Expression profiling of liposarcoma yields a multigene predictor of patient outcome and identifies genes that contribute to liposarcomagenesis
Liposarcomas are the most common type of soft tissue sarcoma but their genetics are poorly defined. To identify genes that contribute to liposarcomagenesis and serve as prognostic candidates, we undertook expression profiling of 140 primary liposarcoma samples, which were randomly split into training set (n = 95) and test set (n = 45). A multigene predictor for distant recurrence-free survival (DRFS) was developed by the supervised principal component method. Expression levels of the 588 genes in the predictor were used to calculate a risk score for each patient. In validation of the predictor in the test set, patients with low risk score had a 3-year DRFS of 83% versus 45% for high risk score patients (P = 0.001). The HR for high versus low score, adjusted for histologic subtype, was 4.42 (95% CI, 1.26-15.55; P = 0.021). The concordance probability for risk score was 0.732. In contrast, the concordance probability for histologic subtype, which had been considered the best predictor of outcome in liposarcoma, was 0.669. Genes related to adipogenesis, DNA replication, mitosis, and spindle assembly checkpoint control were all highly represented in the multigene predictor. Three genes from the predictor, TOP2A, PTK7, and CHEK1, were found to be overexpressed in liposarcoma samples of all five subtypes and in liposarcoma cell lines. RNAi-mediated knockdown of these genes in liposarcoma cell lines reduced proliferation and invasiveness and increased apoptosis. Taken together, our findings identify genes that seem to be involved in liposarcomagenesis and have promise as therapeutic targets, and support the use of this multigene predictor to improve risk stratification for individual patients with liposarcoma.