Factor Receptor Gene Mutation Status Using Serum

Objectives 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, obtaining tumor tissue for mutation analysis is challenging. Here, we aimed to de-tect serum peptides/proteins associated with EGFR gene mutation status, and test whether a classification algorithm based on serum proteomic profiling could be developed to analyze EGFR gene mutation status to aid therapeutic decision-making. Patients and Methods Serum collected from 223 stage IIIB or IV NSCLC patients with known EGFR gene mutation status in their tumors prior to therapy was analyzed by matrix-assisted laser desorption/ioni-zation time-of-flight mass spectrometry (MALDI-TOF-MS) and ClinProTools software. Dif-ferences in serum peptides/proteins between patients with EGFR gene TKI-sensitive mutations and wild-type EGFR genes were detected in a training group of 100 patients; based on this analysis, a serum proteomic classification algorithm was developed to classify EGFR gene mutation status and tested in an independent validation group of 123 patients. The correlation between EGFR gene mutation status, as identified with the serum proteomic classifier and response to EGFR-TKIs was analyzed. Results Nine peptide/protein peaks were significantly different between NSCLC patients with EGFR gene TKI-sensitive mutations and wild-type EGFR genes in the training group. A genetic al-gorithm model consisting of five peptides/proteins (m/z 4092.4, 4585.05, 1365.1, 4643.49 and 4438.43) was developed from the training group to separate patients with EGFR gen