Preclinical Models for Functional Precision Lung Cancer Research.

Area of Science:

• Oncology
• Translational Research
• Cancer Biology

Background:

• Patient-centered precision oncology aims to tailor cancer care.
• Lung cancer research heavily relies on preclinical models and technological advancements.
• Traditional cell lines lack tumor heterogeneity and stromal interactions crucial for predicting patient outcomes.

Purpose of the Study:

• To review the role of preclinical models in advancing precision oncology for lung cancer.
• To compare the utility of various preclinical models, including cell lines, PDXs, and organoids.
• To highlight emerging technologies enhancing drug development and personalized treatment strategies.

Main Methods:

• Review of existing literature on preclinical models in lung cancer.
• Comparison of traditional cell lines, patient-derived xenografts (PDXs), and patient-derived lung cancer organoids (LCOs).
• Discussion of in vivo models (humanized PDX, syngeneic, GEMMs) and in vitro models (LCOs).

Main Results:

• Patient-derived xenografts (PDXs) better retain tumor histopathology and genetics than cell lines, aiding response prediction.
• In vivo models like humanized PDXs, syngeneic, and GEMMs are vital for immunotherapy and ADC research.
• Patient-derived lung cancer organoids (LCOs) show high growth rates, genomic fidelity, and strong clinical response correlations, offering a promising in vitro tool.

Conclusions:

• Preclinical models, especially PDXs and LCOs, are critical for understanding lung cancer biology and predicting treatment efficacy.
• Advancements in imaging, omics, and AI integrated with these models are revolutionizing drug development.
• This integrated approach promises more accurate personalized treatment strategies, improving patient outcomes in lung cancer.