Spatial biomarker discovery via interpretable semantic learning in histopathology.

Abstract

Spatial biomarkers are critical for precision oncology but remain challenging to systematically discover due to the complexity of whole-slide images. We present PathPrism, an interpretable AI framework for spatial biomarker discovery and virtual experimentation. Unlike black-box models, PathPrism encodes tissue architecture into pathologically informed spatial features, enabling transparent modeling of prognosis, molecular alterations, and therapy response. Applied to 7,000 patients with colorectal cancer across 11 cohorts, PathPrism uncovered hundreds of biomarkers predictive of survival, MSI, BRAF, and TP53 mutations, and stratified chemotherapy benefit in stage II/III disease. Building on these interpretable findings, PathPrism uses large language models as auxiliary tools to generate hypotheses grounded in spatial semantics. We further introduce VirtualWSI, a platform for semantic perturbation within an interpretable spatial biomarker atlas. PathPrism provides a scalable and interpretable framework for spatial biomarker discovery.