| Epidermal growth factor receptor(EGFR) gene mutations in tumors predict tumor response to EGFR tyrosine kinase inhibitors(EGFR-TKIs) in non-small-cell lung cancer(NSCLC). However, in some cases, tumor tissue either is inadequate for molecular testing because of its small quantity or very low tumor content or is not readily available. Furthermore,the methods used to assess EGFR gene mutation status in plasma or serum samples are not approved by the current guidelines. Thus, other sensitive and noninvasive approaches for evaluating EGFR gene mutation status using surrogate tumor tissues to predict EGFR-TKI efficacy are still needed. Recently, peptide mass fingerprinting based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry(MALDI-TOF-MS) has been widely used to detect diagnostic, prognostic, and predictive proteomic biomarkers. No studies have been found on the analysis of EGFR gene mutation status by developing a classification algorithm based on serum proteomic profiling using peptide mass fingerprinting.Part 1 Clinical features and response to EGFR-TKIs of advanced non-small-cell lung cancer in patients with EGFR gene mutationObjectives To discuss the clinical features of advanced non-small-cell lung cancer in patients with EGFR gene mutation, and to analyze the correlation between EGFR gene mutation status and response to EGFR-TKIs in patients with advanced Non-Small-Cell Lung Cancer.Objectives To discuss the clinical features of advanced non-small-cell lung cancer in patients with EGFR gene mutation, and to analyze the correlation between EGFR gene mutation status and response to EGFR-TKIs in patients with advanced Non-Small-Cell Lung Cancer.Methods A total of 352 stage IIIB or IV NSCLC patients with known EGFR gene mutation status in their tumors who were diagnosed at 307 Hospital between May 2011 andApril 2014 were enrolled in this study. The clinical data, imaging data and follow-up data of these 352 patients were reviewed.Results Among the patients enrolled in this study, there were no significant differences between patients with EGFR gene TKI-sensitive mutations and wild-type EGFR genes with respect to age, histologic type, or disease stage, but differences in sex and smoking history were observed between these two arms, with more females and more non-smokers in patients with EGFR gene TKI-sensitive mutations.Of 352 patients with advanced NSCLC enrolled in this study, 250 had measurable tumors and received EGFR-TKI treatment. Among these 250 patients, 93(58.1%) of 160 patients with EGFR gene TKI-sensitive mutations and 9(10.0%) of 90 patients with wild-type EGFR genes exhibited an objective response(p<0.0001). Disease control was noted in 136(83.8%) of 160 patients with EGFR gene TKI-sensitive mutations and 32(35.6%) of 90 patients with wild-type EGFR genes(p<0.0001). The median PFS time for patients with EGFR gene TKI-sensitive mutations and wild-type EGFR genes were 10.0 months(95% CI, 9.0 to 10.9) and 2.0 months(95% CI, 1.8 to 2.1), respectively. Patients with EGFR gene TKI-sensitive mutations had a significantly longer PFS than patients with wild-type EGFR genes(p < 0.001, log-rank test). Patients with EGFR gene TKI-sensitive mutations had an OS time of 29.0 months(95% CI, 27.4 to 30.6) compared with 28.0 months(95% CI, 25.4 to 30.5) for the patients with wild-type EGFR genes. There was no significant difference in OS between the two groups(p=0.657, log-rank test).Conclusion The incidence of EGFR gene TKI-sensitive mutations is associated with clinical background, such as race, sex, smoking history and pathological type, with more Asians, more females and more non-smokers and more adenocarcinma in patients with EGFR gene TKI-sensitive mutations.EGFR-TKI sensitivity has been associated with activating mutations in the kinase domain of the EGFR gene. Patients with these EGFR gene TKI-sensitive mutations have a significantly better response to EGFR-TKIs, whereas those with wild-type EGFR genes exhibit a worse tumor response.Part 2 Development and validation of classification algorithm based on serum proteomic profiling to analyze EGFR gene mutation status in patients with advanced and metastatic non–small-cell lung cancerObjectives To detect serum peptides/proteins associated with EGFR