Huang Yufei, Zeng Hui, Zhang Guochao, Ren Fangzhou, Yuan Zhenlong, Ren Jingyu, Xu Jiaxi, Song Zehao, Li Wenbin, Ying Jianming, Feng Feiyue, Tan Fengwei
Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Immunology and National Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Ann Surg Oncol. 2025 Feb;32(2):760-770. doi: 10.1245/s10434-024-16528-7. Epub 2024 Nov 25.
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.
表皮生长因子受体（EGFR）突变在Ⅰ期浸润性肺腺癌（LUAD）中的预后意义仍存在争议。改进肺癌分期系统需要进一步研究可操作的突变及其与生存结果的关联。
共分析了410例Ⅰ期浸润性LUAD患者的驱动基因突变。根据临床病理特征对EGFR突变、第19外显子缺失、第21外显子L858R以及次要基因型进行生存分析。采用Kaplan-Meier法和对数秩检验确定预后意义。单因素和多因素Cox比例风险回归模型评估变量对无复发生存期（RFS）的影响。对有进一步分析样本的患者，通过计算机生成的随机数分为训练数据集和验证数据集。应用多种机器学习算法构建基因组预测模型，并对每个模型评估C指数。
210例患者（51.2%）发生EGFR突变。在Ⅰ期浸润性LUAD中，EGFR突变与较差的RFS密切相关（P = 0.022），尤其是在从不吸烟者（P < 0.001）、女性（P = 0.024）、部分实性（P = 0.002）和ⅠA期亚组（P = 0.020）中。最常共同突变的基因是TP53。此外，EGFR/TP53共同突变的患者，无论突变类型如何，预后均较差。基于随机生存森林（RSF）算法的突变预后模型获得了最高的平均C指数（训练队列中C指数为0.87，验证队列中为0.74），并表现出较强的RFS估计性能[曲线下面积（AUC）：1年为0.87，3年为0.92，5年为0.92]。
EGFR突变是Ⅰ期浸润性LUAD中RFS估计的可靠生物标志物。将EGFR突变与其他可操作的突变相结合可增强个性化的RFS估计。
