Changes in Gene Expression and Cellular Architecture in an Ovarian Cancer Progression Model
Ovarian cancer is the fifth leading cause of cancer deaths among women. Early stage disease often remains undetected due the lack of symptoms and reliable biomarkers. The identification of early genetic changes could provide insights into novel signaling pathways that may be exploited for early detection and treatment.
Mouse ovarian surface epithelial (MOSE) cells were used to identify stage-dependent changes in gene expression levels and signal transduction pathways by mouse whole genome microarray analyses and gene ontology. These cells have undergone spontaneous transformation in cell culture and transitioned from non-tumorigenic to intermediate and aggressive, malignant phenotypes. Significantly changed genes were overrepresented in a number of pathways, most notably the cytoskeleton functional category. Concurrent with gene expression changes, the cytoskeletal architecture became progressively disorganized, resulting in aberrant expression or subcellular distribution of key cytoskeletal regulatory proteins (focal adhesion kinase, α-actinin, and vinculin). The cytoskeletal disorganization was accompanied by altered patterns of serine and tyrosine phosphorylation as well as changed expression and subcellular localization of integral signaling intermediates APC and PKCβII.
Our studies have identified genes that are aberrantly expressed during MOSE cell neoplastic progression. We show that early stage dysregulation of actin microfilaments is followed by progressive disorganization of microtubules and intermediate filaments at later stages. These stage-specific, step-wise changes provide further insights into the time and spatial sequence of events that lead to the fully transformed state since these changes are also observed in aggressive human ovarian cancer cell lines independent of their histological type. Moreover, our studies support a link between aberrant cytoskeleton organization and regulation of important downstream signaling events that may be involved in cancer progression. Thus, our MOSE-derived cell model represents a unique model for in depth mechanistic studies of ovarian cancer progression.