Schmauch Benoit, Cabeli Vincent, Domingues Omar Darwiche, Le Douget Jean-Eudes, Hardy Alexandra, Belbahri Reda, Maussion Charles, Romagnoni Alberto, Eckstein Markus, Fuchs Florian, Swalduz Aurélie, Lantuejoul Sylvie, Crochet Hugo, Ghiringhelli François, Derangere Valentin, Truntzer Caroline, Pass Harvey, Moreira Andre L, Chiriboga Luis, Zheng Yuanning, Ozawa Michael, Howitt Brooke E, Gevaert Olivier, Girard Nicolas, Rexhepaj Elton, Valtingojer Iris, Debussche Laurent, de Rinaldis Emanuele, Nestle Frank, Spanakis Emmanuel, Fantin Valeria R, Durand Eric Y, Classe Marion, Von Loga Katharina, Pronier Elodie, Cesaroni Matteo
Owkin France, Paris, France.
Bavarian Cancer Research Center (Bayerisches Zentrum für Krebsforschung, BZKF), Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Institute of Pathology, University Hospital Erlangen, Erlangen, Germany.
iScience. 2024 Dec 20;28(1):111638. doi: 10.1016/j.isci.2024.111638. eCollection 2025 Jan 17.
Over the last decade, Hippo signaling has emerged as a major tumor-suppressing pathway. Its dysregulation is associated with abnormal expression of and -family genes. Recent works have highlighted the role of YAP1/TEAD activity in several cancers and its potential therapeutic implications. Therefore, identifying patients with a dysregulated Hippo pathway is key to enhancing treatment impact. Although recent studies have derived RNA-seq-based signatures, there remains a need for a reproducible and cost-effective method to measure the pathway activation. In recent years, deep learning applied to histology slides have emerged as an effective way to predict molecular information from a data modality available in clinical routine. Here, we trained models to predict YAP1/TEAD activity from H&E-stained histology slides in multiple cancers. The robustness of our approach was assessed in seven independent validation cohorts. Finally, we showed that histological markers of disease aggressiveness were associated with dysfunctional Hippo signaling.
在过去十年中，河马信号通路已成为一种主要的肿瘤抑制途径。其失调与 和 -家族基因的异常表达有关。最近的研究突出了YAP1/TEAD活性在几种癌症中的作用及其潜在的治疗意义。因此，识别河马信号通路失调的患者是提高治疗效果的关键。尽管最近的研究已经得出基于RNA测序的特征，但仍需要一种可重复且经济高效的方法来测量该信号通路的激活情况。近年来，应用于组织学切片的深度学习已成为从临床常规可用的数据模式预测分子信息的有效方法。在这里，我们训练模型从多种癌症的苏木精和伊红（H&E）染色组织学切片中预测YAP1/TEAD活性。我们方法的稳健性在七个独立的验证队列中进行了评估。最后，我们表明疾病侵袭性的组织学标志物与功能失调的河马信号通路有关。
