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

cs.CVAug 18, 2026

Learning latent progression states from spatial heterogeneity in uterine histopathology

Authors: Qiming He, Yan Liu, Shuang Ge, Fan Yang, Yuxiang Wang, Ieng Man Zhang, Jing Yang, Zihao Jia,

Organizations: Fuzhou University Affiliated Provincial Hospital, Interdisciplinary Institute for Medical Engineering, Fuzhou University, Fuzhou, China

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.

Figures & tables

Figure 1: Overview of SpaTIE. a, H&E histopathology provides spatial morphology that may contain latent information. b, Study cohorts and data resources, including the PK3-Uter pretraining cohort and independent TCGA-UCEC and TCGA-UCS cohorts used for evaluation. c, SpaTIE performed uterus-specific representation learning and beat universal pathology foundation model (UPFM) across downstream clinical tasks. d, Mapping from static histopathology to progression states (pseudotime). e, Analysis workflow for linking SpaTIE-derived progression states to multi-omics features. f, Association between SpaTIE-derived progression states and fraction genome altered in TCGA-UCS. g, Summary of pathway-level associations with low- versus high-pseudotime groups across cohorts and omics modalities. h, Example survival stratification by pseudotime group in the TCGA-UCS high-FGA subgroup. i, Representative state-ordered heatmap of estrogen-response RNA-seq features in TCGA-UCS. j, Cross-omics virtual-perturbation schematic illustrating candidate relationships among epigenomic, transcriptomic and protein-level features.

Figure 2: SpaTIE learns a static morphology-aware representation space for uterine histopathology. a, UMAP visualization of patch-level embeddings extracted by SpaTIE, UPFM, and Baseline on TCGA-UCEC and TCGA-UCS. b, Quantitative comparison of static latent-space organization. c, Examples of representative cluster-center patches identified from the SpaTIE latent space (TCGA-UCEC). d-f, Performance comparison of downstream prediction tasks on TCGA-UCEC using F1-score, accuracy, and ROC-AUC. g-i, Performance comparison of downstream prediction tasks on TCGA-UCS using F1-score, accuracy, and ROC-AUC. j-l, Representative attention maps and high-attention histopathological regions from SpaTIE-based downstream prediction models for disease_type, pten, and tp53 respectively (TCGA-UCEC). m-o, Representative attention maps and high-attention histopathological regions from SpaTIE-based downstream prediction models for morphology, tp53, and origin respectively (TCGA-UCS).

Figure 3: SpaTIE maps static morphological representations to latent progression-associated tumor states. (a) UMAP visualization of latent state organization inferred from SpaTIE, UPFM, and baseline embeddings on TCGA-UCEC and TCGA-UCS. (b) Representative spatial tumor-state maps generated by SpaTIE, UPFM, and baseline models. (c) Quantitative comparison of inferred state organization using STAB, SPA, and FS metrics. (d) Association between SpaTIE-derived states and fraction genome altered in TCGA-UCS. (e,f) Exploratory survival stratification within FIGO stage IV patients in TCGA-UCEC and TCGA-UCS. (g,h) Exploratory subgroup survival analyses in TCGA-UCS patients with high fraction genome altered or without prior malignancy.

Figure 4: Association between SpaTIE-derived progression-associated states and pathway or molecular features. (a) Violin plots comparing pathway scores between low- and high-state groups for significant pathway-level associations. (b) Summary of significant pathway associations across cohorts and omics modalities. (c) Representative scatter plots showing correlations between inferred states and pathway scores. (d) State-ordered heatmap of UCS RNA-seq estrogen-response pathway members. (e) State-ordered heatmap of UCEC methylation features in the estrogen-response pathway. (f) Representative feature-level associations between inferred states and individual molecular features.

Figure 5: Progression-guided virtual perturbation analysis. (a-e) Representative virtual perturbation results in TCGA-UCEC across methylation, mutation, RNA-seq, RPPA, and SCNV modalities. (f) Cross-omics schematic of UCEC candidate perturbation relationships. (g-k) Representative virtual perturbation results in TCGA-UCS across methylation, mutation, RNA-seq, RPPA, and SCNV modalities. (l) Cross-omics schematic of UCS candidate perturbation relationships.

Task	TCGA-UCEC	TCGA-UCS	Definition & Categories
grade	$\checkmark$		Neoplasm histologic grading (G1/G2/G3/High).
subtype	$\checkmark$		Molecular subtyping (CN_HIGH/CN_LOW/POLE/MSI).
FIGO	$\checkmark$	$\checkmark$	FIGO stage prediction (I/II/III/IV).
Disease_type	$\checkmark$		Tumor histological subtyping (Endometrioid Endometrial Adenocarcinoma, Mixed Serous and Endometrioid Carcinoma, Serous Endometrial Adenocarcinoma).
arid1a	$\checkmark$		ARID1A mutation status prediction (Negative/Positive).
kmt2d	$\checkmark$		KMT2D mutation status prediction (Negative/Positive).

