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Agglomerative epigenetic aberrations are a common event in human breast cancer.

ABSTRACT: Changes in DNA methylation patterns are a common characteristic of cancer cells. Recent studies suggest that DNA methylation affects not only discrete genes, but it can also affect large chromosomal regions, potentially leading to LRES. It is unclear whether such long-range epigenetic events are relatively rare or frequent occurrences in cancer. Here, we use a high-resolution promoter tiling array approach to analyze DNA methylation in breast cancer specimens and normal breast tissue to address this question. We identified 3,506 cancer-specific differentially methylated regions (DMR) in human breast cancer with 2,033 being hypermethylation events and 1,473 hypomethylation events. Most of these DMRs are recurrent in breast cancer; 90% of the identified DMRs occurred in at least 33% of the samples. Interestingly, we found a nonrandom spatial distribution of aberrantly methylated regions across the genome that showed a tendency to concentrate in relatively small genomic regions. Such agglomerates of hypermethylated and hypomethylated DMRs spanned up to several hundred kilobases and were frequently found at gene family clusters. The hypermethylation events usually occurred in the proximity of the transcription start site in CpG island promoters, whereas hypomethylation events were frequently found in regions of segmental duplication. One example of a newly discovered agglomerate of hypermethylated DMRs associated with gene silencing in breast cancer that we examined in greater detail involved the protocadherin gene family clusters on chromosome 5 (PCDHA, PCDHB, and PCDHG). Taken together, our results suggest that agglomerative epigenetic aberrations are frequent events in human breast cancer.

SUBMITTER: Novak P

PROVIDER: S-EPMC2680223 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

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Agglomerative epigenetic aberrations are a common event in human breast cancer.

Novak Petr P Jensen Taylor T Oshiro Marc M MM Watts George S GS Kim Christina J CJ Futscher Bernard W BW

Cancer research 20081001 20

Changes in DNA methylation patterns are a common characteristic of cancer cells. Recent studies suggest that DNA methylation affects not only discrete genes, but it can also affect large chromosomal regions, potentially leading to LRES. It is unclear whether such long-range epigenetic events are relatively rare or frequent occurrences in cancer. Here, we use a high-resolution promoter tiling array approach to analyze DNA methylation in breast cancer specimens and normal breast tissue to address ...[more]

PMID: 18922938

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Project description:IntroductionAberrant methylation of CpG islands is a hallmark of cancer and occurs at an early stage in breast tumorigenesis. However, its impact on tumor development is not fully determined, and its potential as a diagnostic biomarker remains to be validated. Methylation profiling of invasive breast carcinoma has been largely explored. Conversely, very little and sparse information is available on early-stage breast cancer. To gain insight into the epigenetic switches that may promote and/or contribute to the initial neoplastic events during breast carcinogenesis, we have analyzed the DNA methylation profile of ductal carcinoma in situ, a premalignant breast lesion with a great potential to progress toward invasive carcinoma.MethodsWe have utilized a comprehensive and sensitive array-based DNA mapping technique, the methylated-CpG island recovery assay, to profile the DNA methylation pattern in ductal carcinoma in situ. Differential methylation of CpG islands was compared genome-wide in tumor DNA versus normal DNA utilizing a statistical linear model in the LIMMA software package.ResultsUsing this approach, we have identified 108 significant CpG islands that undergo aberrant DNA methylation in ductal carcinoma in situ and stage I breast tumors, with methylation frequencies greater than or comparable with those of more advanced invasive carcinoma (50% to 93%). A substantial fraction of these hypermethylated CpG islands (32% of the annotated CpG islands) is associated with several homeobox genes, such as the TLX1, HOXB13, and HNF1B genes. Fifty-three percent of the genes hypermethylated in early-stage breast cancer overlap with known Polycomb targets and include homeobox genes and other developmental transcription factors.ConclusionsWe have identified a series of new potential methylation biomarkers that may help elucidate the underlying mechanisms of breast tumorigenesis. More specifically, our results are suggestive of a critical role of homeobox gene methylation in the insurgence and/or progression of breast cancer.

