Integrated machine learning survival framework for consensus modeling in a large multicenter cohort of NSCLC resistant to aumolertinib

Patients with advanced non-small cell lung cancer (NSCLC) harboring epidermal growth factor receptor (EGFR) mutations often benefit from third-generation tyrosine kinase inhibitors (TKIs), such as aumolertinib (AUM). However, the development of drug resistance significantly limits the clinical efficacy of AUM. To address this, we established an in vitro model of AUM-resistant cell lines and performed RNA sequencing to identify resistance-associated differentially expressed genes. Using machine learning, we constructed an AUM resistance-related prognostic signature (ARRPS). Our results demonstrated that ARRPS effectively predicts the prognostic risk of patients. Notably, for patients with high ARRPS scores, the addition of CD-437 or TPCA-1 to conventional AUM treatment may help overcome drug resistance. These findings suggest that ARRPS serves as both a prognostic tool and a guide for personalized treatment strategies, potentially optimizing the clinical management of NSCLC patients.