Spatial Architecture of B7-H3-Expressing Cell Subpopulations Predicts Patient Prognosis in Lung Cancer Brain Metastases: A Pilot Study

Le résumé fourni par la source

Background: The clinical outcomes of lung cancer brain metastases (LCBMs) are highly variable. Traditional pathology relies on bulk cell densities. These static measures fail to capture the spatial architecture of the tumor immune microenvironment (TIME). B7-H3 (CD276) represents a key immune checkpoint in LCBMs. We investigated whether the spatial orchestration of B7-H3-expressing cell populations predicts patient prognosis. Methods: We performed multiplex immunohistochemistry (mIHC) for B7-H3 and Iba1 (a macrophage marker) in surgically resected tissues from 22 patients. We used QuPath for single-cell segmentation and classification. We performed spatial point pattern and spatial autocorrelation analyses to evaluate the relative positioning of single cells. We computed spatial interaction metrics, which included cross-Moran’s I and the cross-K function, within a 35 μm radius. We correlated these metrics with postoperative overall survival (OS) and determined prognostic thresholds via time-dependent ROC curve analysis. Results: Standard cell densities generally did not correlate with OS, although B7-H3+ tumor-associated macrophage (TAM) density showed a positive correlation. Conversely, specific spatial metrics served as significant prognostic factors. High spatial mixing and clustering of B7-H3+ and B7-H3- TAMs, as shown by high cross-Moran’s I (p = 0.011) and the cross-K functions (p = 0.033), correlated with significantly shorter OS. This pattern suggests a coordinated local immunosuppressive network. Conversely, high spatial integration between B7-H3+ and B7-H3- tumor cells correlated with prolonged OS (p = 0.016), whereas spatial segregation of B7-H3+ tumor cells predicted poor outcomes. Conclusions: Decoding the spatial architecture of B7-H3-expressing cell subpopulations provides superior prognostic stratification compared with standard density-based metrics. These localized spatial niches represent potential biomarkers and therapeutic targets for personalized LCBM management.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.