Texas Children's Cancer Center, Texas Children's Hospital, and Department of Pediatrics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.
Oncol Rep. 2011 Feb;25(2):303-14. doi: 10.3892/or.2010.1111. Epub 2010 Dec 15.
Osteosarcoma is the most common malignant bone tumor that affects hundreds of children and young adults every year. The major prognostic factor in patients with localized osteosarcoma is the development of resistance towards pre-operative chemotherapy. However, modifications of post-operative chemotherapy based on the histological response have not significantly improved the outcome of patients. Thus, it would be of tremendous clinical value if the poor responders could be identified at the time of diagnosis, so that ineffective therapy can be prevented and intensified or alternative therapy could be provided to improve their outcome. We hypothesized that plasma proteomic profiles could be used to distinguish good from poor responders prior to the start of treatment. In order to test this hypothesis, we analyzed the proteomic profiles in two sets of plasma samples (n=54) from osteosarcoma patients collected before (n=27) and after (n=27) pre-operative chemotherapy. Using a linear support vector machine algorithm and external leave-one-out cross validation, we developed two classifiers that classified good and poor responders with an equal accuracy of 85% (p<0.01 after 5000 permutations) in both sets of plasma samples. In order to understand the biological basis of the classifiers, we further identified and validated two plasma proteins, serum amyloid protein A and transthyretin, in the classifiers. Our results suggest that plasma proteomic profiles can predict chemotherapy response before treatment as accurately as after treatment. Our study could lead to the development of a simple blood test that can predict chemotherapy response in osteosarcoma patients. Since the two identified proteins are involved in innate immunity, our findings are corroborated by the notion that boosting the innate immunity in conjunction with chemotherapy, achieves a better anti-tumor activity, thus improving the overall survival of osteosarcoma patients.
骨肉瘤是最常见的恶性骨肿瘤，每年影响数百名儿童和青少年。局部骨肉瘤患者的主要预后因素是对术前化疗产生耐药性。然而，基于组织学反应对术后化疗进行修改并没有显著改善患者的预后。因此，如果能够在诊断时识别出反应不佳的患者，从而防止无效治疗并加强治疗或提供替代治疗以改善其预后，将具有巨大的临床价值。我们假设在开始治疗之前，可以使用血浆蛋白质组学特征来区分反应良好和反应不佳的患者。为了验证这一假设，我们分析了两组骨肉瘤患者的血浆样本（n=54）的蛋白质组学特征，这些样本分别在术前化疗前（n=27）和化疗后（n=27）采集。使用线性支持向量机算法和外部留一法交叉验证，我们开发了两个分类器，这两个分类器在两组血浆样本中以 85%的相同准确性（经过 5000 次置换后的 p<0.01）分类了反应良好和反应不佳的患者。为了了解分类器的生物学基础，我们进一步鉴定并验证了分类器中的两种血浆蛋白，血清淀粉样蛋白 A 和转甲状腺素蛋白。我们的研究结果表明，在治疗前，血浆蛋白质组学特征可以像治疗后一样准确地预测化疗反应。我们的研究可能会导致开发出一种简单的血液测试，能够预测骨肉瘤患者的化疗反应。由于这两种鉴定出的蛋白参与固有免疫，因此我们的发现与这样一种观点相符，即与化疗联合增强固有免疫可以实现更好的抗肿瘤活性，从而提高骨肉瘤患者的总体生存率。
