Bridging clinical and engineering knowledge bases to optimize lung cancer models

Lung cancer remains the leading cause of cancer-related deaths worldwide. As the National Institutes of Health moves away from animal models to human-based models, microfluidics-based lung-on-a-chip technologies become increasingly promising.

However, siloed specialized knowledge hinders the development of clinically relevant models.

“Biologists often have difficulties in understanding the constantly evolving clinical needs,” said author Jian Zhou. “And bioengineers often lack the knowledge of the lung’s cellular composition and intercellular crosstalk.”

To fill these gaps, Yu et al. — a collaboration of surgeons, oncologists, bioengineers, and scientists — published a review connecting the current clinical landscape in managing lung cancer and recent advancements in microfluidic systems that capture the lung tumor microenvironment.

Recent advancements in microfluidics models use patient-derived tumor cells. These methods often fall short, precluding interactions between tumors and lung physiology that are critical to tumorigenesis — the transformation of normal cells into cancer cells.

A more comprehensive model that includes all relevant cell types would better reflect the plasticity of a cancerous lung and thus how it may develop treatment resistance. Specificity in how cell types connect to the lung cancer subtypes may increase the predictive power of models for drug testing and immunotherapy.

“This may involve patient-derived tumor cells, patient-derived stroma cells, lung-specific cells, immune cells, and mechanical cues when relevant,” said Zhou.

The team integrates first-hand insights from clinicians to detail feasible timelines for biospecimen collection and how to integrate bioengineered models into the clinical workflow.

Multidisciplinary collaborations, they report, require efficient communication, shared infrastructures, and explicit discussions on priorities. It is a two-way street; scientists must learn clinical workflows, and clinicians must learn foundational science.

Source: “Microfluidic lung cancer models: Bridging clinical treatment strategies and tumor microenvironment recapitulation,” by Zhiyun Yu, Arsalan A. Khan, Wara Naeem, Jeffrey A. Borgia, Michael J. Liptay, Christopher W. Seder, and Jian Zhou, APL Bioengineering (2025). The article can be accessed at https://doi.org/10.1063/5.0282002 .