标题： PAST：用于癌症组织病理学和空间转录组学的多模态单细胞基础模型 显示英文标题

标题： PAST: A multimodal single-cell foundation model for histopathology and spatial transcriptomics in cancer

摘要： 虽然病理学基础模型已经改变了癌症图像分析，但它们往往缺乏与单细胞分辨率的分子数据的整合，这限制了它们在精准肿瘤学中的应用。 在这里，我们提出了PAST，一种泛癌种的单细胞基础模型，该模型在2000万个配对的组织病理学图像和单细胞转录组上进行训练，覆盖多种肿瘤类型和组织背景。 通过联合编码细胞形态和基因表达，PAST学习统一的跨模态表示，能够捕捉细胞水平的空间和分子异质性。 这种方法使我们能够直接从常规病理切片中准确预测单细胞基因表达、虚拟分子染色和多模态生存分析。 在各种癌症和下游任务中，PAST始终优于现有方法，展示了强大的泛化能力和可扩展性。 我们的工作为病理学基础模型建立了一个新范式，提供了一个多功能工具，用于高分辨率空间组学、机制发现和精准癌症研究。 显示英文摘要

摘要： 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.