) Osteosarcoma is the most common malignant bone tumor in children. Approximately 80 percent of patients present with non-metastatic disease. After the diagnosis is made by a biopsy, treatment involves 3-4 courses of neoadjuvant chemotherapy before definitive surgery, followed by post-operative chemotherapy. With currently available treatment, approximately 30-40 percent of patients with non-metastatic disease relapse after therapy. There is no prognostic factor that can be used at the time of diagnosis to predict which patients will have a high risk of relapse. The only significant prognostic factor predicting the outcome in a patient with non-metastatic osteosarcoma is the histopathologic response of the primary tumor resected at the time of definitive surgery. The degree of necrosis in the primary tumor is a reflection of the tumor response to neoadjuvant chemotherapy. Higher degree of necrosis is associated with lower risk of relapse and therefore better outcome. Patients with lower degree of necrosis have a much higher risk of relapse and poor outcome even after complete resection of the primary tumor. Unfortunately this poor outcome cannot be altered despite modification of post-operative chemotherapy to account for the resistance of the primary tumor to neoadjuvant chemotherapy. Thus there is an urgent need to identify prognostic factors that can be used at the time of diagnosis to recognize the subtypes of osteosarcomas patients that have high risk of relapse so that more appropriate chemotherapy can be used at the outset to improve the outcome. We propose to establish a molecular classification system to distinguish such subsets of osteosarcoma based on their gene expression profiles. This project will be a collaboration among several institutions including the Texas Children's Cancer Center, Baylor College of Medicine, Pediatric Oncology Branch, NCI, Cancer Genetics Branch, NHGRI, Biometric Research Branch, NCI and Incyte Pharmaceuticals, Inc. We plan to recruit 100 osteosarcoma patients who are receiving the same therapy through a treatment protocol. Using cDNA microarrays, we will investigate the gene expression profiles of the tumor tissues at the time of biopsy and definitive surgery. These profiles will be correlated with clinical outcome. In addition, we also plan to compare the gene expression profiles of the primary tumor and those of the metastatic lesions.
The specific aims are: 1. To validate and optimize cDNA microarray technology for gene expression profiling of clinical specimens. 2. To establish the relevant gene expression profiles for molecular classification of osteosarcoma by correlating these profiles with clinical outcome, chemosensitivity, and metastatic potential.
