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

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 is associated with patient prognosis. Methods: Multiplex immunohistochemistry (mIHC) for B7-H3 and Iba1 (a macrophage marker) was performed on surgically resected tissues from 22 patients, with single-cell segmentation and classification in QuPath. We then applied spatial point pattern and spatial autocorrelation analyses to evaluate the relative positioning of single cells, computing spatial interaction metrics, including cross-Moran’s I and the cross-K function, within a 35 μm radius. These metrics were correlated with postoperative overall survival (OS), and prognostic thresholds were determined 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 moderate positive correlation (rs=0.434, p = 0.044), of a magnitude comparable to the clinical GPA score (rs=0.464, p = 0.029). Conversely, specific spatial metrics emerged as candidate 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 function (p = 0.033), associated with significantly shorter OS. This pattern suggests a coordinated local immunosuppressive network. Conversely, high spatial integration between B7-H3+ and B7-H3− tumor cells was associated with prolonged OS (p = 0.016), whereas spatial segregation of B7-H3+ tumor cells was associated with 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.