Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer

Abstract

Epithelial ovarian cancer is the leading cause of death from gynecologic cancer, in part because of the lack of effective early detection methods. Although alterations of several genes, such as c-erb-B2, c-myc, and p53, have been identified in a significant fraction of ovarian cancers, none of these mutations are diagnostic of malignancy or predictive of tumor behavior over time. Here, we used oligonucleotide microarrays with probe sets complementary to >6,000 human genes to identify genes whose expression correlated with epithelial ovarian cancer. We extended current microarray technology by simultaneously hybridizing ovarian RNA samples in a highly parallel manner to a single glass wafer containing 49 individual oligonucleotide arrays separated by gaskets within a custom-built chamber (termed ''array-of-arrays''). Hierarchical clustering of the expression data revealed distinct groups of samples. Normal tissues were readily distinguished from tumor tissues, and tumors could be further subdivided into major groupings that correlated both to histological and clinical observations, as well as cell type-specific gene expression. A metric was devised to identify genes whose expression could be considered ideal for molecular determination of epithelial ovarian malignancies. The list of genes generated by this method was highly enriched for known markers of several epithelial malignancies, including ovarian cancer. This study demonstrates the rapidity with which large amounts of expression data can be generated. The results highlight important molecular features of human ovarian cancer and identify new genes as candidate molecular markers.

Immunofluorometric quantitation and histochemical localisation of kallikrein 6 protein in ovarian cancer tissue: a new independent unfavourable prognostic biomarker

“…Such studies have identified the gene coding for HE4, a secreted extracellular protease inhibitor, as a promising diagnostic marker (Ono et al, 2000) and pinpointed other genes that correlate with serous and mucinous histology (Schummer et al, 1999). A recent report (Welsh et al, 2001) analysed the expression levels of more than 6000 human genes in 27 ovarian tumours and four normal ovarian tissue samples using this technique. This study identified genes whose expression pattern distinguished between ovarian cancer of low and high malignancy and ranked other genes in terms of the diagnostic potential of their expression.…”

Gene expression profiling of primary cultures of ovarian epithelial cells identifies novel molecular classifiers of ovarian cancer

“…Interestingly, this classifier allowed a lower ratio of errors/correctly classified (r ¼ 0.05) than the set of 126 genes. To validate the classifier, an additional independent test set of 23 primary culture samples and 137 arrays of surgical specimens from publicly available data sets derived from Affymetrix HuFL GeneChip platform (Welsh et al, 2001;Schwartz et al, 2002;Ouellet et al, 2005) were tested. The set of 23 primary culture samples contained two LMP serous ascites.…”

Gene expression profiles in primary ovarian serous papillary tumors and normal ovarian epithelium: Identification of candidate molecular markers for ovarian cancer diagnosis and therapy

“…[3][4][5][6][7][8][9][10][11] Gene expression fingerprints representing large numbers of genes may allow precise and accurate grouping of human tumors and may identify patients who are unlikely to be cured by conventional therapy. Consistent with this view, evidence has been provided to support the notion that poor-prognosis B-cell lymphomas and biologically aggressive breast and ovarian carcinomas can be readily distinguished by gene expression profiles.…”