Proteomics In Non-Small Cell Lung Cancer Patients For Prediction Of Clinical Outcome After Treatment With Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors And Chemotherapy

Backgroud Lung cancer is one of the leading causes of cancer-related death worldwide,and 80%-85%of those cases are classified as non-small cell lung cancer(NSCLC).In recent years,remarkable successes have been made in the development of epidermal growth factor receptor tyrosine kinase inhibitors(EGFR-TKIs),which can regulate the expressions of specific molecules in lung cancer cells.Recently,several studies have attempted to identify clinical and laboratory hematological biomarkers to distinguish the EGFR-TKIs resistant NSCLC patients.EGFR mutation has been reported to be a predictive marker for the EGFR-TKIs resistance in NSCLC patients.However,the acquisition of EGFR mutational profiling in every treatment stage of NSCLC cancer patients is complicate,expensive and time-consuming.Blood testing is an alternate choice for tracking the treatment progress with low cost and convenience.Matrix-assisted laser desorption/ionization-time of flight-mass spectrometry(MALDI-TOF-MS)has been developed as a new approach for the analysis of a variety of biological specimens,it can simultaneously investigate a large amounts of proteins and identify the proteomic patterns with high sensitivity,which was also been used to study the proteomic patterns in NSCLC.Obsjective This study aimed to identify potential serum biomarkers to classify advanced NSCLC patients for clinical outcome after treat with EGFR-TKIs and chemotherapy.Methods Patients were with advanced NSCLC were enrolled in our study,blood samples were collected and analyzed by using the integrated approach of MALDI-TOF-MS.Protein Chip Data Analyze System were utilized to identify the protein spectra of EGFR-TKIs/chemotherapy resistant and sensitive patients.Furthermore,support vector machine(SVM)was used to construct a predictive model with high accuracy.The model was trained using 46 samples and tested by the remaining 15 samples.Additionally,ExPASy Bioinformatics Resource Portal was used to search potential candiDAte proteins for peaks in this model.Results Among the 61 patients in the study,none had a complete response,18 patients had a partial response(29.5%);18 patients had a stable disease more than 6 months(29.5%)and 25 patients had a progressive disease or stable disease less than 6 months(41%).There were 36 patients with resistance to EGFR-TKI and 25 patients with EGFR-TKI sensitivity.Seven mass peaks 3264DA,9156DA,9172DA,3964DA,9451DA,4295DA,and 3983DA were detected from the EGFR-TKI sensitive group and the EGFR-TKI resistant group by Protein Chip DAta Analyze System.A predictive model was built by 3 protein peaks of 3264DA,9451DA,4295DA(m/z)with significantly differential expression between the target-therapy sensitive group and resistant group's serum samples.The specificity was 80%,and the sensitivity was 80.77%in the training set.In the blind test sets,9 out of 10 EGFR-TKI sensitive group samples and 4 of 5 of the EGFR-TKI resistant group were correctly sensitivity of 90%,and a specificity of 80%.Apelin was detected in the protein library as a potential candiDAte for protein peak with m/z of 3264 DA in our drug resistant group.Westem-blot also proved this hypothesis.A total of 43 patients took first-line chemotherapy,and 25 of whose chemotherapy regimen were cisplantun plus gemcitabine(GP).In GP chemotherapy sensitive group,14 patients had a partial response,in chemotherapy resistant group 2 patients had stable disease;9 patients had progressive disease.Ten mass peaks were detected from the chemotherapy sensitive group and the chemotherapy resistant group by ZJU-Protein Chip Data Analyze System.A modal was built by 7 protein peaks of 4978DA,2999DA,4996DA,2627DA,4188DA and 1458DA(m/z)with significantly differential expression between chemotherapy sensitive group and resistant group's serum samples.Conclusions MALDI-TOF-MS might be a method in the search for predictive serum biomarkers for EGFR-TKI resistance in advanced NSCLC patients.Apelin might be a predict factor for the resistance of EGFR-TKI for NSCNC.The predictive model for chemotherapy resistance in advanced NSCLC patients may be further verified.