Identification of Candidate Growth Promoting Genes in Ovarian Cancer through Integrated Copy Number and Expression Analysis

Ovarian cancer is a disease characterised by complex genomic rearrangements but the majority of the genes that are the target of these alterations remain unidentified. Cataloguing these target genes will provide useful insights into the disease etiology and may provide an opportunity to develop novel diagnostic and therapeutic interventions. High resolution genome wide copy number and matching expression data from 68 primary epithelial ovarian carcinomas of various histotypes was integrated to identify genes in regions of most frequent amplification with the strongest correlation with expression and copy number. Regions on chromosomes 3, 7, 8, and 20 were most frequently increased in copy number (>40% of samples). Within these regions, 703/1370 (51%) unique gene expression probesets were differentially expressed when samples with gain were compared to samples without gain. 30% of these differentially expressed probesets also showed a strong positive correlation (r≥0.6) between expression and copy number. We also identified 21 regions of high amplitude copy number gain, in which 32 known protein coding genes showed a strong positive correlation between expression and copy number. Overall, our data validates previously known ovarian cancer genes, such as ERBB2, and also identified novel potential drivers such as MYNN, PUF60 and TPX2

Display of probability densities for data from a continuous distribution

Based on cumulative distribution functions, Fourier series expansion and Kolmogorov tests, we present a simple method to display probability densities for data drawn from a continuous distribution. It is often more efficient than using histograms.Comment: 5 pages, 4 figures, presented at Computer Simulation Studies XXIV, Athens, GA, 201

High-throughput detection of aberrant imprint methylation in the ovarian cancer by the bisulphite PCR-Luminex method

<p>Abstract</p> <p>Background</p> <p>Aberrant DNA methylation leads to loss of heterozygosity (LOH) or loss of imprinting (LOI) as the first hit during human carcinogenesis. Recently we developed a new high-throughput, high-resolution DNA methylation analysis method, bisulphite PCR-Luminex (BPL), using sperm DNA and demonstrated the effectiveness of this novel approach in rapidly identifying methylation errors.</p> <p>Results</p> <p>In the current study, we applied the BPL method to the analysis of DNA methylation for identification of prognostic panels of DNA methylation cancer biomarkers of imprinted genes. We found that the BPL method precisely quantified the methylation status of specific DNA regions in somatic cells. We found a higher frequency of LOI than LOH. LOI at <it>IGF2</it>, <it>PEG1 </it>and <it>H19 </it>were frequent alterations, with a tendency to show a more hypermethylated state. We detected changes in DNA methylation as an early event in ovarian cancer. The degree of LOI (LOH) was associated with altered DNA methylation at <it>IGF2/H19 </it>and <it>PEG1</it>.</p> <p>Conclusions</p> <p>The relative ease of BPL method provides a practical method for use within a clinical setting. We suggest that DNA methylation of <it>H19 </it>and <it>PEG1 </it>differentially methylated regions (DMRs) may provide novel biomarkers useful for screening, diagnosis and, potentially, for improving the clinical management of women with human ovarian cancer.</p