gene mutation status using peptide mass fingerprinting, and to test whether a classification algorithm based on serum proteomic profiling could be developed to analyze EGFR gene mutation status to aid therapeutic decision-making.Methods Stage IIIB or IV NSCLC patients with known EGFR gene mutation status in their tumors prior to therapy were enrolled in this study. Fifty patients were randomly selected from patients with EGFR gene TKI-sensitive mutations and wild-type EGFR genes respectively(a total of 100 patients) to form the training group, and the remaining patients formed the validation group. Serum collected prior to therapy was analyzed by peptide mass fingerprinting using MALDI-TOF-MS and Clin Pro Tools software. Differences in serum peptides/proteins between patients with EGFR gene TKI-sensitive mutations and wild-type EGFR genes were detected in the training group; based on this analysis, a serum proteomic classification algorithm was developed to classify EGFR gene mutation status and tested in the validation group. The correlation between EGFR gene mutation status, as identified with the serum proteomic classifier and response to EGFR-TKIs was analyzed.Results A total of 316 patients met the enrollment criteria and were enrolled in this study. Based on the criterion of amplification refractory mutation system(ARMS) in tumors, there were 144 patients with EGFR gene TKI-sensitive mutations and 172 patients with wild-type EGFR genes. Among 223 patients enrolled in this study between May 2011 and April 2013, 50 were randomly selected from those with EGFR gene TKI-sensitive mutations and from those with wild-type EGFR genes(i.e., a total of 100 patients) to form the training group, and the remaining 123 patients enrolled during this time(i.e., 52 patients with EGFR gene TKI-sensitive mutations and 71 with wild-type EGFR genes) formed the validation group-1. And another 93 patients enrolled in this study between May 2013 and April 2014(i.e., 42 patients with EGFR gene TKI-sensitive mutations and 51 with wild-type EGFR genes) formed the validation group-2.A total of 129 peptide peaks were identified in the spectra of the training group dataset generated by MALDI-TOF-MS, and 9 peaks(with m/z 1365.1, 1866.47, 3315.75, 3883.79, 3956.66, 4092.4, 4585.05, 4643.49, and 5866.96) were significantly different(p<0.05) between the patients with EGFR gene TKI-sensitive mutations and patients with wild-type EGFR genes.Built-in mathematical models in Clin Pro Tools 2.1 were applied for classification model construction using spectral data from the training group generated by MALDI-TOF-MS, and a genetic algorithm model named Model GA-7, which was composed of five peptide peaks with m/z 4092.4, 4585.05, 1365.1, 4643.49 and 4438.43, exhibited the best efficiency in separating samples from patients with EGFR gene TKI-sensitive mutations and samples from patients with wild-type EGFR genes. The Model GA-7 was validated in the independent validation group-1 of 123 NSCLC patients in a blinded test, with a sensitivity of 84.6% and a specificity of 77.5%, which indicated a high consistency between ARMS in tumors and the serum proteomic classifier in evaluating EGFR gene mutation status(P<0.001; Kappa value, 0.648). The Model GA-7 was then validated in the independent validation group-2 of 93 NSCLC patients in a blinded test, with a sensitivity of 84.6% and a specificity of 77.5%, which indicated a high consistency between ARMS in tumors and the serum proteomic classifier in evaluating EGFR gene mutation status(P<0.001; Kappa value, 0. 715).In the validation group-1, 81 patients had measurable tumors and received EGFR-TKI treatment. Patients whose matched samples were labeled as “mutant” and “wild” by the classifier exhibited different tumor responses to EGFR-TKIs. Twenty-eight(59.6%) of 47 patients whose matched samples were labeled as “mutant” by the classifier and 3(8.8%) of 34 patients whose matched samples were labeled as “wild” by the classifier exhibited an objective response(p<0.0001). Disease control was noted in 41(87.2%) of 47 patients whose matched samples were labeled as “mutant” by the classifier and 12(35.3%) of 34 patients whose matched samples were labeled as “wild” by the classifier(p<0.0001). The median