A systems biology approach to defining metastatic biomarkers and signaling pathways.

Metastasis is the final stage of cancer and the primary cause of mortality for most solid malignancies. This terminal phase of cancer progression has been investigated using a variety of high-throughput technologies (i.e., gene expression arrays, array comparative genomic hybridization (aCGH), and proteomics) to identify prognostic expression profiles and better characterize the metastatic process. For decades, the predominant model for the metastatic process has been the 'progression model', yet recent microarray results tend to support an inherent metastatic capability within primary tumors. Moreover, studies using a highly metastatic transgenic mammary tumor model suggest that germline polymorphisms are significant determinants of metastatic efficiency. Likewise, a strong concordance of survival has been observed between family members with cancer, further supporting the link between genetic inheritance and survival. In addition, chromosomal aberrations and signaling pathways related to metastatic capacity have been identified by array comparative genomic hybridization (aCGH) and proteomic studies, respectively. Lastly, carcinoma enzyme activity profiles using activity-based proteomics (ABPP), may be more clinically useful than expression-based proteomics for certain cancers. Most importantly, the application of these high-throughput techniques should expedite the search for additional biomarkers, germline polymorphisms, and expression signatures with greater prognostic value.