Metastatic disease is the proximal cause of mortality for most cancers and remains a significant problem for the clinical management of neoplastic disease. Recent advances in global transcriptional analysis have enabled better prediction of individuals likely to progress to metastatic disease. However, minimal overlap between predictive signatures has precluded easy identification of key biological processes contributing to the pro-metastatic transcriptional state. To overcome this limitation, we have applied network analysis to two independent human breast cancer data sets and three different mouse populations developed for quantitative analysis of metastasis. Analysis of these data sets revealed that the gene membership of the networks is highly conserved within and between species, and that these networks predicted distant metastasis free survival. Furthermore these results suggest that inherited susceptibility to metastatic disease is cell autonomous in ER+ tumors and associated with the mitotic spindle checkpoint. In contrast, susceptibility to metastasis is tumor non-autonomous in ER- patients and associated with an inherited difference in immune infiltration of the primary tumor. These results suggest that the application of network analysis across species may provide a robust method to identify key biological programs associated with human cancer progression.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2980. doi:1538-7445.AM2012-2980