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, Hartmann Arndt, Försch Sebastian, Eckstein Markus
Owkin, Paris, France.
Institute of Pathology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Nat Commun. 2024 Dec 30;15(1):10914. doi: 10.1038/s41467-024-55331-6.
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.
成纤维细胞生长因子受体3（FGFR3）中的致病性激活突变驱动尿路上皮癌的疾病维持和进展。10%至15%的肌层浸润性和转移性尿路上皮癌（MIBC/mUC）为FGFR3突变型。用酪氨酸激酶抑制剂（如厄达替尼）选择性靶向FGFR3热点突变已被批准用于mUC，且需要进行FGFR3突变检测。然而，目前的检测方法（聚合酶链反应或下一代测序）需要高质量的组织，周转时间长且费用高昂。为克服这些局限性，我们开发了一种深度学习模型，该模型可使用常规苏木精-伊红染色切片检测FGFR3突变。我们的研究涵盖1222例病例，是对用于MIBC和mUC患者FGFR3突变预筛查模型的大规模验证。在这项工作中，我们证明我们的模型在晚期和转移性病例中实现了高灵敏度（>93%），同时平均减少了40%的分子检测，从而为识别符合FGFR3靶向治疗条件的患者提供了一种经济高效且快速的预筛查工具。
