AI allows pre-screening of FGFR3 mutational status using routine histology slides of muscle-invasive bladder cancer. - Bannier, Pierre Antoine; Saillard, Charlie; Mann, Philipp; Touzot, Maxime; Maussion, Charles; Matek, Christian; Klümper, Niklas; Breyer, Johannes; Wirtz, Ralph; Sikic, Danijel; Schmitz-Dräger, Bernd; Wullich, Bernd; et al. (Nature Communications, 2024)
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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