2025/02/22 by Junbum Kim, Hiranmayi Ravichandran, Liron Yoffe +11 · 1 voice
Medicine · Immunology and Microbiology · #Cancer Cells and Metastasis #Cancer Immunotherapy and Biomarkers #Immune cells in cancer
paper · pdf · doi:10.1101/2025.02.19.637138
openalex publication_date 2025/02/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22
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. 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. 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. 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.