Murine Model of Osteosarcoma Metastasis-associated Differences in Gene Expression in a Updated Version

Despite advances in the management of osteosarcoma (OSA) and other solid tumors, the development of metastasis continues to be the most significant problem and cause of death for cancer patients. To define genetic determinants of pulmonary metastasis, we have applied cDNA microarrays to a recently described murine model of OSA that is characterized by orthotopic tumor growth, a period of minimal residual disease, spontaneous pulmonary metastasis, and cell line variants that differ in metastatic potential. Microarray analysis defined 53 genes (of 3166 unique cDNAs) that were differentially expressed between the primary tumors of the more aggressive (K7M2) and less aggressive (K12) OSA models. By review of the literature, these differentially expressed genes were assigned to six nonmutually exclusive metastasis-associated categories (proliferation and apoptosis, motility and cytoskeleton, invasion, immune surveillance, adherence, and angiogenesis). Functional studies to evaluate K7M2 and K12 for differences in each of these metastasisassociated processes revealed enhanced motility, adherence, and angiogenesis in the more aggressive K7M2 model. For this reason, 10 of the 53 differentially expressed genes that were assigned to the motility and cytoskeleton, adherence, and angiogenesis categories were considered as most likely to define differences in the metastatic behavior of the two models. Ezrin, a gene not described previously in OSA, with functions in motility, invasion, and adherence, was 3-fold overexpressed in K7M2 compared with K12 by microarray. Differential expression for RNA was confirmed by Northern analysis and for protein by immunostaining. Alterations in ezrin protein levels and concomitant cytoskeletal changes in our model confirmed predictions from the arrays. The potential relevance of ezrin in OSA was suggested by its expression in five of five human OSA cell lines. This work represents a rationale approach to the evaluation of microarray data and will be useful to identify genes that may be causally associated with metastasis.