Lung cancer organoids for functional precision oncology: from disease modeling to clinical decision support

Lung cancer is characterized by extensive heterogeneity and the frequent emergence of acquired resistance, posing major obstacles for successful therapy. Traditional preclinical models, such as two-dimensional (2D) cell cultures and animal models, either lack physiological relevance or require timelines that are poorly coordinated with clinical needs. Lung cancer organoids (LCOs) have been recognized as a promising in vitro three-dimensional (3D) platform that retain key features of original tumors, including histological architecture, genetic alterations, and intratumoral heterogeneity. In this review, we summarize recent developments in the area from the perspective of functional precision oncology. We first consider why LCOs are especially valuable, yet technically difficult, in lung cancer, with special focus on sample source, culture bias, contamination by normal airway epithelium, and authentication approaches. We then outline the functional applications of LCOs in recapitulating tumor initiation, evolutionary plasticity, treatment-induced adaptation, immune interactions, drug screening, and biobank construction. Building on these applications, we underscore the critical translational bottlenecks that still constrain routine clinical implementation, such as model fidelity, lack of standardization, temporal restrictions, and inadequate microenvironmental complexity. Lastly, we explore how microfluidic systems, biomimetic matrices, 3D bioprinting, and artificial intelligence (AI)-assisted analytics could help convert LCOs from research models into clinically implementable decision-support platforms. Collectively, these advances place LCOs as a core part of emerging functional precision oncology in lung cancer.