Osteosarcoma is the most common malignant bone tumor in children. After initial diagnosis is made with a biopsy, treatment consists of preoperative chemotherapy followed by definitive surgery and postoperative chemotherapy. The degree of tumor necrosis in response to preoperative chemotherapy is a reliable prognostic factor and is used to guide the choice of postoperative chemotherapy. Patients with tumors, which reveal z90% necrosis (good responders), have a much better prognosis than those with <90% necrosis (poor responders). Despite previous attempts to improve the outcome of poor responders by modifying the postoperative chemotherapy, their prognosis remains poor. Therefore, there is a need to predict at the time of diagnosis patients' response to preoperative chemotherapy. This will provide the basis for developing potentially effective therapy that can be given at the outset for those who are likely to have a poor response. Here, we report the analysis of 34 pediatric osteosarcoma samples by expression profiling. Using parametric two-sample t test, we identified 45 genes that discriminate between good and poor responders (P < 0.005) in 20 definitive surgery samples. A support vector machine classifier was built using these predictor genes and was tested for its ability to classify initial biopsy samples. Five of six initial biopsy samples that had corresponding definitive surgery samples in the training set were classified correctly (83%; confidence interval, 36%, 100%). When this classifier was used to predict eight independent initial biopsy samples, there was 100% accuracy (confidence interval, 63%, 100%). Many of the predictor genes are implicated in bone development, drug resistance, and tumorigenesis. (Cancer Res 2005; 65(18): 8142-50)

Osteosarcoma is the primary malignant cancer of bone and particularly affects adolescents and young adults, causing debilitation, and sometimes death. As a model for human osteosarcoma we have been studying p53+/− mice, which develop osteosarcoma at high frequency. To discover genes that cooperate with p53 deficiency in osteosarcoma formation we have integrated array comparative genomic hybridization, microarray expression analyses in mouse and human osteosarcomas, and functional assays. In this study we found seven frequent regions of copy number gain and loss in the mouse p53+/− osteosarcomas, but have focused on a recurrent amplification event on mouse chromosome 9A1. This amplicon is syntenic with a similar chromosome 11q22 amplicon identified in a number of human tumor types. Three genes on this amplicon, the matrix metalloproteinase gene MMP13, and the anti-apoptotic genes Birc2 (cIAP1), and Birc3 (cIAP2) show elevated expression in mouse and human osteosarcomas. We developed a functional assay using clonal osteosarcoma cell lines transduced with lentiviral shRNA vectors to show that downregulation of MMP13, Birc2, or Birc3 resulted in reduced tumor growth when transplanted into immunodeficient recipient mice. These experiments revealed that high MMP13 expression enhances osteosarcoma cell survival and that Birc2 and Birc3 also enhance cell survival, but only in osteosarcoma cells with the chromosome 9A1 amplicon. We conclude that the anti-apoptotic genes Birc2 and Birc3 are potential oncogenic drivers in the chromosome 9A1 amplicon.Requests for reprints: Lawrence A.

Amplification of the DHFR gene may occur more frequently in the presence of RB1-mediated negative regulation of its activity and can be present at clinical onset in osteosarcoma patients. Simultaneous evaluation of RFC, DHFR and RB1 gene status at the time of diagnosis may become the basis for the identification of potentially MTX-unresponsive osteosarcoma patients, who could benefit from treatment protocols with alternative antifolate drugs.

Osteosarcoma (OS) is the most common malignant bone tumor in children. To identify a plasma proteomic signature that can detect OS, we used SELDI MS to perform proteomic profiling on plasma specimens from 29 OS and 20 age-matched osteochondroma (OC) patients. Nineteen statistically significant ion peaks that were differentially expressed in OS when compared with OC patients were identified (p < 0.001 and false discovery rate < 10%). Using the proteomic profiles, we constructed a multivariate 3-nearest neighbors classifier to distinguish OS from OC patients with a sensitivity of 97% and a specificity of 80% based on external leave-one-out crossvalidation. Permutation test showed that the classification result was statistically significant (p < 0.00005). One of the proteins (m/z 11 704) in the proteomic signature was identified as serum amyloid protein A (SAA) by PMF. The higher plasma level of SAA in OS patients was further validated by Western blotting when compared to that of osteochrondroma patients and normal subjects as reference. The classifier based on this plasma proteomic signature may be useful to differentiate malignant bone cancer from benign bone tumors and for early detection of OS in high-risk individuals.