[show abstract][hide abstract] ABSTRACT: Osteosarcoma is the most common malignant bone tumor in children. Despite the advent of chemotherapy, the survival of osteosarcoma patients has not been significantly improved recently. Chemokines are a group of signaling molecules that have been implicated in tumorigenesis and metastasis.
The authors used an antibody microarray to identify chemokines that were elevated in the plasma samples of osteosarcoma patients. The results were validated using enzyme-linked immunosorbent assays on an independent set of samples. The tumor expressions of 3 chemokines were examined in 2 sets of osteosarcoma tissue arrays. The authors also evaluated the proliferative effect of the chemokines in 4 osteosarcoma cell lines.
The authors found that the plasma levels of CXCL4, CXCL6, and CXCL12 in the osteosarcoma patients were significantly higher than those in the controls, and the results were validated by an independent osteosarcoma cohort (P < .05). However, CXCL4 (100%) and CXCL6 (91%) were frequently expressed in osteosarcoma, whereas CXCL12 was only expressed in 4%. Survival analysis further showed that higher circulating levels of CXCL4 and CXCL6, but not CXCL12, were associated with a poorer outcome of osteosarcoma patients. Addition of exogenous chemokines significantly promoted the growth of different osteosarcoma cells (P < .05).
The results demonstrate that CXCL4 and CXCL6 are frequently expressed in osteosarcoma, and that the plasma levels of these 2 chemokines are associated with patient outcomes. Further study of these circulating chemokines may provide a promising approach for prognostication of osteosarcoma. Targeting these chemokines or their receptors may also lead to a novel therapeutic invention.
Cancer 01/2011; 117(1):207-17. · 5.20 Impact Factor
[show abstract][hide abstract] ABSTRACT: 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.
[show abstract][hide abstract] ABSTRACT: 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 > or = 90% 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 Research 10/2005; 65(18):8142-50. · 8.65 Impact Factor