[show abstract][hide abstract] 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.
[show abstract][hide abstract] 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.
[show abstract][hide abstract] 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.
[show abstract][hide abstract] ABSTRACT: High expression of Rac small GTPases in invasive breast ductal carcinoma is associated with poor prognosis, but its therapeutic value in human cancers is not clear. The aim of the current study was to determine the response of human primary breast cancers to Rac-based drug treatments ex vivo. Three-dimensional organotypic cultures were used to assess candidate therapeutic avenues in invasive breast cancers. Uniquely, in these primary cultures, the tumour is not disaggregated, with both epithelial and mesenchymal components maintained within a 3-dimensional matrix of type I collagen. EHT 1864, a small molecule inhibitor of Rac GTPases, prevents spread of breast cancers in this setting, and also reduces proliferation at the invading edge. Rac1+ epithelial cells in breast tumours also contain high levels of the phosphorylated form of the transcription factor STAT3. The small molecule Stattic inhibits activation of STAT3 and induces effects similar to those seen with EHT 1864. Pan-Rac inhibition of proliferation precedes down-regulation of STAT3 activity, defining it as the last step in Rac activation during human breast cancer invasion. Our data highlights the potential use of Rac and STAT3 inhibition in treatment of invasive human breast cancer and the benefit of studying novel cancer treatments using 3-dimensional primary tumour tissue explant cultures.
[show abstract][hide abstract] ABSTRACT: The discovery of substantial amounts of 5-hydroxymethylcytosine (5hmC), formed by the oxidation of 5-methylcytosine (5mC), in various mouse tissues and human embryonic stem (ES) cells has necessitated a reevaluation of our knowledge of 5mC/5hmC patterns and functions in mammalian cells. Here, we investigate the tissue specificity of both the global levels and locus-specific distribution of 5hmC in several human tissues and cell lines. We find that global 5hmC content of normal human tissues is highly variable, does not correlate with global 5mC content, and decreases rapidly as cells from normal tissue adapt to cell culture. Using tiling microarrays to map 5hmC levels in DNA from normal human tissues, we find that 5hmC patterns are tissue specific; unsupervised hierarchical clustering based solely on 5hmC patterns groups independent biological samples by tissue type. Moreover, in agreement with previous studies, we find 5hmC associated primarily, but not exclusively, with the body of transcribed genes, and that within these genes 5hmC levels are positively correlated with transcription levels. However, using quantitative 5hmC-qPCR, we find that the absolute levels of 5hmC for any given gene are primarily determined by tissue type, gene expression having a secondary influence on 5hmC levels. That is, a gene transcribed at a similar level in several different tissues may have vastly different levels of 5hmC (>20-fold) dependent on tissue type. Our findings highlight tissue type as a major modifier of 5hmC levels in expressed genes and emphasize the importance of using quantitative analyses in the study of 5hmC levels.
[show abstract][hide abstract] ABSTRACT: Invasive lobular cancer (ILC) responds poorly to neoadjuvant chemotherapy but appears to respond well to endocrine therapy. We examined the effectiveness of neoadjuvant letrozole in postmenopausal women (PMW) with estrogen receptor (ER)-rich ILC. PMW were considered for treatment with neoadjuvant letrozole if they had ER-rich, large operable, or locally advanced cancers, or were unfit for surgical therapy. Tumor volume was estimated at diagnosis and at 3 months using calipers (clinical), ultrasound, and mammography. At 3 months, if physically fit, women were assessed for surgery. Responsive women with cancers too large for breast-conserving surgery continued with letrozole. Patients had surgery or were switched to alternative therapy if tumor volume was increasing. Sixty-one patients (mean age, 76.2 years) with 63 ILCs were treated with letrozole for ≥ 3 months. The mean reduction in tumor volume at 3 months was 66% (median, 76%) measured clinically, 61% (median, 73%) measured by ultrasound, and 54% (median, 60%) measured by mammography. Surgery was possible at 3 months in 24 cancers in 24 patients, and all but two of the remaining patients continued with letrozole therapy for a median duration of 9 months. At the time of this publication, 40 patients with a total of 41 cancers have undergone surgery. The rate of successful breast conservation was 81% (25/31). Twenty-one patients have continued with letrozole monotherapy, and 19 remain controlled on letrozole at a median of 2.8 years. There is a high rate of response to letrozole in PMW with ER-rich ILC.
Breast Cancer Research and Treatment 08/2011; 130(3):871-7.
[show abstract][hide abstract] ABSTRACT: Oestrogens in breast cancers are derived from both uptake from the circulation and in situ synthesis. Third generation aromatase inhibitors (AIs) effectively block aromatase activity within the breast. The effects of AIs on the molecular biology of breast cancers may be monitored in patients given neoadjuvant therapy. Changes in tumour gene expression associated with AIs is influenced by time of drug exposure and gene expression profiles may provide important information on tumour response/ resistance to AIs.
[show abstract][hide abstract] ABSTRACT: Cancer is a complex and heterogeneous disease, not only at a genetic and biochemical level, but also at a tissue, organism, and population level. Multiple data streams, from reductionist biochemistry in vitro to high-throughput "-omics" from clinical material, have been generated with the hope that they encode useful information about phenotype and, ultimately, tumour behaviour in response to drugs. While these data stand alone in terms of the biology they represent, there is the enticing prospect that if incorporated into systems biology models, they can help understand complex systems behaviour and provide a predictive framework as an additional tool in understanding how tumours change and respond to treatment over time. Since these biological data are heterogeneous and frequently qualitative rather than quantitative, at the present time a single systems biology approach is unlikely to be effective; instead, different computational and mathematical approaches should be tailored to different types of data, and to each other, in order to test and re-test hypotheses. In time, these models might converge and result in usable tractable models which accurately represent human cancer. Likewise, biologists and clinicians need to understand what the requirements of systems biology are so that compatible data are produced for computational modelling. In this review, we describe some theoretical approaches (data-driven and process-driven) and experimental methodologies which are being used in cancer research and the clinical context where they might be applied.
Methods in molecular biology (Clifton, N.J.) 01/2010; 662:245-63.
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