【作者】 李荣； 陈剑光； 黄逸生； 杨艳； 郭爱林； 吴一龙；

【Author】 Li Rong Chen Jian-Guang Huang Yi-Sheng Yang Yan Wu Yi-Long Centre of Oncology, Guangdong Provincial People’ s Hospital, Guangzhou 510080, China

【机构】 广东省人民医院肿瘤中心；

【摘要】 目的:酪氯酸激酶抑制剂的出现开辟了肿瘤分子靶向治疗的新时代,为肺癌的治疗带来了新的选择。不过这类药物具有相对特异的目标人群,因此如何有效的预测和监测其治疗反应,将有助干临床用药的选择,具有重要的临床意义和经济价值。本研究的主要目的是比较肺癌患者服用 Tarceva前后血清蛋白指纹谱的变化,筛选可以预测疗效的血清标记物。方法:收集28例Tarceva治疗前后的血清样品, 应用弱型阳离子交换芯片(CM10)和表面增强的激光解析的飞行时间质谱进行检测,通过Ciphergen公司的Biomarker Wizard软件和Biomarker Patterns软件进行数据分析,建立预测模型。结果:治疗前后发现有12个差异表达峰,其中分子量为3932．96 Da的蛋白质在治疗获益的患者(部分缓解 +稳定)中表达明显升高,通过其建立的预测模型可以有效的区分治疗有效和无效的病人,敏感性和特异性分别为85． 1％和81．2％。结论:表面增强的激光解析的飞行时间质谱技术建立的血清蛋白指纹图预测模型在肺癌疗效预后方面提供了一条新途径, 分子量为3932．96蛋白质可能是预测 Tarceva疗效的有价值的血清标志物。更多还原

【Abstract】 Objective: The present of tyrosine kinase inhibitor bpens a new era in the treatment of tumor molecular target therapy. It provides alterative choice of patients with lung cancer. But the therapeutic effect of the drugs is limited to differential people, therefore how to predict and monitor treatment response are helpful to direct treatment decisions. This study was order to screen serum biomarkers that can be used to predict the response of patients with lung cancer before and after Tarceva therapy by comparing the changes of serum proteomic fingerprints. Methods: Proteomic fingerprints of 28 serum samples from lung cancer patients before and after Tarceva therapy were generated by the CM10 protein chip and surface enhance laser desorption/ionzition time of flight mass spectrometry (SELDI-TOF MS). A predictive model was performed using Biomarker Wizard and Biomarker Patterns software of Ciphergen company. Results: 12 differentially expressed proteins in serum were screened by analysis of proteomic fingerprints of patients before and after Tarceva therapy. The peak intensity of 3932.96Da protein was significantly higher in patients (objective response + stable disease) than in others. The predictive model based on a protein of 3932.96 Da can effectively distinguish patients achieved response from patients achieved non-response, achieving a sensitivity of 85.1 % and a specificity of 81.5%. Conclusion: The predictive model based on SELDI-TOF MS shows great potential for the prediction of treatment response and prognosis of lung cancer patients. The peak of 3932.96Da protein could be a valuable serum biomarker for the prediction of Tarceva treatment response.更多还原