AI allows pre-screening of FGFR3 mutational status using routine histology slides of muscle-invasive bladder cancer.

Bannier PA, Saillard C, Mann P, Touzot M, Maussion C, Matek C, Klümper N, Breyer J, Wirtz R, Sikic D, Schmitz-Dräger B, Wullich B, Hartmann A, Försch S, Eckstein M (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Nature Communications Nature Publishing Group: Nature Communications

Book Volume: 15

Journal Issue: 1

DOI: 10.1038/s41467-024-55331-6

Abstract

Pathogenic activating mutations in the fibroblast growth factor receptor 3 (FGFR3) drive disease maintenance and progression in urothelial cancer. 10-15% of muscle-invasive and metastatic urothelial cancer (MIBC/mUC) are FGFR3-mutant. Selective targeting of FGFR3 hotspot mutations with tyrosine kinase inhibitors (e.g., erdafitinib) is approved for mUC and requires FGFR3 mutational testing. However, current testing assays (polymerase chain reaction or next-generation sequencing) necessitate high tissue quality, have long turnover time, and are expensive. To overcome these limitations, we develop a deep-learning model that detects FGFR3 mutations using routine hematoxylin-eosin slides. Encompassing 1222 cases, our study is a large-scale validation of a model prescreening FGFR3 mutations for MIBC and mUC patients. In this work, we demonstrate that our model achieves high sensitivity (>93%) on advanced and metastatic cases while reducing molecular testing by 40% on average, thereby offering a cost-effective and rapid pre-screening tool for identifying patients eligible for FGFR3 targeted therapies.

Authors with CRIS profile

Christian Matek Institute of Pathology Danijel Sikic Medizinische Fakultät Bernd Wullich Lehrstuhl für Urologie Arndt Hartmann Lehrstuhl für Allgemeine Pathologie und Pathologische Anatomie Markus Eckstein Institute of Pathology

Additional Organisation(s)

Tumorzentrum der Universität Erlangen-Nürnberg

Involved external institutions

Owkin FR France (FR) Universitätsklinikum Bonn DE Germany (DE) Universität Regensburg DE Germany (DE) STRATIFYER Molecular Pathology GmbH DE Germany (DE) St. Theresien-Krankenhaus Nürnberg DE Germany (DE) Universitätsmedizin der Johannes Gutenberg-Universität Mainz DE Germany (DE)

How to cite

APA:

Bannier, P.A., Saillard, C., Mann, P., Touzot, M., Maussion, C., Matek, C.,... Eckstein, M. (2024). AI allows pre-screening of FGFR3 mutational status using routine histology slides of muscle-invasive bladder cancer. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-55331-6

MLA:

Bannier, Pierre Antoine, et al. "AI allows pre-screening of FGFR3 mutational status using routine histology slides of muscle-invasive bladder cancer." Nature Communications 15.1 (2024).

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