Histology predicts gene expression using spatial patterns and low-rank programs
MoSPR: Histology-to-Gene Expression Prediction with Morpho-Spatial Macrostates and Low-Rank Molecular Programs
Artificial IntelligenceComputer Vision and Pattern RecognitionMachine Learning
Summary
Predicting gene activity from tissue images is hard because tissues have many small, complex patterns and genes are linked in many ways. The authors created MoSPR, a method that groups tiny tissue patterns by their surroundings to form bigger, meaningful regions. These regions then help predict gene activity accurately using a simpler set of gene patterns. MoSPR works better than other methods on several cancer datasets and can clearly explain how specific tissue areas relate to gene activity.
What this means in practice
- •For clinical diagnostics teams: Predict gene expression profiles from standard histology slides to assist personalized cancer treatment decisions.
- •For biomedical software developers: Integrate spatially aware histology-to-gene expression models into pathology tools to enhance molecular understanding of tissue samples.
Authors
Dongmyung Shin, Geongyu Lee, Yesung Cho, Park Jong Bae
Abstract
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.