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Chaudhary, S. ; Voigts, A. ; Vilov, S. ; Heinig, M. ; Casale, F.P.

AI-based histopathology phenotyping reveals germline loci shaping breast cancer morphology.

In: (20th Machine Learning in Computational Biology, MLCB 2025, 10-11 September 2025, New York). 2025. accepted (Proceedings of Machine Learning Research ; 311)
AI foundation models have transformed cancer histopathology by enabling rich, data-driven feature extraction from H&E-stained whole-slide images. However, their application to studying how germline variation shapes tumor morphology remains limited. Here, we perform the first genome-wide association study of breast cancer morphology, independently analyzing AI-derived features from histology images and diagnostic pathology reports. Analyzing H&E slides from 753 patients with matched germline data, we identified six genome-wide significant loci associated with either imaging or textual features, two of which replicated across modalities. We then linked these two loci to histological features described in pathology reports, visual histological features through generative modelling, gene expression modules and patient survival. We found that rs819976 in ATAD3B is associated with disorganized, necrotic tumor morphology, poor-prognosis expression programs, and clinical features including invasive lobular carcinoma and ER positivity. These findings demonstrate the power of AI-based histology to uncover and characterize germline variants that shape tumor morphology, and assess their clinical significance.
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Publikationstyp Artikel: Konferenzbeitrag
Konferenztitel 20th Machine Learning in Computational Biology, MLCB 2025
Konferzenzdatum 10-11 September 2025
Konferenzort New York
Quellenangaben Band: 311 Heft: , Seiten: , Artikelnummer: , Supplement: ,
Institut(e) Institute of AI for Health (AIH)
Institute of Computational Biology (ICB)
Helmholtz Pioneer Campus (HPC)