of "normal" tissue, 22 cases of ductal carcinoma in situ, 22 cases of estrogen receptor positive, HER2-negative (ER+/HER2-) invasive lobular carcinoma, 43 cases of ER+/HER2- invasive ductal carcinoma (IDC), 93 cases of HER2+ IDC and 96 cases of triple-negative IDC. A reexpression strategy using the DNA methyltransferase inhibitor decitabine was used in vitro in MDA-MB-231 cells as well as in vivo in a tumor xenograft model and measured by RT-PCR, immunoblotting and immunohistochemistry. The effect of PKD1 reexpression on cell invasion was analyzed in vitro by transwell invasion assay. Tumor growth and metastasis were monitored in vivo using the IVIS Spectrum Pre-clinical In Vivo Imaging System.ResultsHerein we show that the gene promoter of PRKD1 is aberrantly methylated and silenced in its expression in invasive breast cancer cells and during breast tumor progression, increasing with the aggressiveness of tumors. Using an animal model, we show that reversion of PRKD1 promoter methylation with the DNA methyltransferase inhibitor decitabine restores PKD1 expression and blocks tumor spread and metastasis to the lung in a PKD1-dependent fashion.ConclusionsOur data suggest that the status of epigenetic regulation of the PRKD1 promoter can provide valid information on the invasiveness of breast tumors and therefore could serve as an early diagnostic marker. Moreover, targeted upregulation of PKD1 expression may be used as a therapeutic approach to reverse the invasive phenotype of breast cancer cells.

Dataset Information

Epigenetic mapping and functional analysis in a breast cancer metastasis model using whole-genome promoter tiling microarrays.

Publications

Epigenetic mapping and functional analysis in a breast cancer metastasis model using whole-genome promoter tiling microarrays.

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