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.

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