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Publications (3)20.02 Total impact

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    ABSTRACT: The histopathological diagnosis of high-grade endometrioid and serous carcinoma of the ovary is poorly reproducible under the current morphology based classification system, especially for anaplastic, high-grade tumours. The transcription factor Wilms' tumour-1 (WT1) is differentially expressed among the gynaecological epithelia from which epithelial ovarian cancers (EOCs) are believed to originate. In EOCs, WT1 protein is observed in the majority of serous carcinomas and in up to 30% of endometrioid carcinomas. It is unclear whether the latter is a reflection of the actual incidence of WT1 protein expression in endometrioid carcinomas, or whether a significant number of high-grade serous carcinomas have been misclassified as endometrioid carcinoma. Several genetic aberrations are reported to occur in EOCs. These include mutation of the TP53 gene, aberrant activation of beta-catenin signalling and loss of PTEN protein expression, among others. It is unclear whether these aberrations are histotype-specific. The aim of this study was to better define the molecular characteristics of serous and endometrioid carcinomas in an attempt to address the problems with the current histopathological classification methods. Gene expression profiles were analysed to identify reproducible gene expression phenotypes for endometrioid and serous carcinomas. Tissue microarrays (TMA) were used to assess the incidence of TP53, beta-catenin and PTEN aberrations in order to correlate their occurrence with WT1 as an immunohistochemistry based biomarker of serous histotype. It was found that nuclear WT1 protein expression can identify misclassified high-grade endometrioid carcinomas and these tumours should be reassigned to serous histotype. Although low-grade endometrioid carcinomas rarely progress to high-grade carcinomas, a combined WT1-negative, TP53-positive immunophenotype may identify an uncommon high-grade subtype of ovarian endometrioid carcinoma. GEO database: array data accession number GSE6008.
    The Journal of Pathology 10/2009; 220(3):392-400. · 7.59 Impact Factor
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    ABSTRACT: In order to elucidate the biological variance between normal ovarian surface epithelial (NOSE) and epithelial ovarian cancer (EOC) cells, and to build a molecular classifier to discover new markers distinguishing these cells, we analysed gene expression patterns of 65 primary cultures of these tissues by oligonucleotide microarray. Unsupervised clustering highlights three subgroups of tumours: low malignant potential tumours, invasive solid tumours and tumour cells derived from ascites. We selected 18 genes with expression profiles that enable the distinction of NOSE from these three groups of EOC with 92% accuracy. Validation using an independent published data set derived from tissues or primary cultures confirmed a high accuracy (87-96%). The distinctive expression pattern of a subset of genes was validated by quantitative reverse transcription-PCR. An ovarian-specific tissue array representing tissues from NOSE and EOC samples of various subtypes and grades was used to further assess the protein expression patterns of two differentially expressed genes (Msln and BMP-2) by immunohistochemistry. This study highlights the relevance of using primary cultures of epithelial ovarian cells as a model system for gene profiling studies and demonstrates that the statistical analysis of gene expression profiling is a useful approach for selecting novel molecular tumour markers.
    British Journal of Cancer 03/2006; 94(3):436-45. · 5.08 Impact Factor
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    ABSTRACT: Tumors of low malignant potential (LMP) represent 20% of epithelial ovarian cancers (EOCs) and are associated with a better prognosis than the invasive tumors (TOV). Defining the relationship between LMPs and TOVs remains an important goal towards understanding the molecular pathways that contribute to prognosis, as well as providing molecular markers, for these EOCs. To this end, DNA microarray analyses were performed either in a primary culture or a tumor tissue model system and selected candidate genes showing a distinctive expression profile between LMPs and TOVs were identified using a class prediction approach based on three statistical methods of analysis. Both model systems appear relevant as candidate genes identified by either model allowed the proper reclassification of samples as either LMPs or TOVs. Selected candidate genes (CAS, CCNE1, LGALS8, ITGbeta3, ATP1B1, FLIP, KRT7 and KRT19) were validated by real-time quantitative PCR analysis and show differential expression between LMPs and TOVs. Immunohistochemistry analyses showed that the two tumor classes were distinguishable by their expression of CAS, TNFR1A, FLIP, CKS1 and CCNE1. These results define signature patterns for gene expression of LMPs and TOVs and identify gene candidates that warrant further study to deepen our understanding of the biology of EOC.
    Oncogene 08/2005; 24(29):4672-87. · 7.36 Impact Factor