AutoMarker: Outcome-Guided Discovery of Multiscale Biomarker Candidates

Clinical outcomes are observed at the patient level, whereas associated biomarker candidates emerge across cells, local microenvironments, and tissue organization. AutoMarker introduces an outcome-guided framework for discovering multiscale biomarker candidates directly from pathology representations. Protein-aligned morphology representations organize tissue into four coordinated views capturing cellular phenotypes, neighborhood composition, tissue organization, and context-dependent cellular states. Adaptive local divergence search identifies representation-space regions enriched in different outcome groups, while sign-consistent graph refinement consolidates local responses into coherent candidate components. WSI-level abundance comparisons, pathological characterization, and spatial back-tracing connect candidates to tissue context. Across four cohorts, we evaluate multiscale candidate discovery, including independent external validation in TCGA-COAD of candidates discovered and frozen in Orion-CRC. Comparative and ablation experiments demonstrate the contribution of adaptive search and graph refinement, while scalability experiments support mining at the million-entity scale.