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

Pierre-Antoine Bannier (), Charlie Saillard, Philipp Mann, Maxime Touzot, Charles Maussion, Christian Matek, Niklas Klümper, Johannes Breyer, Ralph Wirtz, Danijel Sikic, Bernd Schmitz-Dräger, Bernd Wullich, Arndt Hartmann, Sebastian Försch and Markus Eckstein ()
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Pierre-Antoine Bannier: Owkin
Charlie Saillard: Owkin
Philipp Mann: Owkin
Maxime Touzot: Owkin
Charles Maussion: Owkin
Christian Matek: Friedrich-Alexander-Universität Erlangen-Nürnberg
Niklas Klümper: University Hospital Bonn
Johannes Breyer: University of Regensburg
Ralph Wirtz: STRATIFYER Molecular Pathology
Danijel Sikic: Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN) and Comprehensive Cancer Center Alliance WERA (CCC WERA)
Bernd Schmitz-Dräger: St. Theresienkrankenhaus
Bernd Wullich: Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN) and Comprehensive Cancer Center Alliance WERA (CCC WERA)
Arndt Hartmann: Friedrich-Alexander-Universität Erlangen-Nürnberg
Sebastian Försch: Johannes Gutenberg-Universität Mainz
Markus Eckstein: Friedrich-Alexander-Universität Erlangen-Nürnberg

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-55331-6

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