Worldwide Prevalence of Epidermal Growth Factor Receptor Mutations in Non-Small Cell Lung Cancer: A Meta-Analysis

Identification of variable epidermal growth factor receptor (EGFR) gene mutations in non-small cell lung cancer (NSCLC) is important for the selection of appropriate targeted therapies. This meta-analysis was conducted to provide a worldwide overview of EGFR mutation and submutation (specifically exon 19 deletions, exon 21 L858R substitutions, and others) prevalence, and identify important covariates that influence EGFR mutation status in patients with advanced NSCLC to address this clinical data gap.

Embase^® and MEDLINE^® in Ovid were searched for studies published between 2004 and 2019 with cohorts of ≥ 50 adults with EGFR mutations, focusing on stage III/IV NSCLC (≤ 20% of patients with stage I/II NSCLC). Linear mixed-effects models were fitted to EGFR mutation endpoints using logistic transformation (logit), assuming a binomial distribution. The model included terms for an intercept reflecting European studies and further additive terms for other continents. EGFR submutations examined were exon 19 deletions, exon 21 L858R substitutions, and others.

Of 3969 abstracts screened, 57 studies were included in the overall EGFR mutation analysis and 74 were included in the submutation analysis relative to the overall EGFR mutation population (Europe, n = 12; Asia, n = 51; North America, n = 5; Central America, n = 1; South America, n = 1; Oceania, n = 1; Global, n = 3). The final overall EGFR mutations model estimated Asian and European prevalence of 49.1% and 12.8%, respectively, and included an additive covariate for the proportion of male patients in a study. There were no significant covariates in the submutation analyses. Most submutations were actionable: exon 19 deletions (49.2% [Asia]; 48.4% [Europe]); exon 21 L858R substitutions (41.1% [Asia]; 29.9% [Europe]).

Although EGFR mutation prevalence was higher in Asian than Western countries, data support worldwide testing for EGFR overall and submutations to inform appropriate targeted treatment decisions.