AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Wenckstern, Johann Jain, Eeshaan Cheng, Yexiang von Querfurth, Benedikt Vasilev, Kiril Pariset, Matteo Cheng, Phil F. Liakopoulos, Petros Michielin, Olivier Wicki, Andreas Gut, Gabriele Bunne, Charlotte |
| author_facet | Wenckstern, Johann Jain, Eeshaan Cheng, Yexiang von Querfurth, Benedikt Vasilev, Kiril Pariset, Matteo Cheng, Phil F. Liakopoulos, Petros Michielin, Olivier Wicki, Andreas Gut, Gabriele Bunne, Charlotte |
| contents | Spatial proteomics technologies have transformed our understanding of complex tissue architecture in cancer but present unique challenges for computational analysis. Each study uses a different marker panel and protocol, and most methods are tailored to single cohorts, which limits knowledge transfer and robust biomarker discovery. Here we present Virtual Tissues (VirTues), a general-purpose foundation model for spatial proteomics that learns marker-aware, multi-scale representations of proteins, cells, niches and tissues directly from multiplex imaging data. From a single pretrained backbone, VirTues supports marker reconstruction, cell typing and niche annotation, spatial biomarker discovery, and patient stratification, including zero-shot annotation across heterogeneous panels and datasets. In triple-negative breast cancer, VirTues-derived biomarkers predict anti-PD-L1 chemo-immunotherapy response and stratify disease-free survival in an independent cohort, outperforming state-of-the-art biomarkers derived from the same datasets and current clinical stratification schemes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06039 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery Wenckstern, Johann Jain, Eeshaan Cheng, Yexiang von Querfurth, Benedikt Vasilev, Kiril Pariset, Matteo Cheng, Phil F. Liakopoulos, Petros Michielin, Olivier Wicki, Andreas Gut, Gabriele Bunne, Charlotte Quantitative Methods Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Spatial proteomics technologies have transformed our understanding of complex tissue architecture in cancer but present unique challenges for computational analysis. Each study uses a different marker panel and protocol, and most methods are tailored to single cohorts, which limits knowledge transfer and robust biomarker discovery. Here we present Virtual Tissues (VirTues), a general-purpose foundation model for spatial proteomics that learns marker-aware, multi-scale representations of proteins, cells, niches and tissues directly from multiplex imaging data. From a single pretrained backbone, VirTues supports marker reconstruction, cell typing and niche annotation, spatial biomarker discovery, and patient stratification, including zero-shot annotation across heterogeneous panels and datasets. In triple-negative breast cancer, VirTues-derived biomarkers predict anti-PD-L1 chemo-immunotherapy response and stratify disease-free survival in an independent cohort, outperforming state-of-the-art biomarkers derived from the same datasets and current clinical stratification schemes. |
| title | AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery |
| topic | Quantitative Methods Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2501.06039 |