Pooled Analysis of the Prognostic Significance of Epidermal Growth Factor Receptor (EGFR) Mutational Status in Combination with Other Driver Genomic Alterations in Stage I Resected Invasive Lung Adenocarcinoma for Recurrence-Free Survival: A Population-Based Study.

Abstract

The prognostic significance of epidermal growth factor receptor (EGFR) mutations in stage I invasive lung adenocarcinoma (LUAD) remains debated. Improving the lung cancer staging system requires further investigation into actionable mutations and their association with survival outcomes. A total of 410 patients with stage I invasive LUAD were analyzed for their driver mutations. Survival analysis of EGFR mutations, exon 19 deletion, L858R in exon 21, and minor genotypes were stratified by clinicopathologic characteristics. Kaplan-Meier and log-rank tests were used to determine prognostic significance. Univariate and multivariate Cox proportional hazard regression models assessed variables' impact on recurrence-free survival (RFS). Patients with further-profiled samples were divided into training and validation datasets by computer-generated random numbers. Multiple machine learning algorithms were applied to construct genomic prediction models, with C index evaluated for each. EGFR mutations occurred in 210 patients (51.2%). In stage I invasive LUAD, EGFR mutations strongly correlated with poor RFS (P = 0.022), especially in never smoker (P < 0.001), female (P = 0.024), part-solid (P = 0.002), and stage IA subgroups (P = 0.020). The most frequently co-mutated gene was TP53. Moreover, patients with EGFR/TP53 co-mutations, regardless of mutant types, exhibited worse prognosis. A mutational prognostic model based on the random survival forest (RSF) algorithm achieved the highest mean C index (C index: 0.87 in training cohort versus 0.74 in validation cohort), and demonstrated strong RFS estimation performance [area under the curve (AUC):1-year, 0.87, versus 3-year, 0.92, versus 5-year, 0.92]. EGFR mutations are robust biomarkers for RFS estimation in stage I invasive LUAD. Combining EGFR mutations with other actionable mutations enhances individualized RFS estimation.