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

Fiche publication

Date publication

janvier 2025

Journal

iScience

Auteurs

Membres identifiés du Cancéropôle Est :
Pr GHIRINGHELLI François , Mme TRUNTZER Caroline , Dr DERANGERE Valentin

Tous les auteurs :
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

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/39868035

Résumé

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.