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Genome-Wide DNA methylation analysis of tumor and adjacent normal prostate tissues

ABSTRACT: Archival formalin-fixed paraffin-embedded tissues were collected. The slides were reviewed by a pathologist to identify representative tumor tissue blocks. Enrichment of tumor cells was performed by tissue macro-dissection of predetermined areas, as outlined by Haemotoxylin and Eosin staining. Seven-micron thick sections in isolated areas of interest with at least 50% tumor cellularity were dissected to be used as tumor samples. Normal prostate samples were obtained from the adjacent tissues demonstrating no tumor involvement. Genomic DNA extraction was conducted using the Illumina FFPE DNA recovery kit. Following bisulfite conversion, DNA methylation analysis of the samples was performed using the Illumina Infinium methylation 450k bead chip array (San Diego, CA), according to the manufacturer’s protocol. Both tumor and normal samples were assayed in one experiment to avoid batch effect. The resulting methylated and unmethylated signal intensity data were imported into R 3.4.2 for analysis. Normalization was performed using Illumina normalization method with background correction using the minfi package in R 3.4.2.

ORGANISM(S): Homo sapiens

PROVIDER: GSE112047 | GEO | 2018/03/20

REPOSITORIES: GEO

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Project description:Background: As the most stable and experimentally accessible epigenetic mark, DNA methylation is of great interest to the research community. The landscape of DNA methylation across tissues, through development and in disease pathogenesis is not yet well characterised. Thus there is a need for rapid and cost effective methods for assessing genome-wide levels of DNA methylation. The Illumina Infinium HumanMethylation450 (450K) BeadChip is a very useful addition to the available methods but its complex design, incorporating two different assay methods, requires careful consideration. Accordingly, several normalization schemes have been published. We have taken advantage of known DNA methylation patterns associated with genomic imprinting and X-chromosome inactivation (XCI), in addition to the performance of SNP genotyping assays present on the array, to derive three independent metrics which we use to test alternative schemes of correction and normalization. These metrics also have potential utility as quality scores for datasets. Results: The standard index of DNA methylation at any specific CpG site is β = M/(M + U + 100) where M and U are methylated and unmethylated signal intensities. Betas calculated from raw signal intensities (the default GenomeStudio behaviour) perform well, but using 11 methylomic datasets we demonstrate that quantile normalization methods produce marked improvement, even in highly consistent data, by all three metrics. The commonly used procedure of normalizing betas is inferior to the separate normalization of M and U, and it is also advantageous to normalize Type I and Type II assays separately. More elaborate manipulation of quantiles proves to be counterproductive. Conclusions: Careful selection of preprocessing steps can minimise variance and thus improve statistical power, especially for the detection of the small absolute DNA methylation changes likely associated with complex disease phenotypes. For the convenience of the research community we have created an R software package called wateRmelon, compatible with the existing methylumi, minfi and IMA packages, that allows others to utilise the same normalization methods and data quality tests on 450K data.

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Genome-Wide DNA methylation analysis of tumor and adjacent normal prostate tissues

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