PAST: A multimodal single-cell foundation model for histopathology and spatial transcriptomics in cancer
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908441611599872 |
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| author | Yang, Changchun Li, Haoyang Wu, Yushuai Zhang, Yilan Jiao, Yifeng Zhang, Yu Huang, Rihan Cheng, Yuan Qi, Yuan Guo, Xin Gao, Xin |
| author_facet | Yang, Changchun Li, Haoyang Wu, Yushuai Zhang, Yilan Jiao, Yifeng Zhang, Yu Huang, Rihan Cheng, Yuan Qi, Yuan Guo, Xin Gao, Xin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06418 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PAST: A multimodal single-cell foundation model for histopathology and spatial transcriptomics in cancer Yang, Changchun Li, Haoyang Wu, Yushuai Zhang, Yilan Jiao, Yifeng Zhang, Yu Huang, Rihan Cheng, Yuan Qi, Yuan Guo, Xin Gao, Xin Quantitative Methods Computer Vision and Pattern Recognition Applications 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. |
| title | PAST: A multimodal single-cell foundation model for histopathology and spatial transcriptomics in cancer |
| topic | Quantitative Methods Computer Vision and Pattern Recognition Applications |
| url | https://arxiv.org/abs/2507.06418 |