Methylation detection oligonucleotide microarray analysis: a high-resolution method for detection of CpG island methylation

Kamalakaran, Sitharthan; Kendall, Jude; Zhao, Xiaoyue; Tang, Chunlao; Khan, Sohail Ahmed; Ravi, Kandasamy; Auletta, Theresa; Riggs, Michael; Wang, Yun; Helland, Åslaug; Naume, Bjørn; Dimitrova, Nevenka; Børresen-Dale, Anne Lise; Hicks, Jim; Lucito, Robert

doi:10.1093/nar/gkp413

Cited by 18 publications

(14 citation statements)

References 56 publications

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“…A summary of the clinical information on the samples is provided in Table 1. MOMA has been described and validated using cell lines, breast and ovarian tumor tissues previously (Kamalakaran et al., 2009). Briefly, the sample DNA is digested with MspI to make a representation, ligated with adapters and split into two pools.…”

Section: Resultsmentioning

confidence: 99%

DNA methylation patterns in luminal breast cancers differ from non‐luminal subtypes and can identify relapse risk independent of other clinical variables

et al. 2010

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Subtypes DNA methylation MOMA Survival Epigenetics A B S T R A C TThe diversity of breast cancers reflects variations in underlying biology and affects the clinical implications for patients. Gene expression studies have identified five major subtypese Luminal A, Luminal B, basal-like, ErbB2þ and Normal-Like. We set out to determine the role of DNA methylation in subtypes by performing genome-wide scans of CpG methylation in breast cancer samples with known expression-based subtypes. Unsupervised hierarchical clustering using a set of most varying loci clustered the tumors into a Luminal A majority (82%) cluster, Basal-like/ErbB2þ majority (86%) cluster and a non-specific cluster with samples that were also inconclusive in their expression-based subtype correlations.Contributing methylation loci were both gene associated loci (30%) and non-gene associ-

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Section: Resultsmentioning

confidence: 99%

DNA methylation patterns in luminal breast cancers differ from non‐luminal subtypes and can identify relapse risk independent of other clinical variables

et al. 2010

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“…S3A), we reasoned that several of these events occur in other cancer types as well. To test this, we obtained published tumor and normal methylation profiles for breast, ovarian, and colorectal cancers (27,28) and computed the methylation differences (tumor over normal) in the 500 most demethylated gene promoters in liver cancer. As expected, although these differences are mainly tumor type specific, several genes are hypomethylated in all selected cancers (Fig.…”

Section: à6mentioning

confidence: 99%

Definition of the Landscape of Promoter DNA Hypomethylation in Liver Cancer

et al. 2011

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We use hepatic cellular carcinoma (HCC), one of the most common human cancers, as a model to delineate the landscape of promoter hypomethylation in cancer. Using a combination of methylated DNA immunoprecipitation and hybridization with comprehensive promoter arrays, we have identified approximately 3,700 promoters that are hypomethylated in tumor samples. The hypomethylated promoters appeared in clusters across the genome suggesting that a high-level organization underlies the epigenomic changes in cancer. In normal liver, most hypomethylated promoters showed an intermediate level of methylation and expression, however, high-CpG dense promoters showed the most profound increase in gene expression. The demethylated genes are mainly involved in cell growth, cell adhesion and communication, signal transduction, mobility, and invasion; functions that are essential for cancer progression and metastasis. The DNA methylation inhibitor, 5-aza-2 0 -deoxycytidine, activated several of the genes that are demethylated and induced in tumors, supporting a causal role for demethylation in activation of these genes. Previous studies suggested that MBD2 was involved in demethylation of specific human breast and prostate cancer genes. Whereas MBD2 depletion in normal liver cells had little or no effect, we found that its depletion in human HCC and adenocarcinoma cells resulted in suppression of cell growth, anchorage-independent growth and invasiveness as well as an increase in promoter methylation and silencing of several of the genes that are hypomethylated in tumors. Taken together, the findings define the potential functional role of hypomethylation in cancer. Cancer Res; 71(17); 5891-903. Ó2011 AACR.

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“…The protocol for Methylation detection representational Oligonucleotide Microarray Analysis (MOMA) was performed as previously described [16, 17]. Annotated genomic CpG island locations were obtained from the UCSC genome browser.…”

Section: Experimental Methodsmentioning

confidence: 99%

A quantitative proteomics-based signature of platinum sensitivity in ovarian cancer cell lines

Fan

Wrzeszczyński

et al. 2015

Biochemical Journal

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Although DNA encodes the molecular instructions that underlie control of cell function, it is the proteins that are primarily responsible for implementing those instructions. Therefore, quantitative analyses of the proteome would be expected to yield insights into important candidates for the detection and treatment of disease. We present an iTRAQ (Isobaric Tagging for Relative and Absolute Quantification)-based proteomic analysis of 10 ovarian cancer cell lines and 2 normal ovarian surface epithelial cell lines. We profiled the abundance of 2659 cellular proteins, of which 1273 were common to all 12 cell lines. Of the 1273, 75 proteins exhibited elevated expression, and 164 proteins had diminished expression in the cancerous cells compared to the normal cell lines. The iTRAQ expression profiles allowed us to segregate cell lines based upon sensitivity and resistance to carboplatin. Importantly, we observed no substantial correlation between protein abundance and RNA expression or epigenetic, DNA methylation data. Furthermore, we could not discriminate between sensitivity and resistance to carboplatin on the basis of RNA expression and DNA methylation data alone. This study illustrates the importance of proteomics-based discovery for defining the basis for the carboplatin response in ovarian cancer and highlights candidate proteins, particularly involved in cellular redox regulation, homologous recombination and DNA damage repair, that otherwise could not have been predicted from whole genome and expression data sources alone.

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Methylation detection oligonucleotide microarray analysis: a high-resolution method for detection of CpG island methylation

Cited by 18 publications

References 56 publications

DNA methylation patterns in luminal breast cancers differ from non‐luminal subtypes and can identify relapse risk independent of other clinical variables

DNA methylation patterns in luminal breast cancers differ from non‐luminal subtypes and can identify relapse risk independent of other clinical variables

Definition of the Landscape of Promoter DNA Hypomethylation in Liver Cancer

A quantitative proteomics-based signature of platinum sensitivity in ovarian cancer cell lines

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