Extracellular matrix composition modulates angiosarcoma cell attachment and proliferation.

Angiosarcoma is a rare and generally fatal tumor composed of aberrant cells of endothelial origin. Because of its infrequency in humans, very little is known about the growth requirements of this vascular sarcoma. Unlike the rapidly proliferating solid tumors from which they are isolated from, many of the established angiosarcoma cell lines exhibit less than robust growth in culture and often fail to form tumors in xenograft models. In order to better understand angiosarcoma in vitro growth conditions, we focused on a singular aspect of their culture-adhesion to the extracellular matrix-in order to identify attachment substrates that may facilitate and/or enhance their growth in tissue culture. Our data indicates that the extracellular matrix of angiosarcomas contains similar protein compositions to that of non-diseased endothelial cells. Moreover, angiosarcoma cell lines exhibited strong attachment preference to substrates such as collagen I or fibronectin, and less preference to collagen IV, laminin, or tropoelastin. Growth on preferred extracellular matrix substrates promoted mitogenic signaling and increased proliferation of angiosarcoma cell lines. These findings provide insight that may lead to more successful in vitro growth of angiosarcoma cell lines.

Functional genomics analysis reveals a MYC signature associated with a poor clinical prognosis in liposarcomas.

Liposarcomas, which are malignant fatty tumors, are the second most common soft-tissue sarcomas. Several histologically defined liposarcoma subtypes exist, yet little is known about the molecular pathology that drives the diversity in these tumors. We used functional genomics to classify a panel of diverse liposarcoma cell lines based on hierarchical clustering of their gene expression profiles, indicating that liposarcoma gene expression profiles and histologic classification are not directly correlated. Boolean probability approaches based on cancer-associated properties identified differential expression in multiple genes, including MYC, as potentially affecting liposarcoma signaling networks and cancer outcome. We confirmed our method with a large panel of lipomatous tumors, revealing that MYC protein expression is correlated with patient survival. These data encourage increased reliance on genomic features in conjunction with histologic features for liposarcoma clinical characterization and lay the groundwork for using Boolean-based probabilities to identify prognostic biomarkers for clinical outcome in tumor patients.