Comprehensive non-small cell lung cancer targets: From computational prediction to clinical breakthroughs in overcoming drug resistance

Abstract

Non-small cell lung cancer (NSCLC), the predominant subtype of lung cancer, remains a leading contributor to global cancer-related mortality. Conventional treatments—surgery, chemotherapy, and radiotherapy—are often limited by suboptimal efficacy and substantial toxicity, underscoring the urgent need for more effective targeted therapies. This study provides a comprehensive overview of three key advancements in NSCLC research. First, it highlights state-of-the-art target prediction methodologies that integrate ligand-based, structure-based, and multi-feature deep learning models, supported by experimental validation. Second, it examines clinical progress in targeting classical oncogenic drivers, exemplified by the fourth-generation tyrosine kinase inhibitor amivantamab against epidermal growth factor receptor (EGFR), and explores mechanisms of drug resistance, such as T790M and C797S mutations, along with emerging strategies like synthetic lethality-based interventions. Third, it discusses combination regimens—such as osimertinib co-administered with savolitinib—that mitigate resistance by synergistically inhibiting compensatory signaling pathways, thereby enhancing clinical outcomes. Future research priorities include the design of multi-target therapeutics and the refinement of AI-driven target discovery frameworks. This review addresses current limitations in targeted NSCLC therapy and offers insights to guide future therapeutic development.