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

TL;DR

This study presents a computational method to predict spatial pathway activity from standard histology images, revealing tumor microenvironment heterogeneity with high accuracy and biological relevance.

Contribution

It introduces a novel framework that uses image features from pathology models to infer spatial pathway activity directly from histology images.

Findings

TGFb signaling was most accurately predicted across datasets.

Spatial TGFb activity maps reflected tumor versus non-tumor contrast.

Linear and nonlinear models performed similarly, indicating a mostly linear relationship.

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.