PAST: A multimodal single-cell foundation model for histopathology and spatial transcriptomics in cancer
This provides a versatile tool for precision oncology by integrating cellular morphology and gene expression at single-cell resolution, though it builds incrementally on existing pathology foundation models.
The authors tackled the lack of integration between histopathology images and molecular data in cancer analysis by developing PAST, a pan-cancer single-cell foundation model trained on 20 million paired images and transcriptomes, which enables accurate prediction of single-cell gene expression and outperforms existing methods across diverse cancers.
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.