Microfluidic lung cancer models: Bridging clinical treatment strategies and tumor microenvironment recapitulation

Zhiyun Yu1, Arsalan A Khan1, Wara Naeem1

  • 1Department of Cardiovascular and Thoracic Surgery, Rush University Medical Center, 1725 W Harrison Street, Chicago, Illinois 60612, USA.

APL Bioengineering
|December 19, 2025
PubMed

Insights

Lung cancer research faces challenges with traditional models. Microfluidic lung-on-a-chip technology offers a 3D, physiologically relevant platform for improved lung cancer modeling and drug development.

Area of Science:

  • Biomedical Engineering
  • Oncology
  • Translational Medicine

Background:

  • Lung cancer is a leading cause of mortality, with non-small cell lung cancer being the most common subtype.
  • Current preclinical models struggle to replicate lung complexity, hindering effective drug development.
  • Organ-on-a-chip technology presents a novel approach to overcome these limitations.

Purpose of the Study:

  • To review advancements in microfluidic systems for lung physiology and cancer modeling.
  • To explore the application of these models in disease understanding and drug testing.
  • To bridge the gap between bioengineering and clinical practice in lung cancer research.

Main Methods:

  • Review of microfluidic platforms for recapitulating lung architecture and microenvironment.
  • Integration of human-derived cells, perfusion, and mechanical cues in 3D models.
  • Incorporation of clinician insights on current lung cancer treatment and model utility.

Main Results:

  • Microfluidic lung-on-a-chip models offer enhanced accuracy in replicating lung cancer biology and drug responses.
  • These platforms facilitate the study of tumor heterogeneity and resistance mechanisms.
  • The technology shows promise for personalized oncology and improved drug development.

Conclusions:

  • Microfluidic lung-on-a-chip technology represents a significant advancement over traditional models for lung cancer research.
  • Interdisciplinary collaboration, especially with clinicians, is crucial for translating these systems into clinical practice.
  • These models hold translational potential for personalized cancer therapy and enhanced drug efficacy prediction.