title: Serum proteomic study on EGFR-TKIs target treatment for patients with NSCLC.
abstract: 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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: State Key Laboratory of Oncology in South China, Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, People's Republic of China.
authors.forename: Xuan
authors.initials: X
authors.lastname: Wu
authors.forename: Wenhua
authors.initials: W
authors.lastname: Liang
authors.forename: Xue
authors.initials: X
authors.lastname: Hou
authors.forename: Zhong
authors.initials: Z
authors.lastname: Lin
authors.forename: Hongyun
authors.initials: H
authors.lastname: Zhao
authors.forename: Yan
authors.initials: Y
authors.lastname: Huang
authors.forename: Wenfeng
authors.initials: W
authors.lastname: Fang
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Zhao
authors.forename: Jingxun
authors.initials: J
authors.lastname: Wu
authors.forename: Yunpeng
authors.initials: Y
authors.lastname: Yang
authors.forename: Chong
authors.initials: C
authors.lastname: Xue
authors.forename: Zhihuang
authors.initials: Z
authors.lastname: Hu
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.forename: Jianwei
authors.initials: J
authors.lastname: Zhang
authors.forename: Yuxiang
authors.initials: Y
authors.lastname: Ma
authors.forename: Ting
authors.initials: T
authors.lastname: Zhou
authors.forename: Tao
authors.initials: T
authors.lastname: Qin
authors.forename: Li
authors.initials: L
authors.lastname: Zhang
keywords: matrix assisted laser desorption ionization time of flight mass spectrometry; non-small-cell lung cancer; proteomic classifier; survival