GEO series
From subcutaneous to orthotopic: Bridging the gap to human disease in lung cancer research [mouse]
GSE301349
Mus musculus
Expression profiling by high throughput sequencing
12 samples
2025/12/17
GPL30172
Summary
Preclinical lung cancer models frequently fail to predict clinical outcomes due to their limited ability to replicate the complex tumor microenvironment and metastatic processes seen in patients. In this study, we establish and characterize a clinically relevant orthotopic lung cancer model in mice, offering a substantial advancement over conventional subcutaneous models. Using thoracotomy-based injection of luciferase-expressing lung adenocarcinoma cells, we consistently generate single, well-defined nodules within the lung parenchyma that closely mimic human primary tumors. Longitudinal monitoring with bioluminescence imaging and computed tomography enables accurate spatial and volumetric assessment of tumor progression, with tumor size correlating with the initial cell dose. Comparative analyses demonstrate that orthotopic tumors exhibit enhanced vascularization, proliferation, and reduced hypoxic and stress markers (HIF-1α, γH2AX, p16, p21), alongside elevated Cyclin D1 expression-features reflecting a more physiologically relevant tumor environment. Importantly, orthotopic tumors display an enriched and organized immune infiltration, including enriched CD4⁺ and CD8⁺ T cells and dendritic cells, forming immune niches not present in subcutaneous models. To model metastasis, we isolate and culture circulating tumor cells (CTCs) from orthotopic tumor-bearing mice. Intracardiac injection of these CTCs leads to organ-specific metastases (e.g., liver, brain, bone), recapitulating clinical dissemination patterns. Transcriptomic and proteomic profiling of metastatic sublines reveals both conserved pro-metastatic programs-including chemokine signaling, EMT, and immune evasion-and niche-specific adaptations, particularly in bone metastases. This orthotopic model offers enhanced translational relevance for evaluating therapies and dissecting metastatic progression in lung cancer.
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Paper (PMID 41367565) ↗
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