Project description:Epigenetic modifications, particularly DNA methylation have been increasingly implicated in cancer. Although some genes display aberrant methylation in pancreatic cancer, a comprehensive global analysis is yet to be performed. To define the genome-wide pattern of DNA methylation in pancreatic ductal adenocarcinomas (PDAC), the methylation profile of 156 PDAC and 23 non-malignant pancreas was captured using high-density arrays. More than 90,000 CpG sites were significantly differentially methylated (DM) in PDAC relative to non-malignant pancreas, with pronounced alterations in a sub-set of 13,517 CpG sites. This sub-set of differentially methylated CpG sites segregated PDAC from non-malignant pancreas, regardless of tumour cellularity. As expected, PDAC hyper-methylation was most prevalent in the 5’ region of genes (including the proximal promoter, 5’UTR and CpG islands). From 3981 genes aberrantly methylated, approximately 36% showed significant correlation between methylation and mRNA expression levels. Pathway analysis revealed an enrichment of aberrant methylation in genes involved in key molecular mechanisms important to PDAC: TGF-β, WNT, Integrin signaling, Cell adhesion, Stellate cell activation and Axon guidance. Bisulfite amplicon deep sequencing and qRT-PCR expression analyses of axon guidance pathway genes SLIT2, SLIT3, ROBO1, ROBO3, SRGAP1, and MET suggested epigenetic suppression of SLIT-ROBO signaling and up-regulation of MET expression. Hypo-methylation of MET and ITGA2 correlated with high gene expression, which correlated with poor survival of PDAC patients. These data suggest that aberrant methylation plays an important role in pancreatic carcinogenesis affecting known core signaling pathways with important implications for disease pathophysiology and therapy. This dataset includes gene expression data from 103 primary tumour samples. 86 samples from this dataset have already been deposited into GEO (GSE36924), and has been duplicated here since the data has been processed differently. This data is also available through the International Cancer Genome Consortium (ICGC) Data Portal (http://dcc/icgc.org), under the project code: Pancreatic Cancer (QCMG, AU). Access to the restricted clinical data must be made through the ICGC Data Access Compliance Office (http://www.icgc.org/daco). This dataset contains gene expression array data from 103 primary pancreatic ductal adenocarcinoma samples. All samples have 1 biological replicate. These data have corresponding methylation 450K array data (GSE49149).

Project description:Abstract The proper identification of differentially methylated CpGs is central in most epigenetic studies. The Illumina Human Methylation 450k BeadChip is widely used to quantify DNA methylation, nevertheless the design of an appropriate analysis pipeline faces severe challenges due to the convolution of biological and technical variability and the presence of a signal bias between Infinium I and II probe design types. Despite recent attempts to investigate how to analyze DNA methylation data with such an array design, it has not been possible to perform a comprehensive comparison between different bioinformatics pipelines due to the lack of appropriate datasets having both large sample size and sufficient number of technical replicates. Here we perform such a comparative analysis, targeting the problems of reducing the technical variability, eliminating the probe design bias and reducing the batch effect by exploiting two unpublished datasets, which included technical replicates and were profiled for DNA methylation either on peripheral blood, monocytes or muscle biopsies. We evaluated the performance of different analysis pipelines and demonstrated that a) it is critical to correct for the probe design type, since the amplitude of the measured methylation change depends on the underlying chemistry; b) the effect of different normalization schemes is mixed, and the most effective method in our hands were quantile normalization and Beta Mixture Quantile dilation (BMIQ); c) it is beneficial to correct for batch effects. In conclusion, our comparative analysis using a comprehensive dataset suggests an efficient pipeline for proper identification of differentially methylated CpGs using the Illumina 450k arrays. DNA samples from peripheral blood or CD14+ monocytes were included in the study. DNA methylation levels were profiled using Illumina 450K arrays. Specifically, 50 biological sample replicates from PB and 36 biological sample replicates from monocytes were randomly assigned to 8 BeadChips with technical replicates and processed in one run (a total of 96 DNA samples). Eight samples were technically replicated in pairs, while one sample was represented in a trio of replicates. Different analysis pipelines were compared, however, the file uploaded refers to the best scored. In our publication we used this one to make all analyses and conclusions.

Dataset Information

ICGC CLL 450k DNA methylation

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