Lung Cancer Organoid Models for Studying Resistance to Anti-Angiogenic Therapy: Mechanisms and Applications.

Anti-angiogenic therapies provide clinically meaningful benefit in selected treatment settings for advanced non-small cell lung cancer, but durable responses are limited by heterogeneous and adaptive resistance. This resistance is not solely tumor-intrinsic; it emerges from interactions among tumor plasticity, compensatory angiogenic signaling, hypoxia, stromal and immune remodeling, vascular recovery, and alternative blood-supply mechanisms. These processes require different biological variables and therefore cannot be adequately interrogated by a single experimental model. Patient-derived lung cancer organoids preserve patient-specific tumor features and are useful for studying tumor-intrinsic responses, but tumor-only organoids do not reproduce perfusion-dependent vascular physiology, permeability, or drug-delivery dynamics. Microenvironment-integrated co-culture, vascularization, and microfluidic or perfusion engineering can extend these capabilities, although model complexity alone does not establish stronger evidence. This review integrates the clinical and mechanistic basis of anti-angiogenic resistance with the capabilities and limitations of current lung cancer organoid models and other three-dimensional systems. We propose a mechanism-matched framework linking the resistance mechanism to the required variable, the minimum set of model capabilities, and claim-matched readouts. Particular emphasis is placed on distinguishing structural from functional vascularization, tumor drug response from anti-angiogenic resistance, and acute treatment effects from acquired resistance. Current lung cancer organoid systems can model tumor-intrinsic and selected microenvironment-mediated responses and, increasingly, vascular responses, but direct longitudinal evidence of acquired anti-angiogenic resistance remains limited. Future progress will depend on fit-for-purpose standardization, functional qualification, longitudinal modeling, and prospective alignment with clinically observed resistance trajectories.