从子宫组织病理学的空间异质性中学习潜在进展状态

中文摘要 AI 辅助

肿瘤进展伴随组织结构、形态及微环境的变化，但组织病理学中与进展相关的异质性通常被压缩为静态诊断类别。本文提出SpaTIE（一种子宫特异性计算病理学框架），可学习形态感知表征并将空间组织病理学异质性组织为与进展相关的肿瘤状态。SpaTIE基于10426张子宫苏木精-伊红全切片图像开发，并在TCGA-UCEC和TCGA-UCS队列中进行评估。学习到的表征形成了形态流形，支持诊断、分子及生存相关预测任务，并能将注意力定位到信息丰富的肿瘤区域。除监督预测外，SpaTIE无需时间或分子监督，即可从横断面形态中推断肿瘤状态轴。这些形态衍生状态具有空间连贯性，与临床病理变量及生存结果相关，且并非简单再现分期或诊断标签。整合多组学分析将推断的状态与DNA甲基化、体细胞拷贝数变异、突变、RNA-seq及RPPA谱关联，突出了与染色质调控、拷贝数相关结构变异、受体酪氨酸激酶信号、细胞粘附、细胞外基质重塑及代谢适应相关的分子程序。进展引导的虚拟扰动进一步优先考虑与形态衍生状态组织耦合的分子特征。综上，这些发现表明子宫组织病理学包含可恢复的与进展相关的肿瘤状态信息，并确立SpaTIE为连接空间形态与多组学信息的肿瘤状态发现框架。

英文摘要

Tumor progression is accompanied by changes in architecture, morphology and microenvironmental organization, yet progression-associated heterogeneity is usually compressed into static diagnostic categories in histopathology. Here we present SpaTIE, a uterus-specific computational pathology framework that learns morphology-aware representations and organizes spatial histopathological heterogeneity into progression-associated tumor states. SpaTIE was developed using 10,426 uterine hematoxylin and eosin whole-slide images and evaluated in TCGA-UCEC and TCGA-UCS cohorts. The learned representations formed morphology manifolds, supported diagnostic, molecular and survival-related prediction tasks, and localized attention to informative tumor regions. Beyond supervised prediction, SpaTIE inferred tumor-state axes from cross-sectional morphology without temporal or molecular supervision. These morphology-derived states were spatially coherent and showed associations with clinicopathological variables and survival outcomes, while not simply recapitulating staging or diagnostic labels. Integrative multi-omics analyses linked the inferred states to DNA methylation, somatic copy-number variation, mutation, RNA-seq and RPPA profiles, highlighting molecular programs related to chromatin regulation, copy-number-associated structural variation, receptor tyrosine kinase signaling, cell adhesion, extracellular-matrix remodeling and metabolic adaptation. Progression-guided virtual perturbation further prioritized molecular features coupled to the morphology-derived state organization. Together, these findings suggest that uterine histopathology contains recoverable progression-associated tumor-state information and establish SpaTIE as a framework for connecting spatial morphology with multi-omics-informed tumor-state discovery.

发表机构

• Fuzhou University（福州大学）
• Fuzhou University Affiliated Provincial Hospital（福州大学附属省立医院）
• Interdisciplinary Institute for Medical Engineering, Fuzhou University（福州大学医学工程交叉研究院）
• Medical Optical Technology R&D Center, Research Institute of Tsinghua, Pearl River Delta（清华珠三角研究院医学光学技术研发中心）
• Peking University Health Science Center（北京大学医学部）
• Third Hospital, School of Basic Medical Sciences, Peking University Health Science Center（北京大学医学部基础医学院第三医院）
• Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院生物医药与健康工程研究院）
• Tsinghua University（清华大学）
• Peng Cheng Laboratory（鹏城实验室）
• Jinfeng Laboratory（金凤实验室）

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