SpaCEy links spatial tissue patterns to clinical outcomes using explainable graph neural networks.

Tissues are complex ecosystems organised in space, and alterations in this organisation underpin multiple diseases. Spatial omics enables molecular profiling of tissue organisation, but linking these patterns to clinical outcomes remains challenging. We present SpaCEy (Spatial Clinical Explainability), an explainable graph neural network that identifies tissue patterns predictive of clinical outcomes in spatial proteomics datasets. SpaCEy models tissues as spatial graphs from molecular marker expression, without using predefined cell-type labels or anatomical regions as model inputs. Its embeddings capture intercellular relationships and molecular dependencies for predicting overall survival and disease progression. An integrated explainer identifies recurring spatial patterns and coordinated marker expression relevant to model predictions. Applied to a spatial proteomic lung cancer cohort, SpaCEy identifies spatial and protein-expression patterns associated with disease progression. Across multiple breast cancer proteomic datasets, it stratifies patients by overall survival, both across and within established clinical subtypes, and highlights protein markers underlying this stratification.