Genome-wide DNA methylation, gene expression, and copy number integrative analysis of melanoma brain metastasis
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE44662 | GEO | 2013/09/19
SECONDARY ACCESSION(S): PRJNA190781
REPOSITORIES: GEO
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Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2013-09-19 | E-GEOD-44662 | biostudies-arrayexpress
Project description:Genome-wide DNA methylation, gene expression, and copy number integrative analysis of melanoma brain metastasis
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Project description:DNA methylation is an essential epigenetic modification that plays a key role associated with the regulation of gene expression during differentiation, but in disease states such as cancer, the DNA methylation landscape is often deregulated. There are now numerous technologies available to interrogate the DNA methylation status of CpG sites in a targeted or genome-wide fashion, but each method, due to intrinsic biases, potentially interrogates different fractions of the genome. In this study, we compare the affinity-purification of methylated DNA between two popular genome-wide techniques, methylated DNA immunoprecipitation (MeDIP) and methyl-CpG binding domain-based capture (MBDCap), and show that each technique operates in a different domain of the CpG density landscape. We explored the effect of whole-genome amplification and illustrate that it can reduce sensitivity for detecting DNA methylation in GC-rich regions of the genome. By using MBDCap, we compare and contrast microarray- and sequencing-based readouts and highlight the impact that copy number variation (CNV) can make in differential comparisons of methylomes. These studies reveal that the analysis of DNA methylation data and genome coverage is highly dependent on the method employed, and consideration must be made in light of the GC content, the extent of DNA amplification, and the copy number.
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Project description:Germline copy number variants (CNVs) and somatic copy number alterations (SCNAs) are of significant importance in syndromic conditions and cancer. Massively parallel sequencing is increasingly used to infer copy number information from variations in the read depth in sequencing data. However, this approach has limitations in the case of targeted re-sequencing, which leaves gaps in coverage between the regions chosen for enrichment and introduces biases related to the efficiency of target capture and library preparation. We present a method for copy number detection, implemented in the software package CNVkit, that uses both the targeted reads and the nonspecifically captured off-target reads to infer copy number evenly across the genome. This combination achieves both exon-level resolution in targeted regions and sufficient resolution in the larger intronic and intergenic regions to identify copy number changes. In particular, we successfully inferred copy number at equivalent to 100-kilobase resolution genome-wide from a platform targeting as few as 293 genes. After normalizing read counts to a pooled reference, we evaluated and corrected for three sources of bias that explain most of the extraneous variability in the sequencing read depth: GC content, target footprint size and spacing, and repetitive sequences. We compared the performance of CNVkit to copy number changes identified by array comparative genomic hybridization. We packaged the components of CNVkit so that it is straightforward to use and provides visualizations, detailed reporting of significant features, and export options for integration into existing analysis pipelines. CNVkit is freely available from https://github.com/etal/cnvkit.
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Project description:BackgroundHeritable epigenetic alterations have been proposed as an explanation for familial clustering of melanoma. Here we performed genome-wide DNA methylation analysis on affected family members not carrying pathogenic variants in established melanoma susceptibility genes, compared with healthy volunteers.ResultsAll melanoma susceptibility genes showed the absence of epimutations in familial melanoma patients, and no loss of imprinting was detected. Unbiased genome-wide DNA methylation analysis revealed significantly different levels of methylation in single CpG sites. The methylation level differences were small and did not affect reported tumour predisposition genes.ConclusionOur results provide no support for heritable epimutations as a cause of familial melanoma.
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Project description:Melanoma has a high tendency to metastasize to brain tissue. The understanding about the molecular alterations of early-stage melanoma progression to brain metastasis (MBM) is very limited. Identifying MBM-specific genomic and epigenomic alterations is a key initial step in understanding its aggressive nature and identifying specific novel druggable targets. Here we describe a multi-platform dataset generated with different stages of melanoma progression to MBM. This data includes genome-wide DNA methylation (Illumina HM450K BeadChip), gene expression (Affymetrix HuEx 1.0 ST array), single nucleotide polymorphisms (SNPs) and copy number variation (CNV; Affymetrix SNP 6.0 array) analyses of melanocyte cells (MNCs), primary melanoma tumors (PRMs), lymph node metastases (LNMs) and MBMs. The analysis of this data has been reported in our recently published study (Marzese et al., 2014).
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Project description:Colorectal cancer is a heterogeneous disease caused by both genetic and epigenetics factors. Analysing DNA methylation changes occurring during colorectal cancer progression and metastasis formation is crucial for the identification of novel epigenetic markers of patient prognosis. Genome-wide methylation sequencing of paired samples of colon (normal adjacent, primary tumour and lymph node metastasis) showed global hypomethylation and CpG island (CGI) hypermethylation of primary tumours compared to normal. In metastasis we observed high global and non-CGI regions methylation, but lower CGI methylation, compared to primary tumours. Gene ontology analysis showed shared biological processes between hypermethylated CGIs in metastasis and primary tumours. After complementary analysis with The Cancer Genome Atlas (TCGA) cohort, FIGN, HTRA3, BDNF, HCN4 and STAC2 genes were found associated with poor survival. We mapped the methylation landscape of colon normal tissues, primary tumours and lymph node metastasis, being capable of identified methylation changes throughout the genome. Furthermore, we found five genes with potential for methylation biomarkers of poor prognosis in colorectal cancer patients.
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