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 prometastatic transcriptional state. To overcome this limitation, we have applied network analysis to two independent human breast cancer datasets and three different mouse populations developed for quantitative analysis of metastasis. Analysis of these datasets 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 susceptibility to metastatic disease is cell-autonomous in estrogen receptor-positive tumors and associated with the mitotic spindle checkpoint. In contrast, nontumor genetics and pathway activities-associated stromal biology are significant modifiers of the rate of metastatic spread of estrogen receptor-negative tumors. 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.gene expression | mouse models R ecent advances in global transcriptome analysis has enabled better understanding of the different subtypes of breast cancer (1), as well as tumor prognosis and treatment (2). Gene signatures derived from these analyses have provided new opportunities for better tailoring of treatment options based on individual tumor biology. However, although these signatures are potentially important clinical tools, for the most part they do not provide novel insight regarding the underlying mechanisms. This lack of insight is in part because they were developed as prognostic classifiers, based on a minimum set of genes rather than to interrogate the mechanisms underlying tumor biology. The ability of these clinical classifiers to investigate molecular mechanism is further complicated by the minimal overlap between independent signatures derived from different studies. The lack of overlap is thought to be because of the fact that there are likely thousands of genes that correlate with tumor progression (3). Membership of the individual genes in each signature is therefore dictated by the transcriptional patterns derived from the specific patient populations. Subtle variations in those populations result in different gene sets meeting the statistical thresholds to be included in the final signature. Using conventional methods, it has been estimated that thousands of samples would be required to develop a robust, stable signature (4). Thus, although these signatures have important potential for clinical applications, comparisons of the signatures have not provided similar benefit for the elucidation of mechanisms of m...