Microfluidic lung cancer models: Bridging clinical treatment strategies and tumor microenvironment recapitulation.
Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer accounting for a majority of cases.
APA
Yu Z, Khan AA, et al. (2025). Microfluidic lung cancer models: Bridging clinical treatment strategies and tumor microenvironment recapitulation.. APL bioengineering, 9(4), 041505. https://doi.org/10.1063/5.0282002
MLA
Yu Z, et al.. "Microfluidic lung cancer models: Bridging clinical treatment strategies and tumor microenvironment recapitulation.." APL bioengineering, vol. 9, no. 4, 2025, pp. 041505.
PMID
41415904
Abstract
Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer accounting for a majority of cases. Despite advances in targeted therapies and immunotherapy, challenges such as tumor heterogeneity, resistance mechanisms, and limited preclinical models hinder treatment efficacy. Traditional cancer models, including 2D cell cultures and animal models, often fail to accurately replicate the lung's complex architecture, microenvironment, and biomechanical cues, leading to poor predictive performance in drug development. Microfluidic-based organ-on-a-chip technology offers a promising alternative by integrating human-derived cells with precisely controlled perfusion, mechanical cues, and tumor-stroma interactions in physiologically relevant 3D models. These platforms enable the study of lung cancer biology, drug responses, and patient-specific therapeutic outcomes with improved accuracy. In this review, we discuss recent advancements in microfluidic systems for recapitulating normal lung physiology and 3D lung cancer microenvironment, covering various microfluidic platforms with applications in disease modeling and drug testing. Unlike other review articles, we bring first-hand insights from clinicians about the current treatment practice for lung cancer and the clinical utilities of lung cancer-on-a-chip models, which bioengineers have been seeking. We also highlight the translational potential of these systems in personalized oncology and the need for interdisciplinary collaborations, particularly with clinicians, to enhance their clinical impact.
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