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 <i>YAP1</i> and <i>TEAD</i>-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.

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Publication Details

Journal
iScience
Published
2024-12-20
DOI
https://doi.org/10.1016/j.isci.2024.111638
Citations
2
Primary Topic
Hippo pathway signaling and YAP/TAZ
Type
article
Field-Weighted Citation Impact
0.65

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article

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

Benoît Schmauch, Alberto Romagnoni, Caroline Truntzer, Iris Valtingojer et al.
2 citations
iScience
Hippo pathway signaling and YAP/TAZ
0.65
article

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

Benoît Schmauch, Alberto Romagnoni, Caroline Truntzer, Iris Valtingojer, Matteo Cesaroni, Elodie Pronier, Aurélie Swalduz, Florian Fuchs, Frank O. Nestlé, Brooke E. Howitt, François Ghiringhelli, Luis Chiriboga, Laurent Debüssche, Vincent Cabeli, Sylvie Lantuéjoul, Alexandra Hardy, Éric Durand, Valeria R. Fantin, Katharina von Loga, Emanuele de Rinaldis, Hugo Crochet, Charles Maussion, Valentin Dérangère, Marion Classe, Olivier Gevaert, Elton Rexhepaj, Jean-Eudes Le Douget, Yuanning Zheng, Markus Eckstein, Emmanuel Spanakis, Nicolas M Girard, Harvey I. Pass, Reda Belbahri, André Moreira, Michael G. Ozawa, Omar Darwiche Domingues
article en
2 citations

Abstract

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

iScienceVol. 28(1)
Université Claude Bernard Lyon 1 (FR), Inserm (FR), Université de Bourgogne (FR), Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Sanofi (France) (FR), NYU Langone Health (US), Universitätsklinikum Erlangen (DE), Centre Georges François Leclerc (FR), Centre Léon Bérard (FR), Sanofi (United States) (US), Genomic and Immunotherapy Medical Institute (FR), Centre de Recherche en Cancérologie de Lyon (FR), Centre de recherche Translationnelle en Médecine moléculaire (FR), Comprehensive Cancer Center Erlangen (DE), Institut Curie (FR), Université Grenoble Alpes (FR), Stanford University (US)
Amgen, Eli Lilly and Company, Pfizer, AstraZeneca, Sanofi, Gilead Sciences, Astellas Pharma, Ipsen, Daiichi Sankyo Europe, Cilag
Openalex Percentile: Top 30%
Hippo pathway signaling and YAP/TAZ
0.65
DOIOpen Access PDFOpenAlex
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