Patient-Derived Three-Dimensional Lung Tumor Models to Evaluate Response to Immunotherapy

Area of Science:

• Oncology
• Immunology
• Biomedical Engineering

Background:

• Preclinical models are crucial for understanding tumor biology and therapy response.
• Existing models often fail to fully replicate the complex in vivo tumor microenvironment.
• There is a need for advanced models to evaluate novel cancer therapies like immune checkpoint inhibitors (ICI).

Purpose of the Study:

• To develop a novel ex vivo patient-derived three-dimensional lung tumor model (3D-LTM).
• To evaluate the rapid response of this model to immune checkpoint inhibitors (ICI).
• To identify predictive signatures for immunotherapy response in non-small cell lung cancer (NSCLC).

Main Methods:

• Development of a 3D-LTM maintaining human extracellular matrix, cell-cell interactions, and tissue architecture.
• Evaluation of 3D-LTM response to ICI, recapitulating patient response heterogeneity.
• Spatial transcriptomics and gene expression pathway analysis of 3D-LTM tissues.

Main Results:

• The 3D-LTM successfully recapitulated variable responses to immunotherapy observed in NSCLC patients.
• Positive correlations with response included CD8+ T cells, CD4+ memory T cells, mast cells, NK cells, endothelial cells, and non-classical monocytes.
• Macrophages negatively correlated with response; chemokine signaling pathways were activated in responders, while antigen presentation pathways were suppressed in non-responders.

Conclusions:

• The developed 3D-LTM is a valuable tool for evaluating immunotherapy response in NSCLC.
• This model can rapidly test novel immune-directed therapies and aid in developing biomarkers for ICI response.
• Identified immune cell signatures and pathway activations provide insights into predicting early ICI response in NSCLC patients.