PAST: A multimodal single-cell foundation model for histopathology and spatial transcriptomics in cancer
Journal:
arXiv
Published Date:
Jul 8, 2025
Abstract
While pathology foundation models have transformed cancer image analysis,
they often lack integration with molecular data at single-cell resolution,
limiting their utility for precision oncology. Here, we present PAST, a
pan-cancer single-cell foundation model trained on 20 million paired
histopathology images and single-cell transcriptomes spanning multiple tumor
types and tissue contexts. By jointly encoding cellular morphology and gene
expression, PAST learns unified cross-modal representations that capture both
spatial and molecular heterogeneity at the cellular level. This approach
enables accurate prediction of single-cell gene expression, virtual molecular
staining, and multimodal survival analysis directly from routine pathology
slides. Across diverse cancers and downstream tasks, PAST consistently exceeds
the performance of existing approaches, demonstrating robust generalizability
and scalability. Our work establishes a new paradigm for pathology foundation
models, providing a versatile tool for high-resolution spatial omics,
mechanistic discovery, and precision cancer research.
