Next-generation lung-cancer-on-a-chip: Toward personalized therapy, AI, and CRISPR-driven models.

Area of Science:

• Biomedical Engineering
• Oncology
• Genomics

Background:

• Lung-cancer-on-a-chip (LCOC) technologies are advancing but often study components like mechanical strain, patient-derived tumors, multi-organ interactions, AI analytics, and CRISPR editing in isolation.
• Existing models lack a unified approach to integrate these critical factors for comprehensive lung cancer modeling.

Purpose of the Study:

• To present a unified framework for next-generation lung-cancer-on-a-chip (LCOC) architectures by integrating emerging technologies.
• To demonstrate how this integrated approach can enable personalized prediction of lung cancer progression, metastasis, and drug response.

Main Methods:

• Embedding patient-derived lung tumor fragments into cyclically stretched microenvironments within a breathing LCOC model.
• Linking the LCOC to downstream organ compartments to map metastatic routes under physiological mechanics.
• Utilizing continuous high-resolution imaging to feed AI pipelines for automated drug-response prediction and metastatic trajectory simulation.
• Incorporating on-chip CRISPR editing for investigating metastatic drivers in dynamic, strain-modulated microenvironments.

Main Results:

• The integrated LCOC framework enables patient-specific mapping of metastatic routes under physiologically relevant mechanical strain.
• AI-driven analysis of high-resolution imaging allows for automated drug-response prediction and metastatic trajectory simulation.
• On-chip CRISPR editing facilitates accurate investigation of metastatic drivers within dynamic microenvironments.

Conclusions:

• A next-generation, personalized multi-organ-on-chip architecture can be developed by synthesizing LCOC, AI, and CRISPR technologies.
• This integrated platform has the potential to predict individual lung cancer disease progression and treatment outcomes without direct patient risk.
• Addressing practical barriers such as tumor fragility, imaging domain shift, and gene-editing delivery is crucial for clinical translation.