Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients

Schmauch B, Cabeli V, Domingues OD, Le Douget JE, Hardy A, Belbahri R, Maussion C, Romagnoni A, Eckstein M, Fuchs F, Swalduz A, Lantuejoul S, Crochet H, Ghiringhelli F, Derangere V, Truntzer C, Pass H, Moreira AL, Chiriboga L, Zheng Y, Ozawa M, Howitt BE, Gevaert O, Girard N, Rexhepaj E, Valtingojer I, Debussche L, de Rinaldis E, Nestle F, Spanakis E, Fantin VR, Durand EY, Classe M, Von Loga K, Pronier E, Cesaroni M (2025)

Publication Type: Journal article

Publication year: 2025

Journal

iScience Elsevier

Book Volume: 28

Article Number: 111638

Journal Issue: 1

DOI: 10.1016/j.isci.2024.111638

Abstract

Over the last decade, Hippo signaling has emerged as a major tumor-suppressing pathway. Its dysregulation is associated with abnormal expression of YAP1 and TEAD-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.

Authors with CRIS profile

Markus Eckstein Institute of Pathology

Involved external institutions

Owkin FR France (FR) New York University (NYU) US United States (USA) (US) Bayerisches Zentrum für Krebsforschung (BZKF) DE Germany (DE) Université Claude Bernard Lyon 1 (UCB) FR France (FR) Centre Léon-Bérard (UNICANCER) FR France (FR) Université de Bourgogne (uB) / University of Burgundy / University of Dijon FR France (FR) Sanofi-Aventis Groupe FR France (FR) Stanford University US United States (USA) (US) Institut Curie FR France (FR)

How to cite

APA:

Schmauch, B., Cabeli, V., Domingues, O.D., Le Douget, J.E., Hardy, A., Belbahri, R.,... Cesaroni, M. (2025). Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients. iScience, 28(1). https://doi.org/10.1016/j.isci.2024.111638

MLA:

Schmauch, Benoit, et al. "Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients." iScience 28.1 (2025).

BibTeX: Download