This Article Full Text Full Text (PDF) Supplementary Data Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Man, T.-K. Articles by Lau, C. C. Search for Related Content PubMed PubMed Citation Articles by Man, T.-K. Articles by Lau, C. C.

Expression Profiles of Osteosarcoma That Can Predict Response to Chemotherapy

¹ Departments of Pediatrics, Texas Children's Cancer Center; Departments of ² Pathology and ³ Orthopedic Surgery, Texas Children's Hospital/Baylor College of Medicine, Houston, Texas; ⁴ Cook Children's Medical Center, Fort Worth, Texas; ⁵ Pediatric Oncology Branch, National Cancer Institute, Bethesda, Maryland; ⁶ University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma; and ⁷ Children's Hospital Los Angeles, Los Angeles, California

Requests for reprints: Ching C. Lau, Texas Children's Hospital, 6621 Fannin Street, MC 3-3320, Houston, TX 77030-2399. Phone: 832-824-4543; Fax: 832-825-4038; E-mail: cclau{at}txccc.org.

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 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.

This article has been cited by other articles:

				T. A. Guise, R. O'Keefe, R. L. Randall, and R. M. Terek Molecular Biology and Therapeutics in Musculoskeletal Oncology J. Bone Joint Surg. Am., March 1, 2009; 91(3): 724 - 732. [Full Text] [PDF]

				W. K. Kwok, M.-T. Ling, H. F. Yuen, Y.-C. Wong, and X. Wang Role of p14ARF in TWIST-mediated senescence in prostate epithelial cells Carcinogenesis, December 1, 2007; 28(12): 2467 - 2475. [Abstract] [Full Text] [PDF]

				A. Srivastava, B. Fuchs, K. Zhang, M. Ruan, C. Halder, E. Mahlum, K. Weber, M. E. Bolander, and G. Sarkar High WT1 Expression Is Associated with Very Poor Survival of Patients with Osteogenic Sarcoma Metastasis. Clin. Cancer Res., July 15, 2006; 12(14): 4237 - 4243. [Abstract] [Full Text] [PDF]