Table S1: Downstream prediction tasks in TCGA-UCEC and TCGA-UCS cohorts.

Task	Baseline	UPFM	SpaTIE
disease_type	0.813 $\pm$ 0.013	0.882 $\pm$ 0.029	0.880 $\pm$ 0.002
subtype	0.494 $\pm$ 0.005	0.592 $\pm$ 0.008	0.593 $\pm$ 0.015
figo	0.352 $\pm$ 0.010	0.453 $\pm$ 0.007	0.446 $\pm$ 0.018
grade	0.640 $\pm$ 0.004	0.688 $\pm$ 0.019	0.685 $\pm$ 0.026
pten	0.739 $\pm$ 0.032	0.842 $\pm$ 0.015	0.846 $\pm$ 0.017
pik3a	0.577 $\pm$ 0.009	0.585 $\pm$ 0.017	0.579 $\pm$ 0.003

Table S2: Performance of all the models on TCGA-UCEC (F1-score, mean

\pm

std). Bold indicates the optimal result for the current task.

Task	Baseline	UPFM	SpaTIE
disease_type	0.860 $\pm$ 0.003	0.908 $\pm$ 0.028	0.911 $\pm$ 0.003
subtype	0.539 $\pm$ 0.004	0.629 $\pm$ 0.022	0.630 $\pm$ 0.011
figo	0.620 $\pm$ 0.002	0.668 $\pm$ 0.023	0.678 $\pm$ 0.002
grade	0.706 $\pm$ 0.008	0.746 $\pm$ 0.012	0.754 $\pm$ 0.016
pten	0.774 $\pm$ 0.030	0.858 $\pm$ 0.005	0.865 $\pm$ 0.002
pik3a	0.577 $\pm$ 0.009	0.587 $\pm$ 0.003	0.602 $\pm$ 0.003

Table S3: Performance of all the models on TCGA-UCEC (Accuracy, mean

\pm

std).

Task	Baseline	UPFM	SpaTIE
disease_type	0.893 $\pm$ 0.026	0.934 $\pm$ 0.029	0.938 $\pm$ 0.025
subtype	0.729 $\pm$ 0.008	0.798 $\pm$ 0.005	0.799 $\pm$ 0.014
figo	0.613 $\pm$ 0.002	0.684 $\pm$ 0.007	0.693 $\pm$ 0.004
grade	0.837 $\pm$ 0.008	0.866 $\pm$ 0.030	0.865 $\pm$ 0.009
pten	0.801 $\pm$ 0.027	0.890 $\pm$ 0.002	0.889 $\pm$ 0.006
pik3a	0.587 $\pm$ 0.004	0.592 $\pm$ 0.022	0.602 $\pm$ 0.005

Table S4: Performance of all the models on TCGA-UCEC (ROC-AUC, mean

\pm

std).

Task	Baseline	UPFM	SpaTIE
morphology	0.844 $\pm$ 0.002	0.788 $\pm$ 0.002	0.854 $\pm$ 0.020
origin	0.750 $\pm$ 0.010	0.763 $\pm$ 0.028	0.800 $\pm$ 0.005
figo	0.589 $\pm$ 0.019	0.523 $\pm$ 0.005	0.559 $\pm$ 0.002
fbxw7	0.711 $\pm$ 0.006	0.665 $\pm$ 0.002	0.735 $\pm$ 0.007
pik3a	0.676 $\pm$ 0.031	0.743 $\pm$ 0.007	0.746 $\pm$ 0.004
ppp2r1a	0.579 $\pm$ 0.012	0.642 $\pm$ 0.007	0.627 $\pm$ 0.007

Table S5: Performance of all the models on TCGA-UCS (F1-score, mean

\pm

std).

Task	Baseline	UPFM	SpaTIE
morphology	0.900 $\pm$ 0.004	0.880 $\pm$ 0.009	0.926 $\pm$ 0.003
origin	0.781 $\pm$ 0.005	0.792 $\pm$ 0.006	0.825 $\pm$ 0.012
figo	0.605 $\pm$ 0.024	0.594 $\pm$ 0.003	0.627 $\pm$ 0.028
fbxw7	0.724 $\pm$ 0.021	0.756 $\pm$ 0.002	0.759 $\pm$ 0.009
pik3a	0.726 $\pm$ 0.014	0.759 $\pm$ 0.008	0.767 $\pm$ 0.018
ppp2r1a	0.698 $\pm$ 0.025	0.732 $\pm$ 0.008	0.741 $\pm$ 0.013

Table S6: Performance of all the models on TCGA-UCS (Accuracy, mean

\pm

std).