PFS time for patients whose matched samples were labeled as “mutant” and “wild” by the classifier were 10.0 months(95% CI, 9.0 to 10.9) and 2.3 months(95% CI, 1.9 to 2.7), respectively. Patients whose matched samples were labeled as “mutant” by the classifier had a significantly longer PFS than patients whose matched samples were labeled as “wild” by the classifier(p=0.001, log-rank test). Patients whose matched samples were labeled as “mutant” by the classifier had an OS time of 29.0 months(95% CI, 25.2 to 32.8) compared with 28.0 months(95% CI, 17.7 to 38.3) for the patients whose matched samples were labeled as “wild-type” by the classifier. There was no significant difference in OS between the two groups(p=0.441, log-rank test).In the validation group-2, 64 patients had measurable tumors and received EGFR-TKI treatment. Patients whose matched samples were labeled as “mutant” and “wild” by the classifier exhibited different tumor responses to EGFR-TKIs. Twenty-four(60.0%) of 40 patients whose matched samples were labeled as “mutant” by the classifier and 2(8.3%) of 24 patients whose matched samples were labeled as “wild” by the classifier exhibited an objective response(p<0.0001). Disease control was noted in 35(87.5%) of 40 patients whose matched samples were labeled as “mutant” by the classifier and 7(29.2%) of 24 patients whose matched samples were labeled as “wild” by the classifier(p<0.0001). The median PFS time for patients whose matched samples were labeled as “mutant” and “wild” by the classifier were 11.0 months(95% CI, 9.5 to 12.9) and 2.0 months(95% CI, 1.8 to 2.2), respectively. Patients whose matched samples were labeled as “mutant” by the classifier had a significantly longer PFS than patients whose matched samples were labeled as “wild” by the classifier(p<0.001, log-rank test). Patients whose matched samples were labeled as “mutant” by the classifier had an OS time of 28.0 months(95% CI, 25.3 to 30.7) compared with 24.0 months(95% CI, 19.2 to 28.8) for the patients whose matched samples were labeled as “wild-type” by the classifier. There was no significant difference in OS between the two groups(p=0.280, log-rank test).Conclusion It could detect differences in serum peptides/proteins between NSCLC patients with EGFR gene TKI-sensitive mutations and NSCLC patients with wild-type EGFR genes applying peptide mass fingerprinting using MALDI-TOF-MS coupled with Clin Pro Tools software to analyze serum from NSCLC patients with a known EGFR gene mutation status(i.e., determined by amplification refractory mutation system [ARMS] in tumor tissue). Based on these differences, a classification algorithm was developed for the analysis of EGFR gene mutation status. Classification of EGFR gene mutation status using the serum proteomic classifier established in the present study in patients with stage IIIB or IV NSCLC is feasible and may predict tumor response to EGFR-TKIs.Part 3Identification of serum biomarkers for EGFR gene mutation status in patients with advanced NSCLCObjectives To identify the candidate serum biomarkers for EGFR gene mutation status in patients with advanced NSCLC.Methods Ten patients with highly expressed interesting peptides were selected based on the spectrums obtained from serum peptide analysis using MALDI-TOF-MS in Part 2. Five ml of eluted fractions from each these patients in Part 2 were collected, pooled, and lyophilized for MALDI-TOF-MS. The interesting peptide peaks present in each MS scan were selected and analyzed by MALDI-TOF/TOF- MS/MS. Fragment ion spectra from TOF/TOF analyses were transformed into a peak list using the Flex Analysis software. Peptide identifications were made by database comparison with a human database using the MASCOT search program online.Results The three positively identified candidate peptides, with m/z of 1365.1、1866.47 '3883.79, were Homer protein homolog 3, Mesogenin-1and REM2- and Rab-like small GTPase 1, respectively.Conclusion Identification of serum biomarkers using MB-IMAC-Cu2+ combined with MALDI-TOF/TOF-MS is feasible. Homer protein homolog 3, Mesogenin-1and REM2- and Rab-like small GTPase 1 may be associated with the response to EGFR-TKIs in patients with advanced NSCLC. |
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