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    ABSTRACT: Gonadotrophin-releasing hormone (GnRH) significantly inhibits proliferation of a proportion of cancer cell lines by activating GnRH receptor-G protein signaling. Therefore, manipulation of GnRH receptor signaling may have an under-utilized role in treating certain breast and ovarian cancers. However, the precise signaling pathways necessary for the effect and the features of cellular responses remain poorly defined. We used transcriptomic and proteomic profiling approaches to characterize the effects of GnRH receptor activation in sensitive cells (HEK293-GnRHR, SCL60) in in vitro and in vivo settings, compared to unresponsive HEK293. Analyses of gene expression demonstrated a dynamic SCL60 response to the GnRH super-agonist Triptorelin. Early and mid-phase changes (0.5-1.0 h) comprised mainly transcription factors. Later changes (8-24 h) included a GnRH target gene, CGA, and up or down-regulation of transcripts encoding signaling and cell division machinery. Pathway analysis exposed identified altered mitogen-activated protein kinase and cell cycle pathways, consistent with occurrence of G2/M arrest and apoptosis. NFκB pathway gene transcripts were differentially expressed between control and Triptorelin-treated SCL60 cultures. Reverse phase protein and phospho-proteomic array analyses profiled responses in cultured cells and SCL60 xenografts in vivo during Triptorelin anti-proliferation. Increased phosphorylated NFκB (p65) occurred in SCL60 in vitro, and p-NFκB and IκBε were higher in treated xenografts than controls after 4 days Triptorelin. NFκB inhibition enhanced the anti-proliferative effect of Triptorelin in SCL60 cultures. This study reveals details of pathways interacting with intense GnRH receptor signaling, identifies potential anti-proliferative target genes and implicates the NFκB survival pathway as a node for enhancing GnRH agonist-induced anti-proliferation.
    Full-text · Article · Nov 2012 · Endocrine Related Cancer
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    ABSTRACT: Background Aberrant CpG island promoter DNA hypermethylation is frequently observed in cancer and is believed to contribute to tumor progression by silencing the expression of tumor suppressor genes. Previously, we observed that promoter hypermethylation in breast cancer reflects cell lineage rather than tumor progression and occurs at genes that are already repressed in a lineage-specific manner. To investigate the generality of our observation we analyzed the methylation profiles of 1,154 cancers from 7 different tissue types. Results We find that 1,009 genes are prone to hypermethylation in these 7 types of cancer. Nearly half of these genes varied in their susceptibility to hypermethylation between different cancer types. We show that the expression status of hypermethylation prone genes in the originator tissue determines their propensity to become hypermethylated in cancer; specifically, genes that are normally repressed in a tissue are prone to hypermethylation in cancers derived from that tissue. We also show that the promoter regions of hypermethylation-prone genes are depleted of repetitive elements and that DNA sequence around the same promoters is evolutionarily conserved. We propose that these two characteristics reflect tissue-specific gene promoter architecture regulating the expression of these hypermethylation prone genes in normal tissues. Conclusions As aberrantly hypermethylated genes are already repressed in pre-cancerous tissue, we suggest that their hypermethylation does not directly contribute to cancer development via silencing. Instead aberrant hypermethylation reflects developmental history and the perturbation of epigenetic mechanisms maintaining these repressed promoters in a hypomethylated state in normal cells.
    Full-text · Article · Oct 2012 · Genome biology
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    ABSTRACT: Affymetrix GeneChips and Illumina BeadArrays are the most widely used commercial single channel gene expression microarrays. Public data repositories are an extremely valuable resource, providing array-derived gene expression measurements from many thousands of experiments. Unfortunately many of these studies are underpowered and it is desirable to improve power by combining data from more than one study; we sought to determine whether platform-specific bias precludes direct integration of probe intensity signals for combined reanalysis. Using Affymetrix and Illumina data from the microarray quality control project, from our own clinical samples, and from additional publicly available datasets we evaluated several approaches to directly integrate intensity level expression data from the two platforms. After mapping probe sequences to Ensembl genes we demonstrate that, ComBat and cross platform normalisation (XPN), significantly outperform mean-centering and distance-weighted discrimination (DWD) in terms of minimising inter-platform variance. In particular we observed that DWD, a popular method used in a number of previous studies, removed systematic bias at the expense of genuine biological variability, potentially reducing legitimate biological differences from integrated datasets. Normalised and batch-corrected intensity-level data from Affymetrix and Illumina microarrays can be directly combined to generate biologically meaningful results with improved statistical power for robust, integrated reanalysis.
    Full-text · Article · Aug 2012 · BMC Medical Genomics
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