June 14, 2024

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


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 RNAseq-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.

Benoit Schmauch
Vincent Cabeli
Omar Darwiche-Domingues
Jean-Eudes Le Douget
Alexandra Hardy
Reda Belbahri
Charles Maussion
Alberto Romagnoni
Markus Eckstein
Florian Fuchs
Aurélie Swalduz
Sylvie Lantuejoul
Hugo Crochet
François Ghiringhelli
Valentin Derangere
Caroline Truntzer
Harvey Pass
Andre Moreira
Luis Chiriboga
Yuanning Zheng
Michael Ozawa
Brooke E. Howitt
Olivier Gevaert
Prof. Nicolas Girard, MD, PhD
Elton Rexhepaj
Iris Valtingojer
Laurent Debussche
Emanuele de Rinaldis
Frank Nestle
Emmanuel Spanakis
Valeria R. Fantin
Eric Durand, PhD
Marion Classe
Katharina Von Loga
Elodie Pronier
Matteo Cesaroni