Graph neural network modeling of spatial tumor-immune interactions identifies prognostic cellular niches in non‑small cell lung cancer

作者：Katharina Hoebel, James R. Lindsay, Jennifer Altreuter, J. Alessi, Jason L. Weirather, Ian Dryg, Anita Giobbie-Hurder, Zhirou Li, Kun‐Hsing Yu, Mark M. Awad, Scott Rodig, William Lotter · 发表于：npj Precision Oncology · 年份：2026 · DOI：10.1038/s41698-026-01314-3 · 被引用次数：2 · 研究领域：Mathematical Biology Tumor Growth、Ferroptosis and cancer prognosis、Radiomics and Machine Learning in Medical Imaging

The spatial organization of immune and tumor cells within the tumor microenvironment (TME) has a critical influence on antitumor immunity and patient survival. However, hand-engineered metrics such as cell densities or pairwise proximity scores fail to capture the complexity of local cell-cell interactions. Understanding these higher-order spatial patterns and their relation to patient outcomes is especially important for non-small cell lung cancer (NSCLC), the deadliest cancer worldwide. Here, we elucidate the NSCLC TME using a graph neural network (GNN)-based framework to model spatially localized cellular neighborhoods in multiplex immunofluorescence data from a clinical cohort of 506 patients. The GNN predicted patient survival with high accuracy (concordance index: 0.82) and remained a significant prognostic factor when adjusted for clinical covariates. Interpretability analyses revealed that specific combinations of cell types, particularly involving CD8+ T cells, PD-L1+ immune cells, and FOXP3+ regulatory T cells, modulated predictions depending on their spatial context. In-silico manipulation experiments applied to the trained GNN, used here as an interpretable surrogate model, suggested that the impact of CD8+ cells on survival were estimated as favorable when in direct tumor contact and less favorable when adjacent to immunosuppressive cells. Latent-space clustering identified distinct TME states predictive of outcome, reflecting varying balances of immune activatio...