Recent progress in our laborabory has been significant. During the past two 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 than mutation within tumors, plays a major role in the progression of human cancer. Based on these results, we have extended our analysis to identify additional genes associated with metastatic breast cancer. Using a combination of techniques and strategies including genetics, whole genome analysis of gene expression, and human epidemiology, we have identified seven additional genes that appear to play a role in human breast cancer progression. Furthermore, molecular characterization of these genes demonstrated that several physically interact within cells, and all appear to be involved in a common gene expression network. A putative network of these genes has been assembled and tentatively titled the Diasporin network. Additional efforts have focused on the ability of these metastasis modifier genes to be used in clinical prognostic tests. Work in other laboratories has demonstrated that metastatic and non-metastatic breast cancers display different gene expression patterns, which can be used to predict the likelihood that a patient will progress to metastatic disease. Using publicly available human breast cancer gene expression data, we investigated the effect of each of our candidate genes to predict outcome. These experiments demonstrated that the metastasis modifier candidate genes were capable of inducing a predictive gene signature for human patients, and provided the first putative molecular basis for these clinically relevant tests. Current investigations include characterizations of additional candidate genes as well as further investigations into previously identified metastasis modifier genes. Ongoing work on the Diasporin pathway suggests that genetic modification of breast cancer metastasis may be the result of epigenetic changes with tumor cells. Additional efforts are focused on further characterization of the molecular basis of the clinical prognostic gene expression patterns. Using these complementary systems biology and systems genetics 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.