Mutation and Polymorphism in the EGFR-TK Domain Associated with Lung Cancer

EGFR Q787Q Polymorphism Is a Germline Variant and a Prognostic Factor for Lung Cancer Treated With TKIs

The prevalence and impact of epidermal growth factor receptor (EGFR) Q787Q polymorphism on the treatment of lung adenocarcinoma remains unclear. We retrospectively analyzed patients with stage IV lung adenocarcinoma to evaluate the prevalence of the EGFR Q787Q polymorphism and its influence on effects of tyrosine kinase inhibitor (TKI) treatment. A total of 333 patients were included in this study. The prevalence of the EGFR Q787Q polymorphism was 38%, 42%, and 35% in the total patients, EGFR mutation negative, and EGFR mutation positive groups, respectively. The prevalence of EGFR Q787Q polymorphism was significantly higher in EGFR wild-type patients than in the general non-cancerous population from Taiwan Biobank and 1000 Genome Project databases, respectively. EGFR Q787Q polymorphism had significant protective effects on the overall survival of EGFR-mutant lung adenocarcinoma treated with EGFR TKIs (aHR =0.61, p=0.03). Our study demonstrated that EGFR Q787Q polymorphism is a germline variant in the general population. It is a protective predictor of overall survival in patients with stage IV EGFR-mutated lung adenocarcinoma treated with TKIs.

Construction of subtype-specific prognostic gene signatures for early-stage non-small cell lung cancer using meta feature selection methods

Feature selection in the framework of meta-analyses (meta feature selection), combines meta-analysis with a feature selection process and thus allows meta-analysis feature selection across multiple datasets. In the present study, a meta feature selection procedure that fitted a multiple Cox regression model to estimate the effect size of a gene in individual studies and to identify the overall effect of the gene using a meta-analysis model was proposed. The method was used to identify prognostic gene signatures for lung adenocarcinoma and lung squamous cell carcinoma. Furthermore, redundant gene elimination (RGE) is of crucial importance during feature selection, and is also essential for a meta feature selection process. The current study demonstrated that the proposed meta feature selection procedure with RGE outperforms that without RGE in terms of predictive ability, model parsimony and biological interpretation.