Publications (44) View all
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Article: Identification of novel breast cancer-associated transcripts by uniGene database mining and gene expression analysis in normal and malignant cells.
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ABSTRACT: Breast cancer is a heterogeneous and complex disease. Although the use of tumor biomarkers has improved individualized breast cancer care, i.e., assessment of risk, diagnosis, prognosis, and prediction of treatment outcome, new markers are required to further improve patient clinical management. In the present study, a search for novel breast cancer-associated genes was performed by mining the UniGene database for expressed sequence tags (ESTs) originating from human normal breast, breast cancer tissue, or breast cancer cell lines. Two hundred and twenty-eight distinct breast-associated UniGene Clusters (BUC1-228) matched the search criteria. Four BUC ESTs (BUC6, BUC9, BUC10, and BUC11) were subsequently selected for extensive in silico database searches, and in vitro analyses through sequencing and RT-PCR based assays on well-characterized cell lines and tissues of normal and cancerous origin. BUC6, BUC9, BUC10, and BUC11 are clustered on 10p11.21-12.1 and showed no homology to any known RNAs. Overall, expression of the four BUC transcripts was high in normal breast and testis tissue, and in some breast cancers; in contrast, BUC was low in other normal tissues, peripheral blood mononuclear cells (PBMCs), and other cancer cell lines. Results to-date suggest that BUC11 and BUC9 translate to protein and BUC11 cytoplasmic and nuclear protein expression was detected in a large cohort of breast cancer samples using immunohistochemistry. This study demonstrates the discovery and expression analysis of a tissue-restricted novel transcript set which is strongly expressed in breast tissue and their application as clinical cancer biomarkers clearly warrants further investigation. © 2012 Wiley Periodicals, Inc..Genes Chromosomes and Cancer 12/2012; · 3.31 Impact Factor -
Article: TOMM34 expression in early invasive breast cancer: a biomarker associated with poor outcome.
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ABSTRACT: Appropriate mitochondrial functioning in normal cells depends on proper functioning of mitochondrial translocation machinery, of which translocase of the outer membrane of mitochondria (TOMM) plays important role. The aim of this study was to explore the expression of TOMM34 in invasive breast cancer (BC) with relevance to BC molecular subtypes and patients' outcome. Gene expression data of 128 BC were analysed using artificial neuronal network (ANN) analysis to identify differentially expressed genes between BC with distant metastases and that without distant metastases. TOMM34 expression was assessed in a large series of BC (n = 1,061) with long-term follow-up using tissue microarray and immunohistochemistry. TOMM34 protein expression was quantitatively measured using the novel reverse phase protein microarray (RPPA) technique. ANN analysis revealed TOMM34 gene transcript as one of the top differentially expressed gene correlated with BC distant metastasis. Protein expression of TOMM34 was associated with features of aggressive behaviour including higher tumour grade, advanced nodal stage, larger tumour size and lymphovascular invasion. TOMM34 over-expression was significantly associated with shorter BC-specific survival and metastasis-free survival independent of standard prognostic parameters. TOMM34 protein expression was quantified by RPPA which showed that the mean expression values of TOMM34 were higher in samples demonstrating features of poor outcome. This study demonstrates at translational protein expression level that TOMM34 is a marker of poor prognosis in BC. Our findings underscore the role played by mitochondrial machinery in BC progression and warrant their validation on a prospective basis.Breast Cancer Research and Treatment 10/2012; · 4.43 Impact Factor -
Conference Proceeding: MS-Labeller: Bioinformatics support for quality assessment on high resolution mass spectrometry sample.
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ABSTRACT: A major criticism of mass spectrometry publications is their data reproducibility. Here we address this criticism by including human quality control samples amongst other controls alongside every sample cohort analyzed. One of the reasons for such criticism may due to a lack of standard approach for mass spectrometric quality assessment. This study proposes an innovative bioinformatics approach to examine the quality of human control samples in the light of alleviating reproducibility issues. Results demonstrated the feasibility of a parsimony bioinformatics model to overcome human subject when inspecting sample quality of mass spectrometric data.Proceedings of the IEEE-EMBS international conference on biomedical and health informatics; 01/2012 -
Conference Proceeding: Modeling Estrogen Receptor Pathways In Breast Cancer Using An Artificial Neural Networks Based Inference Approach
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ABSTRACT: Estrogen receptor (ER) status is an important consideration in the prognosis and management of breast cancer patients, dictating treatment and patient management. While the prognosis of ER positive patients is generally poorer because of treatments such as Tamoxifen this situation has been reversed. Some detail is known of the ER pathway, however this has been based on reductionist studies of small nu mbers of markers. Here we present an Artificial Neural Network (ANN) using a feed forward back-propagation algorithm applied to a three layer multi-layer perceptron based approach that facilitates a wider more holistic approach to the identification of genes associated with ER status and the modeling of their interactions with one another in the context of a pathway.IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI 201 2), Hong Kong and Shenzhen, China; 01/2012 -
Article: A comparative biomarker study of 514 matched cases of male and female breast cancer reveals gender-specific biological differences.
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ABSTRACT: Male breast cancer remains understudied despite evidence of rising incidence. Using a co-ordinated multi-centre approach, we present the first large scale biomarker study to define and compare hormone receptor profiles and survival between male and female invasive breast cancer. We defined and compared hormone receptor profiles and survival between 251 male and 263 female breast cancers matched for grade, age, and lymph node status. Tissue microarrays were immunostained for ERα, ERβ1, -2, -5, PR, PRA, PRB and AR, augmented by HER2, CK5/6, 14, 18 and 19 to assist typing. Hierarchical clustering determined differential nature of influences between genders. Luminal A was the most common phenotype in both sexes. Luminal B and HER2 were not seen in males. Basal phenotype was infrequent in both. No differences in overall survival at 5 or 10 years were observed between genders. Notably, AR-positive luminal A male breast cancer had improved overall survival over female breast cancer at 5 (P = 0.01, HR = 0.39, 95% CI = 0.26-0.87) but not 10 years (P = 0.29, HR = 0.75, 95% CI = 0.46-1.26) and both 5 (P = 0.04, HR = 0.37, 95% CI = 0.07-0.97) and 10 years (P = 0.04, HR = 0.43, 95% CI = 0.12-0.97) in the unselected group. Hierarchical clustering revealed common clusters between genders including total PR-PRA-PRB and ERβ1/2 clusters. A striking feature was the occurrence of ERα on distinct clusters between genders. In female breast cancer, ERα clustered with PR and its isoforms; in male breast cancer, ERα clustered with ERβ isoforms and AR. Our data supports the hypothesis that breast cancer is biologically different in males and females suggesting implications for clinical management. With the incidence of male breast cancer increasing this provides impetus for further study.Breast Cancer Research and Treatment 11/2011; 133(3):949-58. · 4.43 Impact Factor
