Sparad:
| Huvudupphovsman: | |
|---|---|
| Materialtyp: | Recurso digital |
| Språk: | engelska |
| Publicerad: |
Zenodo
2025
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| Ämnen: | |
| Länkar: | https://doi.org/10.5281/zenodo.14827075 |
| Taggar: |
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Innehållsförteckning:
- <p><span>Lung cancer remains the deadliest cancer in the United States, with lung adenocarcinoma (LUAD) as its most prevalent subtype. While computed tomography (CT)-based screening has improved early detection and enabled curative surgeries, the molecular and cellular dynamics driving early-stage LUAD progression remain poorly understood, limiting non-surgical treatment options. To address this gap, we profiled 2.24 million cells from 122 early-stage LUAD patients using multiplexed imaging mass cytometry (IMC). This analysis revealed the molecular, spatial, and temporal dynamics of LUAD development.</span></p> <p><span>Our findings uncover a binary progression model. LUAD advances through either inflammation, driven by a balance of cytotoxic and regulatory immune activity, or fibrosis, characterized by stromal activation. Surprisingly, tumor cell populations did not increase significantly. Instead, they displayed a mixed phenotypic profile consistent with epithelial-to-mesenchymal transition (EMT), effectively masking the expansion of malignant cells.</span></p> <p><span>Furthermore, we addressed discrepancies between CT-based and histology-based subtyping. CT scans, while non-invasive, often mischaracterize invasive fibrotic tumors—which account for 20.5% of LUAD cases—as mild, non-solid ground glass opacities (GGOs). Using high-content IMC imaging, we demonstrate that these tumors harbor significant risks and advocate for improved diagnostic strategies. These strategies should integrate molecular profiling to refine patient stratification and therapeutic decision-making.</span></p> <p><a name="_qg0iebrfx2zi"></a><span>Altogether, our study provides a high-resolution, systems-level view of the tumor microenvironment in early-stage LUAD. We characterize key transitions in oncogenesis and propose a precision-driven framework to enhance the detection and management of aggressive disease subtypes.</span></p>