用空间蛋白组学数据训练通用模型，实现跨样本精准生物标志物发现
AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery
- 基于多路成像数据学习蛋白质、细胞和微环境的多层次表征
- 在三阴性乳腺癌中预测免疫治疗反应并分层无病生存期，优于现有方法
- 支持零样本标注，适用于不同标记面板和数据集，适合临床诊断与药物研发
空间蛋白组学技术深刻揭示了癌症组织复杂结构，但计算分析面临挑战：每项研究使用不同的标记组合与流程，多数方法仅适配单一队列，限制知识迁移与稳健生物标志物发现。本文提出虚拟组织（VirTues），一种面向空间蛋白组学的通用基础模型，直接从多重成像数据中学习标记感知、多尺度的蛋白质、细胞、微环境与组织表征。单一预训练主干可支持标记重建、细胞分型、微环境注释、空间生物标志物发现及患者分层，包括跨异质标记面板和数据集的零样本注释。在三阴性乳腺癌中，VirTues推导的生物标志物可预测抗PD-L1化疗免疫治疗响应，并在独立队列中分层无病生存期，性能超越同数据集衍生的最先进生物标志物及当前临床分层方案。
原文摘要 · Abstract (English)
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.
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