Genome-scale molecular profiling from routine histopathology with a multimodal pathology foundation model
Abstract
Pathology foundation models trained from whole-slide images alone treat tissue morphology as an autonomous visual phenotype, leaving learned representations only weakly anchored to the molecular processes that generate tissue architecture. Here we present Fuji, a multimodal pathology foundation model that grounds morphology in transcriptomic and mutational supervision through a two-stage pretraining strategy: multi-teacher contrastive distillation that harmonizes formalin-fixed paraffin-embedded and fresh-frozen specimens, followed by multimodal multi-task learning that jointly optimizes masked image reconstruction, transcriptomic embedding reconstruction, and attention-aligned mutational signature inference. Trained on 60,546 whole-slide images paired with 45,675 bulk RNA-seq profiles and 22,683 whole-genome sequencing samples drawn from TCGA, GTEx, and CPTAC, Fuji yields a protocol-invariant, genome-informed embedding that we treat as a quantitative coordinate system for probing how genomic states project into tissue architecture. Genome-scale instability phenotypes, microsatellite instability, chromosomal instability, homologous recombination deficiency, and whole-genome duplication, are recoverable from histology with AUCs up to 0.98, consistently exceeding existing pathology foundation models. Extrachromosomal DNA amplification leaves a distinct morphological footprint that stratifies survival independently of tissue lineage. Etiologic mutational processes such as…
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