Elucidating cellular origins and TME dynamic evolution in NSCLC through multi-omics technologies

Area of Science:

• Oncology
• Genomics
• Immunology

Background:

• Non-small cell lung cancer (NSCLC) remains a major cause of cancer death.
• Tumor heterogeneity and microenvironment remodeling drive resistance to current therapies like immunotherapy and targeted treatments.
• Understanding lung adenocarcinoma (LUAD) cell origins and tumor microenvironment (TME) evolution is critical for new therapeutic strategies.

Purpose of the Study:

• To review how multi-omics approaches elucidate NSCLC origins and TME dynamics.
• To identify key cellular players and pathways driving LUAD progression and therapeutic resistance.
• To explore emerging strategies for overcoming resistance in NSCLC treatment.

Main Methods:

• Single-cell and spatial transcriptomics (multi-omics) to analyze cellular heterogeneity and spatial organization.
• Analysis of tumor microenvironment (TME) components, including immune cells and cancer-associated fibroblasts (CAFs).
• Review of emerging therapeutic strategies targeting lineage plasticity, TME reprogramming, and microbiome modulation.

Main Results:

• Multi-omics identifies alveolar type 2 (AT2) cells as LUAD origins, with SOX2/WNT/YAP pathways mediating lineage plasticity.
• The TME evolves through immune-editing, with CAF/tumor-associated macrophage (TAM) crosstalk promoting immunosuppression.
• Key therapeutic targets include specific immune subsets (CXCL13+CD8+T cells, M1/M2 macrophages) and antigen-presenting CAFs (apCAFs).

Conclusions:

• Multi-omics provides critical insights into NSCLC evolution and TME complexity.
• Targeting lineage plasticity, TME reprogramming, and microbiome modulation may overcome resistance to immune checkpoint blockade (ICB) and tyrosine kinase inhibitors (TKIs).
• AI-driven TME modeling is essential for resolving spatiotemporal heterogeneity and guiding precision interventions in NSCLC.