Learning latent progression states from spatial heterogeneity in uterine histopathology

Qiming He; Yan Liu; Shuang Ge; Fan Yang; Yuxiang Wang; Ieng Man Zhang; Jing Yang; Zihao Jia; Ajin Hu; Yexing Zhang; Zixiu Song; Qiang Huang; Xiaoya Zhao; Zihan Wang; Xianjing Zheng; Yijun Zheng; Liling Lin; Shuxing Liu; Bin Bao; Yue Xie; Tian Guan; Yonghong He; Congrong Liu

arXiv · 2608.17337

Learning latent progression states from spatial heterogeneity in uterine histopathology

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Abstract

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.

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Qiming He, Yan Liu, Shuang Ge, Fan Yang, Yuxiang Wang, Ieng Man Zhang, Jing Yang, Zihao Jia, Ajin Hu, Yexing Zhang, Zixiu Song, Qiang Huang, Xiaoya Zhao, Zihan Wang, Xianjing Zheng, Yijun Zheng, Liling Lin, Shuxing Liu, Bin Bao, Yue Xie, Tian Guan, Yonghong He, Congrong Liu. 2026-08-18. Learning latent progression states from spatial heterogeneity in uterine histopathology. https://arxiv.org/abs/2608.17337

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