# Learning latent progression states from spatial heterogeneity in uterine histopathology

arXiv: https://arxiv.org/abs/2608.17337 · HTML: https://mlsanity.com/paper/2608.17337/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 18 Aug 2026.

## Builds on (most cited first, 14 of 14)

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- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Multimodal Whole Slide Foundation Model for Pathology](https://mlsanity.com/paper/2411.19666/) (2024)
- [Virchow: A Million-Slide Digital Pathology Foundation Model](https://mlsanity.com/paper/2309.07778/) (2023)
- [A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models](https://mlsanity.com/paper/2407.06508/) (2024)
- [How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities](https://mlsanity.com/paper/2409.11654/) (2024)
- [Benchmarking foundation models as feature extractors for weakly-supervised computational pathology](https://mlsanity.com/paper/2408.15823/) (2024)
- [Deep Generative Classification of Blood Cell Morphology](https://mlsanity.com/paper/2408.08982/) (2024)
- [Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology](https://mlsanity.com/paper/2505.21928/) (2025)
- [GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights](https://mlsanity.com/paper/2603.02926/) (2026)

## Abstract (arXiv)

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.