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

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.

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Publication Details

Journal
Nature Communications
Published
2024-12-30
DOI
https://doi.org/10.1038/s41467-024-55331-6
Citations
16
Primary Topic
Bladder and Urothelial Cancer Treatments
Type
article
Field-Weighted Citation Impact
9.16

Funders

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article

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

Maxime Touzot, Charlie Saillard, Pierre‐Antoine Bannier, Bernd Wullich et al.
16 citations
Nature Communications
Bladder and Urothelial Cancer Treatments
9.16
article

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

Maxime Touzot, Charlie Saillard, Pierre‐Antoine Bannier, Bernd Wullich, Bernd J. Schmitz‐Dräger, Christian Matek, Johannes Breyer, Ralph M. Wirtz, Charles Maussion, Sebastian Foersch, Niklas Klümper, P Mann, Markus Eckstein, Danijel Sikic, Arndt Hartmann
article en
16 citations

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.

Nature CommunicationsVol. 15(1)
Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Johannes Gutenberg University Mainz (DE), University Hospital Bonn (DE), Universitätsklinikum Erlangen (DE), University Medical Center of the Johannes Gutenberg University Mainz (DE), Pierre Fabre (France) (FR), Comprehensive Cancer Center Erlangen (DE), University of Regensburg (DE)
Else Kröner-Fresenius-Stiftung
Zero hunger
Openalex Percentile: Top 1%
Bladder and Urothelial Cancer Treatments
9.16
DOIOpen Access PDFOpenAlex
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