Lung-molGPA may stratify the prognostic impact of TP53 co-mutation in EGFR-mutant lung adenocarcinoma with brain metastases: a multi-center retrospective analysis
Guangchuan Deng, Yanxin Zhang, Jing Fan, Jiang Yuanzhu, Chenran Zhao, Jiamao Lin, Yuan Peng, Zhenxiang Li, Zhenzhou YangBackground:
The optimal approach to treatment intensification for epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma with brain metastases remains a topic of debate, especially in the context of high-risk molecular features such as TP53 co-mutation. The extent to which baseline clinical risk influences treatment efficacy is yet to be determined.
Objectives:
We aim to systematically evaluate the prognostic significance of EGFR/TP53 co-mutations in treatment-naïve patients with lung adenocarcinoma and newly diagnosed brain metastases.
Design:
This study enrolled 218 treatment-naïve patients with EGFR-mutant lung adenocarcinoma and brain metastases who received first-line third-generation EGFR-tyrosine kinase inhibitors (TKIs). Treatment effects were evaluated using interaction models stratified by Lung-molGPA scores. Stratification: Group A (Lung-molGPA 1–2) versus Group B (Lung-molGPA 2.5–4).
Methods:
Within each group, the influence of TP53 co-mutations on survival was analyzed. Additionally, among patients with TP53 co-mutations, the effects of cranial radiotherapy (CRT) and treatment with either third-generation EGFR-TKIs monotherapy or third-generation EGFR-TKIs combined with chemotherapy on overall survival (OS) were further investigated, taking into account the Lung-molGPA scores.
Results:
The Lung-molGPA significantly influenced the prognostic impact of TP53 mutations and the survival benefits of treatment intensification strategies (log-rank test
Conclusion:
Baseline clinical risk, as delineated by the Lung-molGPA, serves as a crucial determinant of therapeutic benefit in cases of EGFR-mutant lung adenocarcinoma with cerebral metastases. Implementing risk-adapted treatment intensification strategies could potentially prevent overtreatment in patients classified as low-risk, while simultaneously optimizing clinical outcomes in high-risk cohorts.