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

Epithelial ovarian cancer is the leading cause of death from gynecologic ca ncer, in part because of the lack of effective early detection methods. Alt hough alterations of several genes, such as c-erb-B2 c-myc, and p53, have b een 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 complement ary to >6,000 human genes to identify genes whose expression correlated wit h epithelial ovarian cancer. We extended current microarray technology by s imultaneously hybridizing ovarian RNA samples in a highly parallel manner t o a single glass wafer containing 49 individual oligonucleotide arrays sepa rated 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 he further subdivided into major groupings that correlated bot h to histological and clinical observations, as well as cell type-specific gene expression. A metric was devised to identify genes whose expression co uld be considered ideal for molecular determination of epithelial ovarian m alignancies. The list of genes generated by this method was highly enriched for known markers of several epithelial malignancies, including ovarian ca ncer. This study demonstrates the rapidity with which large amounts of expr ession data can be generated. The results highlight important molecular fea tures of human ovarian cancer and identify new genes as candidate molecular markers.