Task	Baseline	UPFM	SpaTIE
morphology	0.924 $\pm$ 0.030	0.935 $\pm$ 0.005	0.905 $\pm$ 0.025
origin	0.783 $\pm$ 0.019	0.822 $\pm$ 0.004	0.821 $\pm$ 0.027
figo	0.734 $\pm$ 0.025	0.710 $\pm$ 0.013	0.712 $\pm$ 0.010
fbxw7	0.698 $\pm$ 0.026	0.730 $\pm$ 0.002	0.713 $\pm$ 0.004
pik3a	0.708 $\pm$ 0.005	0.780 $\pm$ 0.019	0.823 $\pm$ 0.023
ppp2r1a	0.633 $\pm$ 0.007	0.670 $\pm$ 0.006	0.683 $\pm$ 0.026

Table S7: Performance of all the models on TCGA-UCS (ROC-AUC, mean

\pm

std).

Cohort	Clinical variable	Statistical value	$p$ value	FDR
TCGA-UCEC
UCEC	Fraction Genome Altered (FGA)	$\rho=-0.181$	$1.76\times 10^{-5}$	$5.29\times 10^{-5}$
UCEC	Diagnosis Age	$\rho=-0.071$	0.0930	0.1507
UCEC	Birth from Initial Pathologic Diagnosis Date	$\rho=0.069$	0.1004	0.1507
UCEC	TMB (nonsynonymous)	$\rho=-0.051$	0.2433	0.2920
UCEC	Mutation Count	$\rho=-0.041$	0.3442	0.3442

Table S8: Associations between SpaTIE-derived state values and clinicopathological variables in TCGA-UCEC and TCGA-UCS cohorts.

Cohort	Omics	Pathway	$p$ value	$n$	Higher group
UCS	RNASeq	Estrogen_Response	0.0048	57	low-state
UCEC	Methylation	PRC2_Polycomb	0.0189	389	high-state
UCEC	SCNV	MMR_CNV	0.0222	496	low-state
UCEC	RPPA	AKT_mTOR	0.0249	405	low-state
UCEC	RPPA	Cell_Cycle	0.0310	405	low-state

Table S9: Pathway-level associations between SpaTIE-derived progression states and multi-omics pathway scores. Pathway scores were compared between low- and high-state groups. These results are used as pathway-level context and should not be interpreted as evidence that all member features are individually significant.

Cohort	Omics	Top feature	KO score	Base $\rho$	$p$ value	FDR-supported top 20
UCEC	Methylation	PRR22	0.312	0.338	$7.75\times 10^{-12}$	20
UCEC	Mutation	GPR84	2.307	-0.176	$5.81\times 10^{-3}$	0
UCEC	SCNV	FAM81A	0.138	0.144	$1.28\times 10^{-3}$	3
UCEC	RNASeq	RAB27B	0.302	-0.302	$1.70\times 10^{-4}$	0
UCEC	RPPA	AXL	0.186	0.189	$1.25\times 10^{-4}$	5
UCS	Methylation	PIK3C2G	0.428	-0.465	$2.64\times 10^{-4}$	0

Table S10: Overview of progression-guided virtual perturbation results. The last column reports how many of the top 20 perturbation-ranked features also showed FDR-supported association with SpaTIE-derived progression states in the feature-level analysis.

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MoSPR: Histology-to-Gene Expression Prediction with Morpho-Spatial Macrostates and Low-Rank Molecular Programs

Dongmyung Shin, Geongyu Lee, Yesung Cho, Park Jong Bae

OmixAI Co. Ltd. · Oncocross Co. Ltd. · Kyunghee University

Computational Pathology Explainable Medical Image Analysis Gene Expression Prediction from Histopathology Images Whole-Slide Image Analysis

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Predicting molecular profiles from histopathology remains challenging because whole-slide images contain spatially organized, heterogeneous tissue patterns, while gene expression comprises thousands of correlated targets. We introduce MoSPR (Morpho-Spatial Program Regression), a linear framework that couples an adjacency-informed histology representation with a low-rank molecular basis. MoSPR clusters frozen patch embeddings into morphology microstates, aggregates their spatial adjacencies across the training cohort, and groups microstates with similar adjacency patterns into shared macrostates. Each slide is then represented by global morphology and macrostate-specific deviations, which are linearly mapped to coefficients of a training-derived low-rank gene-expression basis. Across three cancer cohorts from The Cancer Genome Atlas, MoSPR achieves the highest mean gene-expression prediction scores among all evaluated methods. Without pathway-level supervision, pathway scores derived from its predicted expression profiles rank first in eight of nine comparisons across three pathway collections. Ablation studies on the breast cancer cohort show complementary gains from adjacency-derived macrostate representation and low-rank molecular prediction. Moreover, with half of the training data on this cohort, MoSPR exceeds the full-data gene-prediction score of the strongest competing baseline. Finally, its linear formulation enables exact decomposition of each predicted expression profile into global and macrostate-specific molecular contributions, providing an interpretable link between spatially coherent macrostate regions and their associated molecular programs. Our code is available at https://github.com/Radisen-Panthera/MoSPR.

Abstract

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