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.

翻译：空间蛋白质组学技术已彻底改变我们对癌症复杂组织结构理解，但也为计算分析带来独特挑战。每项研究采用不同的标记物组合与实验方案，且多数方法仅针对单一队列定制，这限制了知识迁移与稳健生物标志物发现。本文提出虚拟组织（VirTues），一种通用空间蛋白质组学基础模型，可直接从多重成像数据中学习具有标记物感知能力的蛋白质、细胞、生态位及组织的多尺度表征。基于单一预训练主干网络，VirTues支持标记物重建、细胞分型、生态位注释、空间生物标志物发现及患者分层，包括跨异质标记组合与数据集的零样本注释。在三阴性乳腺癌中，VirTues衍生的生物标志物可预测抗PD-L1化学免疫治疗反应，并在独立队列中实现无病生存期分层，其性能优于从相同数据集衍生的最先进生物标志物及当前临床分层方案。