PAST模型融合病理图像与单细胞基因表达，实现癌症空间异质性精准分析。
PAST: A multimodal single-cell foundation model for histopathology and spatial transcriptomics in cancer
- 联合编码细胞形态与基因表达，学习跨模态统一表征。
- 在2000万对数据上训练，可准确预测单细胞基因表达。
- 适合肿瘤空间组学、机制研究和精准医疗方向的学者使用。
尽管病理基础模型已革新癌症图像分析，但通常缺乏与单细胞分辨率分子数据的整合，限制了其在精准肿瘤学中的应用。本文提出PAST，一个基于2000万对病理图像与单细胞转录组数据的泛癌种单细胞基础模型，覆盖多种肿瘤类型和组织背景。通过联合编码细胞形态与基因表达，PAST学习到统一的跨模态表征，捕捉细胞层面的空间与分子异质性。该方法可直接从常规病理切片中实现单细胞基因表达预测、虚拟分子染色及多模态生存分析。在多种癌症和下游任务中，PAST性能持续优于现有方法，展现出优异的泛化能力与可扩展性。本研究确立了病理基础模型新范式，为高分辨率空间组学、机制发现与精准癌症研究提供通用工具。
原文摘要 · Abstract (English)
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.
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