Project description:The identification and characterization of tumor suppressor genes has enhanced our understanding of the biology of cancer and enabled the development of new diagnostic and therapeutic modalities. Whereas in past decades, a handful of tumor suppressors have been slowly identified using techniques such as linkage analysis, large-scale sequencing of the cancer genome has enabled the rapid identification of a large number of genes that are mutated in cancer. However, determining which of these many genes play key roles in cancer development has proven challenging. Specifically, recent sequencing of human breast and colon cancers has revealed a large number of somatic gene mutations, but virtually all are heterozygous, occur at low frequency, and are tumor-type specific. Keywords: Microarray, Hypermethylome, DNA-hypermethylation, DAC, TSA, Epigenetic, Mutation, Colorectal Cancer, Breast Cancer The candidate hypermethylome genes were identified as described below. These genes were compared to the CAN genes identified by Sjöblom T et al (Science, 2006). Cell culture and treatment. For drug treatments, log phase MCF7, T-47D, MDA-MB231 and MDA-MB-468 cells were cultured in McCoys 5A media (Invitrogen) containing 10% BCS and 1x penicillin/streptomycin with 5M 5aza-deoxycytidine (DAC) (Sigma; stock solution: 1mM in PBS) for 96 hours, replacing media and DAC every 24 hours. Cell treatment with 300nM Trichostatin A (Sigma; stock solution: 1.5mM dissolved in ethanol) was performed for 18 hours. Control cells underwent mock treatment in parallel with addition of equal volume of PBS or ethanol without drugs. Microarray analysis. Total RNA was harvested from log phase cells using the Qiagen kit according to the manufacturers instructions, including a DNAase step. RNA was quantified using the NanoDrop ND-100 followed by quality assessment with 2100 Bioanalyzer (Agilent Technologies). RNA concentrations for individual samples were greater than 200ng/ul, with 28s/18s ratios greater than 2.2 and RNA integrity numbers of 10 (highest). Sample amplification and labeling procedures were carried out using the Low RNA Input Fluorescent Linear Amplification Kit (Agilent Technologies) according to the manufacturers instructions. The labeled cRNA was purified using the RNeasy mini kit (Qiagen) and quantified. RNA spike-in controls (Agilent Technologies) were added to RNA samples before amplification. 0.75 microgram of samples labeled with Cy3 or Cy5 were mixed with control targets (Agilent Technologies), assembled on Oligo Microarray, hybridized, and processed according to the Agilent microarray protocol. Scanning was performed with the Agilent G2565BA microarray scanner under default settings recommended by Agilent Technologies. Data analysis. All arrays were subject to quality checks recommended by the manufacturer. Images were visually inspected for artifacts and distributions of signal and background intensity of both red and green channels were examined to identify anomalous arrays. No irregularities were observed, and all arrays were retained and used. All calculations were performed using the R statistical computing platform and packages from Bioconductor bioinformatics software project. The log ratio of red signal to green signal was calculated and Loess normalization as implemented in the limma package from Bioconductor.

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Project description:Age at breast cancer diagnosis is a known prognostic factor. Previously, several groups including ours have shown that young age at diagnosis is associated with higher prevalence of basal-like tumors and aggressive tumor phenotypes. Yet the impact of age at diagnosis on the genomic landscape of breast cancer remains unclear. In this study, we examined the pattern of somatic mutations, chromosomal copy number variations (CNVs) and transcriptomic profiles in young and elderly breast cancer patients.Analyses were performed on The Cancer Genome Atlas (TCGA) dataset. Patients with metastatic disease at diagnosis, classified as normal-like by PAM50 or had missing clinical information were excluded. Young patients were defined as ?45 years of age, while elderly patients were those ?70 years of age at breast cancer diagnosis. The remaining patients were classified as "intermediate". We evaluated the association between age at diagnosis and somatic mutations, CNV and gene expression in a logistic regression model adjusting for tumor size, nodal status, histology and breast cancer subtype. All analyses were corrected for multiple testing using the Benjamini-Hochberg approach.In this study, 125, 486 and 169 patients were ?45, 46-69 and ?70 years of age, respectively. Older patients had more somatic mutations (n?=?44 versus 35 versus 31; P?=?0.0009) and more CNVs, especially in ductal tumors (P?=?0.02). Eleven mutations were independently associated with age at diagnosis, of which only GATA3 was associated with young age (15.2% versus 8.2% versus 9%; P?=?0.003). Only two CNV events were independently associated with age, with more chr18p losses in older patients and more chr6q27 deletions in younger ones. Younger age at diagnosis was associated with higher expression of gene signatures related to proliferation, stem cell features and endocrine resistance.Age adds a layer of biological complexity beyond breast cancer molecular subtypes, classic pathological and clinical variables, worthy of further consideration in future drug development as we seek to refine therapeutic strategies in the era of personalized medicine.

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Agglomerative epigenetic aberrations are a common event in human breast cancer.

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Agglomerative epigenetic aberrations are a common event in human breast cancer.

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