Serum peptide expression and treatment responses in patients with advanced non‑small‑cell lung cancer

Abstract

Abstract. Epidermal growth factor receptor (EGFR) mutation is an important predictor for response to personalized treatments of patients with advanced non-small-cell lung cancer (NSCLC). However its usage is limited due to the difficult of obtaining tissue specimens. A novel prediction system using matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been reported to be a perspective tool in European countries to identify patients who are likely to benefit from EGFR tyrosine kinase inhibitor (TKI) treatment. In the present study, MALDI-TOF MS was used on pretreatment serum samples of patients with advanced non-small-cell lung cancer to discriminate the spectra between disease control and disease progression groups in one cohort of Chinese patients. The candidate features for classification were subsequently validated in a blinded fashion in another set of patients. The correlation between plasma EGFR mutation status and the intensities of representative spectra for classification was evaluated. A total of 103 patients that were treated with EGFR-TKIs were included. It was determined that 8 polypeptides peaks were significant different between the disease control and disease progression group. A total of 6 polypeptides were established in the classification algorithm. The sensitivity of the algorithm to predict treatment responses was 76.2% (16/21) and the specificity was 81.8% (18/22). The accuracy rate of the algorithm was 79.1% (34/43). A total of 3 polypeptides were significantly correlated with EGFR mutations (P=0.04, P=0.03 and P=0.04, respectively). The present study confirmed that MALDI-TOF MS analysis can be used to predict responses to EGFR-TKI treatment of the Asian population where the EGFR mutation status differs from the European population. Furthermore, the expression intensities of the three polypeptides in the classification model were associated with EGFR mutation. IntroductionLung cancer remains a common cause of mortalities worldwide, accounting for 1.6 million mortalities in 2012 and ~20% of all cancer mortalities (1). Non-small cell lung cancer (NSCLC) is the predominant type of the disease with ~80% of cases (1). In the last decade, the important discovery of mutations in the epidermal growth factor receptor (EGFR) gene had led to the development of targeted therapy and personalized medicine (2). One class of anti-tumor drugs that target EGFR is a group of small molecule inhibitors that inhibit the tyrosine kinase domain of EGFR, EGFR-tyrosine kinase inhibitors (TKIs). Examples of EGFR-TKIs include gefitinib and erlotinib (3,4).EGFR-TKIs have demonstrated initial success in some patients with activating mutations (5). However, numerous patients do not respond to the drug (6-8). Therefore, the identification of biomarkers that are predicative of response to the drug became a key issue for doctors to select the optimal therapy. To date, mutations in the EGFR gene (exon 19 deletion, exon 18 G719X and exon 21 L858R) have been reported to be predictors ...