Serum proteomic study on EGFR-TKIs target treatment for patients with NSCLC

被引:7
作者
Wu, Xuan [1 ]
Liang, Wenhua [1 ]
Hou, Xue [1 ]
Lin, Zhong [2 ]
Zhao, Hongyun [1 ]
Huang, Yan [1 ]
Fang, Wenfeng [1 ]
Zhao, Yuanyuan [1 ]
Wu, Jingxun [3 ]
Yang, Yunpeng [1 ]
Xue, Chong [1 ]
Hu, Zhihuang [1 ]
Zhang, Jing [1 ]
Zhang, Jianwei [1 ]
Ma, Yuxiang [1 ]
Zhou, Ting [1 ]
Qin, Tao [1 ]
Zhang, Li [1 ]
机构
[1] Sun Yat Sen Univ, Ctr Canc, Dept Med Oncol, State Key Lab Oncol South China, Guangzhou 510060, Guangdong, Peoples R China
[2] Sun Yat Sen Univ, Affiliated Hosp 5, Dept Med Oncol, Zhuhai, Peoples R China
[3] Xiamen Univ, Affiliated Hosp 1, Dept Med Oncol, Xiamen, Peoples R China
来源
ONCOTARGETS AND THERAPY | 2013年 / 6卷
关键词
non-small-cell lung cancer; matrix assisted laser desorption ionization time of flight mass spectrometry; proteomic classifier; survival; CELL LUNG-CANCER; FACTOR RECEPTOR MUTATIONS; TYROSINE KINASE INHIBITORS; CIRCULATING TUMOR DNA; MASS-SPECTROMETRY; TREATED PATIENTS; PHASE-III; GEFITINIB; ERLOTINIB; PLASMA;
D O I
10.2147/OTT.S51887
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Background: Although epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) are widely used for EGFR mutated non-small-cell lung cancer (NSCLC) patients, tumor sample availability and heterogeneity of the tumor remain challenging for physicians' selection of these patients. Here, we developed a serum proteomic classifier based on matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS) to predict the clinical outcome of patients treated with EGFR-TKIs. Method: A total of 68 patients were included in this study. All patients received EGFR-TKIs as second or third line treatment and blood samples were collected before treatment. Using magnetic bead assisted serum peptide capture coupled to MALDI-TOF-MS, pretreatment serum from 24 NSCLC patients was analyzed to develop a proteomic classifier (training set). In a blinded test set with 44 patients, each sample was classified into "good" or "poor" groups using this classifier. Survival analysis of each group was done based on this classification. Result: A 3-peptide proteomic classifier was developed from the training set. In the testing set, the classifier was able to distinguish patients of "good" or "poor" outcomes with 93% accuracy, sensitivity, and specificity. The overall survival and progression free survival of the predicted good group were found to be significantly longer than the poor group, not only in the whole population but also in certain subgroups, such as pathological adenocarcinoma and nonsmokers. With respect to the tumor samples available for EGFR mutation detection, all eight EGFR mutant tumors and three of the 12 wild type EGFR tumors were classified as good while nine of the 12 wild type EGFR tumors were classified as poor. Conclusion: The current study has shown that a proteomic classifier can predict the outcome of patients treated with EGFR-TKIs and may aid in patient selection in the absence of available tumor tissue. Further studies are necessary to confirm these findings.
引用
收藏
页码:1481 / 1491
页数:11
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