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Related Experiment Video

Updated: May 5, 2026

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Interpreting and Validating a Deep Learning Model Predictive of Spatial Morphologic-Molecular Patterns in Lung

Vibha R Rao, Adrienne A Workman, Scott M Palisoul

    Biorxiv : the Preprint Server for Biology
    |May 4, 2026
    PubMed
    Summary

    We developed XpressO-Lung, a deep learning model that predicts spatial gene expression in lung adenocarcinoma (LUAD) directly from diagnostic slides. This tool links tissue morphology to molecular data, aiding biomarker discovery and precision oncology.

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    Area of Science:

    • Computational pathology
    • Genomics
    • Artificial intelligence in oncology

    Background:

    • Lung adenocarcinoma (LUAD) shows significant heterogeneity, complicating genomic analysis.
    • Current methods like genomic profiling and spatial transcriptomics have limitations in cost, accessibility, and time.

    Purpose of the Study:

    • To develop an explanatory deep learning model, XpressO-Lung, for predicting spatial gene expression heterogeneity in LUAD.
    • To link tissue morphology from diagnostic whole-slide images (WSIs) with bulk-transcriptomic data.

    Main Methods:

    • XpressO-Lung was trained on 200 LUAD cases from The Cancer Genome Atlas.
    • The model learned associations between tissue morphology on H&E-stained WSIs and transcriptomic data.
    • Predicted spatial gene expression patterns were validated using immunohistochemistry on external clinical samples.

    Main Results:

    • XpressO-Lung accurately predicted spatial expression patterns for key genes (e.g., NAPSA, TP53, CD8A) with AUCs from 0.64 to 0.92.
    • Predicted patterns correlated with known tumor microenvironment interactions.
    • Validation confirmed concordance between model predictions and observed histomorphologic features.

    Conclusions:

    • XpressO-Lung effectively bridges histopathology and bulk-transcriptomics for explainable spatio-morpho-genomic analysis in LUAD.
    • The model facilitates biomarker discovery, therapeutic stratification, and precision oncology.
    • This approach enables direct analysis of molecular events on diagnostic WSIs.