HistoAtlas: A Pan-Cancer Morphology Atlas Linking Histomics to Molecular Programs and Clinical Outcomes

2026-03-17

AI Summary

This study systematically interrogates interpretable histomorphological features from routine H&E-stained whole-slide images to uncover their associations with molecular mechanisms and clinical outcomes. Leveraging 6,745 slides across 21 cancer types from The Cancer Genome Atlas (TCGA), the authors extracted 38 interpretable morphological features and rigorously linked them—using covariate adjustment and multiple testing correction—to survival outcomes, gene expression profiles, somatic mutations, and immune subtypes. The work presents the first pan-cancer atlas that traces morphological patterns to specific tissue regions and even single-cell levels, enabling large-scale biomarker discovery without requiring additional staining or sequencing. The findings recapitulate known biological pathways while revealing novel spatially resolved immune signals and morphology-based prognostic subtypes. All data are publicly accessible at https://histoatlas.com.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Computer Vision: Interpretability, Explainability, and Transparency
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

We present HistoAtlas, a pan-cancer computational atlas that extracts 38 interpretable histomic features from 6,745 diagnostic H&E slides across 21 TCGA cancer types and systematically links every feature to survival, gene expression, somatic mutations, and immune subtypes. All associations are covariate-adjusted, multiple-testing corrected, and classified into evidence-strength tiers. The atlas recovers known biology, from immune infiltration and prognosis to proliferation and kinase signaling, while uncovering compartment-specific immune signals and morphological subtypes with divergent outcomes. Every result is spatially traceable to tissue compartments and individual cells, statistically calibrated, and openly queryable. HistoAtlas enables systematic, large-scale biomarker discovery from routine H&E without specialized staining or sequencing. Data and an interactive web atlas are freely available at https://histoatlas.com .

Problem

• histomics
• pan-cancer
• morphology
• biomarker discovery
• clinical outcomes

Innovation

• histomics
• pan-cancer atlas
• H&E-based biomarker discovery
• spatially resolved morphology
• computational pathology