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Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing
Published on: October 10, 2025
Advances in translational lung cancer research in 2025: a narrative review
Yuhan Xu1, Yanbin Kuang1, Yeqin Guo2
1Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Background And Objective:
By 2025, lung cancer translational research increasingly focuses on early detection, more precise stratification, and dynamic treatment selection. Against the backdrop of drug resistance and heterogeneity as major obstacles, clinically applicable diagnostic technologies and mechanism-driven therapeutic approaches are becoming increasingly critical for improving prognosis. This review aims to summarize the major advances in translational lung cancer research reported in 2025 and to discuss their potential clinical implications.
Methods:
We searched PubMed/MEDLINE and Web of Science databases to narratively synthesize five high-impact evidence areas in 2025: (I) early detection and minimal residual disease (MRD) monitoring; (II) biomarkers for targeted therapy and immunotherapy; (III) translational advances in novel therapeutic approaches; (IV) resistance mechanisms and breakthrough strategies; and (V) preclinical models and computational tools.
Key Content And Findings:
Significant advances have been made in multimodal early detection strategies. Liquid biopsy and complementary detection methods have advanced MRD monitoring, substantially enhancing risk stratification and longitudinal surveillance capabilities. Biomarker research has expanded from single markers to multi-marker combinations, enabling more precise response prediction and treatment sequencing guidance. In therapeutic domains, novel therapies like antibody-drug conjugates (ADCs) and bispecific antibodies demonstrate significant advantages across molecular subtypes and treatment stages, while first-line and early-stage regimens continue to be optimized. Resistance studies increasingly adopt reclassification and rematching models, clarifying mechanisms through tissue and liquid biopsies to inform rational combination therapies or next-generation inhibitor development. Concurrently, patient-derived models and functional models integrated with artificial intelligence (AI) analysis pipelines further strengthen the translational evidence chain, enhancing the interpretability of clinical decisions.
Conclusions:
The 2025 therapeutic landscape highlights patient-centered precision medicine framework encompassing early detection, scalable biomarkers, rational combination therapies, and real-time resistance adaptation mechanisms, continuously advanced through sophisticated models and computational tools.
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