Effect of epidermal growth factor receptor gene mutation on the prognosis of pathological stage II-IIIA (8th edition TNM classification) primary lung cancer after curative surgery

Objectives: This retrospective study aimed to elucidate the effect of epidermal growth factor receptor (EGFR) gene mutations on the prognosis of patients with pathological stage II-IIIA primary lung cancer after curative surgery. Materials and methods: We enrolled 539 patients with p-stage II-IIIA (8th edition tumor-node-metastasis [TNM] classification) lung cancer who underwent curative resection at Kanagawa Cancer Center between January 2010 and December 2020 and whose tumors were tested for EGFR mutations. Relapse-free survival (RFS) and overall survival (OS) of patients with EGFR-mutant lung cancer (Mt, n = 126) including EGFR exon 21 L858R point mutation and EGFR exon 19 deletion mutation and EGFR mutation-wild lung cancer (Wt, n = 413) were analyzed using Kaplan-Meier curves and compared using a log-rank test. Cox regression analysis was performed to evaluate the effects of EGFR gene mutations on RFS and OS at each stage. Results: There were 56/256 patients with p-stage II EGFR-Mt/Wt and 70/157 patients with p-stage IIIA EGFR-Mt/Wt. The 5-year RFS rate of patients with EGFR-Mt/Wt was 46.6%/52.0% (p = 0.787) for p-stage II and 17.4%/29.7% (p = 0.929) for p-stage IIIA. The 5-year OS rate was 92.0%/65.7% (p = 0.001) for p-stage II and 56.0%/39.3% (p = 0.016) for p-stage IIIA. EGFR-Mt was not an independent prognostic factor for OS of patients with p-stage IIIA lung cancer (hazard ratio [HR], 0.95; 95% confidence interval [CI], 0.51-1.76; p = 0.872); however, EGFR-Mt was an independent favorable prognostic factor for OS of patients with p-stage II lung cancer (HR, 0.59; 95% CI, 0.36-0.96; p = 0.034). Conclusion: The OS of lung cancer patients with p-stage II or IIIA, classified according to the 8th edition TNM classification, was remarkably favorable. Incorporating EGFR mutations to the anatomical TNM classification may lead to a more accurate prognosis prediction.