Dataset Information

Differential methylation relative to breast cancer subtype and matched normal tissue reveals distinct patterns.

ABSTRACT: Due to the heterogeneous nature of breast cancer and the widespread use of single-gene studies, there is limited knowledge of multi-gene, locus-specific DNA methylation patterns in relation to molecular subtype and clinical features. We, therefore, quantified DNA methylation of 70 candidate gene loci in 140 breast tumors and matched normal tissues and determined associations with gene expression and tumor subtype. Using Sequenom's EpiTYPER platform, approximately 1,200 CpGs were interrogated and revealed six DNA methylation patterns in breast tumors relative to matched normal tissue. Differential methylation of several gene loci was observed within all molecular subtypes, while other patterns were subtype-dependent. Methylation of numerous gene loci was inversely correlated with gene expression, and in some cases, this correlation was only observed within specific breast tumor subtypes. Our findings were validated on a larger set of tumors and matched adjacent normal tissue from The Cancer Genome Atlas dataset, which utilized methylation data derived from both Illumina Infinium 27 and 450 k arrays. These findings highlight the need to control for subtype when interpreting DNA methylation results, and the importance of interrogating multiple CpGs across varied gene regions.

SUBMITTER: Bardowell SA

PROVIDER: S-EPMC3832780 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Differential methylation relative to breast cancer subtype and matched normal tissue reveals distinct patterns.

Bardowell Sabrina A SA Parker Joel J Fan Cheng C Crandell Jamie J Perou Charles M CM Swift-Scanlan Theresa T

Breast cancer research and treatment 20131110 2

Due to the heterogeneous nature of breast cancer and the widespread use of single-gene studies, there is limited knowledge of multi-gene, locus-specific DNA methylation patterns in relation to molecular subtype and clinical features. We, therefore, quantified DNA methylation of 70 candidate gene loci in 140 breast tumors and matched normal tissues and determined associations with gene expression and tumor subtype. Using Sequenom's EpiTYPER platform, approximately 1,200 CpGs were interrogated and ...[more]

PMID: 24212716

Similar Datasets

Project description:BackgroundDNA methylation is an epigenetic event that may regulate gene expression. Because of this regulation role, aberrant DNA methylation is often associated with many diseases. Within-sample DNA co-methylation is the similarity of methylation in nearby cytosine sites of a chromosome. It is important to study co-methylation patterns. However, it is not well studied yet, and it is unclear to us what co-methylation patterns normal DNA samples have. Are the co-methylation patterns of the same tissue across several samples different? Are the co-methylation patterns of various tissues of the same sample different? To answer these questions, we conduct analyses using two sets of data: 3-sample-1-tissue (3S1T) and 1-sample-8-tissue (1S8T).ResultsTo study the co-methylation patterns of the two datasets, 3S1T and 1S8T, we investigate the following questions: How often does one methylation state change to other methylation states and how is this change associated with chromosome distance? Based on the 3S1T data, we find there is not significant co-methylation difference among the same spleen tissues of three different samples. However, the analysis results of 1S8T data show that there were significant differences among eight tissues of one sample. For both 3S1T and 1S8T data, we find that the no/low methylation state A and high/full methylation state D tend to remain the same along a chromosome region. We also find that the low/partial methylation state B and partial/high methylation state C tend to change to higher methylation states along a chromosome. Finally, we find that lengths of most co-methylation regions are very short with only a few hundred base pairs. In fact, only a small proportion of methylated regions are longer than 1000 base pairs.ConclusionsIn this paper, we have addressed a few questions regarding within-sample co-methylation patterns in normal tissues. Our statistical analysis results and answers may help researchers to better understand the biological process of DNA methylation. This may pave the way to develop better analysis methods for future methylation research.

Dataset Information

Differential methylation relative to breast cancer subtype and matched normal tissue reveals distinct patterns.

Publications

Differential methylation relative to breast cancer subtype and matched normal tissue reveals distinct patterns.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets