Recent progress in our laborabory has been significant. During the past years we published the results of our first study of the effect of constitutional polymorphism influencing metastasis. We demonstrated that a single amino acid polymorphism in the gene Sipa1 could modulate the metastatic capacity of mouse mammary tumors by as much as ten-fold. Furthermore we demonstrated, using pilot human epidemiology studies, that polymorphisms in the human ortholog of this gene, SIPA1, were associated with lymph node status in human breast cancer. These findings were the first published example that genetic inheritance rather that mutation within tumors, plays a major role in progression of human cancer. More recently we have been extending these results to investigate the role of additional genes in the establishment of metastasis susceptibility. Two complementary approaches are being pursued. First, using the Sipa1 molecule as a nucleus, we are identifying and characterizing gene products that physically interact with the metastasis modifying protein Sipa1, with particular focus on those protein binding partners that interact with the polymorphic PDZ domain. Current efforts have revealed that at least three of the putative collection of candidate proteins demonstrate significant effects on metastasis in experimental systems, as well as evidence of a role in human breast cancer in pilot epidemiology studies. The second avenue of research is based on expression quantitative trait mapping. Using our highly metastatic mouse model and a complex trait genetics strategy, we have identified an additional 5 genes that demonstrated experimental associations with mammary tumor progression. Analysis of expression array data suggests that at least 4 of these genes may play a role in human breast cancer metastasis susceptibility, with 3 of the novel genes suppressing tumor progression, and one potentially increasing metastasis susceptbility. Using these complementary methodologies, we plan to continue our genetic and genomic analysis of the genome architecture associated with breast cancer tumor progression. The data generated in these studies will be further analyzed by state-of-the-art computational methods to further elucidate the complex interacting networks associated with metastatic progression.
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