Analysis of Genes Predictive of Breast Cancer Metastasis

Institution: University of California, Davis
Investigator(s): Jeffrey Gregg, M.D. -
Award Cycle: 2000 (Cycle VI) Grant #: 6KB-0074 Award: $221,925
Award Type: New Investigator Awards
Research Priorities
Biology of the Breast Cell>Pathogenesis: understanding the disease

Initial Award Abstract (2000)
A key predictor of breast cancer prognosis is metastasis (the movement of breast cancer to other tissues/organs): the higher the degree of metastasis, the lower the rate of survival. Because cancer is a consequence of a broad alteration of cell signaling pathways, the ability of cells to metastasize may be due to changes in a limited number of pathways related to invasiveness and metastasis. We believe that a molecular dissection of these pathways may lead to a better understanding of the basis of breast cancer and the role of metastasis in breast cancer, ultimately contributing to diagnosis and treatment of the disease.

In this project, we will use a very relevant mouse model to investigate the metastatic pathways at a molecular level. One of the mouse mammary tumor lines we have demonstrates 100% metastasis to the lungs. The other exhibits only 9% metastasis to the lungs. By utilizing several powerful gene expression techniques (cDNA microarrays, RT-PCR, suppression subtractive hybridization, tyrosine kinase profiling) studying the molecular differences between these two tumor lines, we may be able to identify key genes involved in the development of metastatic breast cancer. The goal of this project is to gain a clearer understanding of the molecular pathways involved in metastatic breast cancer, ultimately determining clinically relevant predictive gene expression patterns that will guide therapy for the individual patient.

Final Report (2004)
One of the significant predictors of breast cancer prognosis is regional and distant metastasis. In order to study metastatic disease, we employed the use of two related polyomavirus middle T (PyV-mT) transgenic mouse tumor transplant models of mammary carcinoma (termed Met and Db) that display significant differences in metastatic potential.

Through gene expression microarray and biochemical assays we have been dissecting this model in order to better understand the genes involved in metastasis. With gene expression profiling, we identified osteopontin (OPN) to be a highly expressed gene in the tumors of the metastatic mouse model, and a lowly expressed gene in the tumors of the lowly metastatic mouse model. We further analyzed the role of OPN in this model by examining sense and anti-sense constructs using in vitro and in vivo methods.

With in vivo metastasis assays, the anti-sense Met cells (Met-As) showed no metastatic tumor formation to the lungs of recipient mice, while wild-type Met cells, with higher levels of OPN, showed significant amounts of metastasis. The Db cells showed a significantly reduced metastasis rate in the in vivo metastasis assay as compared to Met cells; Db cells with enforced overexpression of osteopontin (Db-S), showed elevated levels of OPN but did not demonstrate an increase in the rate of metastasis compared to the wild-type Db cells.

From these studies, we conclude that OPN is an essential regulator of the metastatic phenotype seen in PyV-mT induced mammary tumors. Yet OPN expression alone is not sufficient to cause metastasis. These data suggest a link between metastasis and P13-K-mediated transcriptional upregulation of OPN, but additional P13-K regulated genes may be essential in precipitating the metastasis phenotype in the PyV-mT model. To this end we are analyzing this system to identify these very important genes.