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Application of organoid models in basic and translational research of lung cancer: a narrative review
Yulong Jin1,2, Hui Li1,3, Chenchen Tang1,2
1Jilin Provincial Key Laboratory of Molecular Diagnosis and Treatment for Malignant Tumor, Jilin Cancer Hospital, Changchun, China.
Background And Objective:
Lung cancer remains the leading cause of cancer-related mortality worldwide. Despite advances in targeted and immunotherapy, challenges such as drug resistance persist. Traditional experimental models often fail to recapitulate human tumor complexity, limiting translational success. Patient-derived organoids (PDOs), which retain key characteristics of original tumors, have emerged as powerful tools. This review aims to systematically examine the methodologies, characterizations, and applications of lung cancer organoids (LCOs) in both basic and translational research.
Methods:
A narrative review was conducted based on literature retrieved from PubMed, Scopus, and Web of Science up to March 2026. Search terms included "lung cancer organoid/LCO", "patient-derived organoid/PDO", "three-dimensional (3D) culture", "2.5-dimensional (2.5D) culture", "drug susceptibility testing", and "precision medicine". Studies focusing on culture techniques, molecular characterization, and preclinical/clinical applications were included.
Key Content And Findings:
This study summarizes current LCO culture systems, comparing conventional 3D platforms and emerging 2.5D systems that offer significant advantages in cost, operational simplicity, and imaging convenience for rapid clinical applications. We detail multi-dimensional characterization approaches (morphological, molecular, functional) and discuss critical quality control standards. LCOs have been instrumental in studying tumorigenesis mechanisms, signaling pathways, and metabolic reprogramming. In translational research, LCOs show high predictive value for drug susceptibility to targeted agents, chemotherapy, and immunotherapy, and serve as platforms for developing novel therapeutics such as antibody-drug conjugates (ADCs).
Conclusions:
LCO models faithfully recapitulate tumor heterogeneity and are increasingly integral to precision medicine and drug development. While challenges remain in vascularization, immune microenvironment reconstitution, and standardization, ongoing integration with bioengineering and artificial intelligence (AI) promises to enhance their translational utility. This review underscores the potential of LCOs to bridge basic research and clinical practice, accelerating personalized therapeutic strategies in lung cancer.
Insights
Lung cancer organoids (LCOs) offer a powerful model for understanding cancer and testing treatments. These patient-derived organoids accurately reflect tumor complexity, aiding precision medicine and drug development for lung cancer.
Area of Science:
- Biomedical research
- Cancer biology
- Translational oncology
Background:
- Lung cancer is a leading cause of cancer mortality globally.
- Drug resistance and limitations of traditional models hinder progress.
- Patient-derived organoids (PDOs) offer a promising alternative for studying tumor complexity.
Purpose of the Study:
- To systematically review methodologies, characterizations, and applications of lung cancer organoids (LCOs).
- To examine LCOs in both basic and translational lung cancer research.
- To highlight LCOs' role in advancing precision medicine and drug development.
Main Methods:
- A narrative review of literature from PubMed, Scopus, and Web of Science up to March 2026.
- Inclusion of studies on LCO culture techniques, molecular characterization, and applications.
- Focus on three-dimensional (3D) and 2.5-dimensional (2.5D) culture systems.
Main Results:
- Comparison of conventional 3D and emerging 2.5D LCO systems, noting advantages of 2.5D for clinical applications.
- Detailed multi-dimensional characterization (morphological, molecular, functional) and quality control standards.
- Demonstrated utility of LCOs in studying tumorigenesis, signaling pathways, and drug susceptibility for targeted agents, chemotherapy, and immunotherapy.
Conclusions:
- LCO models recapitulate tumor heterogeneity and are vital for precision medicine and drug development.
- Challenges in vascularization, immune microenvironment, and standardization are being addressed.
- Integration with bioengineering and AI will enhance LCO translational utility for personalized lung cancer therapies.
