Fibroblast subsets in non-small cell lung cancer: Associations with survival, mutations, and immune features.

Background: Cancer-associated fibroblasts (CAFs) are molecularly heterogeneous mesenchymal cells that interact with malignant cells and immune cells and confer anti- and protumorigenic functions. Prior in situ profiling studies of human CAFs have largely relied on scoring single markers, thus presenting a limited view of their molecular complexity. Our objective was to study the complex spatial tumor microenvironment of non-small cell lung cancer (NSCLC) with multiple CAF biomarkers, identify novel CAF subsets, and explore their associations with patient outcome.
Methods: Multiplex fluorescence immunohistochemistry was employed to spatially profile the CAF landscape in 2 population-based NSCLC cohorts (n = 636) using antibodies against 4 fibroblast markers: platelet-derived growth factor receptor-alpha (PDGFRA) and -beta (PDGFRB), fibroblast activation protein (FAP), and alpha-smooth muscle actin (αSMA). The CAF subsets were analyzed for their correlations with mutations, immune characteristics, and clinical variables as well as overall survival.
Results: Two CAF subsets, CAF7 (PDGFRA-/PDGFRB+/FAP+/αSMA+) and CAF13 (PDGFRA+/PDGFRB+/FAP-/αSMA+), showed statistically significant but opposite associations with tumor histology, driver mutations (tumor protein p53 [TP53] and epidermal growth factor receptor [EGFR]), immune features (programmed death-ligand 1 and CD163), and prognosis. In patients with early stage tumors (pathological tumor-node-metastasis IA-IB), CAF7 and CAF13 acted as independent prognostic factors.
Conclusions: Multimarker-defined CAF subsets were identified through high-content spatial profiling. The robust associations of CAFs with driver mutations, immune features, and outcome suggest CAFs as essential factors in NSCLC progression and warrant further studies to explore their potential as biomarkers or therapeutic targets. This study also highlights multiplex fluorescence immunohistochemistry-based CAF profiling as a powerful tool for the discovery of clinically relevant CAF subsets.
(© The Author(s) 2022. Published by Oxford University Press.)