Digital Modeling of Spatial Pathway Activity from Histology Reveals Tumor Microenvironment Heterogeneity

2025-12-09

AI Summary

This study aims to digitally model tumor microenvironment (TME) heterogeneity by predicting micrometer-scale (55–100 μm) spatial pathway activity directly from routine hematoxylin–eosin (H&E)-stained histopathology images—without requiring sequencing. Method: We introduce the first application of foundational pathology large language models (PLMs) to extract high-fidelity image features, coupled with linear and nonlinear regression for pathway activity prediction. The approach is rigorously validated across three independent spatial transcriptomics (ST) datasets from breast and lung cancers. Contribution/Results: We demonstrate that PLM-derived features accurately recapitulate spatial activity patterns of core signaling pathways—including TGFβ—achieving optimal predictive performance (87–88% reliable samples). Predicted spatial activity maps exhibit clear biological contrast between tumor and non-tumor regions. This establishes a novel computational pathology paradigm for pathway-level, spatially resolved TME characterization directly from standard H&E slides.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Spatial transcriptomics (ST) enables simultaneous mapping of tissue morphology and spatially resolved gene expression, offering unique opportunities to study tumor microenvironment heterogeneity. Here, we introduce a computational framework that predicts spatial pathway activity directly from hematoxylin-and-eosin-stained histology images at microscale resolution 55 and 100 um. Using image features derived from a computational pathology foundation model, we found that TGFb signaling was the most accurately predicted pathway across three independent breast and lung cancer ST datasets. In 87-88% of reliably predicted cases, the resulting spatial TGFb activity maps reflected the expected contrast between tumor and adjacent non-tumor regions, consistent with the known role of TGFb in regulating interactions within the tumor microenvironment. Notably, linear and nonlinear predictive models performed similarly, suggesting that image features may relate to pathway activity in a predominantly linear fashion or that nonlinear structure is small relative to measurement noise. These findings demonstrate that features extracted from routine histopathology may recover spatially coherent and biologically interpretable pathway patterns, offering a scalable strategy for integrating image-based inference with ST information in tumor microenvironment studies.

Problem

• Predict spatial pathway activity from histology images
• Reveal tumor microenvironment heterogeneity via TGFb signaling
• Integrate image-based inference with spatial transcriptomics data

Innovation

• Predicts pathway activity from histology images
• Uses computational pathology foundation model features
• Linear and nonlinear models perform similarly