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

Xuan Wu,^1,* Wenhua Liang,^1,* Xue Hou,^1,* Zhong Lin,² Hongyun Zhao,¹ Yan Huang,¹ Wenfeng Fang,¹ Yuanyuan Zhao,¹ Jingxun Wu,³ Yunpeng Yang,¹ Chong Xue,¹ Zhihuang Hu,¹ Jing Zhang,¹ Jianwei Zhang,¹ Yuxiang Ma,¹ Ting Zhou,¹ Tao Qin,¹ Li Zhang<sup>1

1</sup>State Key Laboratory of Oncology in South China, Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, People’s Republic of China; ²Department of Medical Oncology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, People’s Republic of China; ³Department of Medical Oncology, The First Affiliated Hospital of Xiamen University, Xiamen, People’s Republic of China

*These authors contributed equally to this article

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.

Keywords: non-small-cell lung cancer, matrix assisted laser desorption ionization time of flight mass spectrometry, proteomic classifier, survival