Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients
Summary: 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 impli...
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Elsevier
2025-01-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004224028657 |
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| author | 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 L. Moreira Luis Chiriboga Yuanning Zheng Michael Ozawa Brooke E. Howitt Olivier Gevaert Nicolas Girard Elton Rexhepaj Iris Valtingojer Laurent Debussche Emanuele de Rinaldis Frank Nestle Emmanuel Spanakis Valeria R. Fantin Eric Y. Durand Marion Classe Katharina Von Loga Elodie Pronier Matteo Cesaroni |
| author_facet | 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 L. Moreira Luis Chiriboga Yuanning Zheng Michael Ozawa Brooke E. Howitt Olivier Gevaert Nicolas Girard Elton Rexhepaj Iris Valtingojer Laurent Debussche Emanuele de Rinaldis Frank Nestle Emmanuel Spanakis Valeria R. Fantin Eric Y. Durand Marion Classe Katharina Von Loga Elodie Pronier Matteo Cesaroni |
| author_sort | Benoit Schmauch |
| collection | DOAJ |
| description | Summary: 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. |
| format | Article |
| id | doaj-art-196ce701b91b49b98d1b8c6acb9bfdbc |
| institution | Kabale University |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-196ce701b91b49b98d1b8c6acb9bfdbc2025-01-05T04:28:31ZengElsevieriScience2589-00422025-01-01281111638Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patientsBenoit Schmauch0Vincent Cabeli1Omar Darwiche Domingues2Jean-Eudes Le Douget3Alexandra Hardy4Reda Belbahri5Charles Maussion6Alberto Romagnoni7Markus Eckstein8Florian Fuchs9Aurélie Swalduz10Sylvie Lantuejoul11Hugo Crochet12François Ghiringhelli13Valentin Derangere14Caroline Truntzer15Harvey Pass16Andre L. Moreira17Luis Chiriboga18Yuanning Zheng19Michael Ozawa20Brooke E. Howitt21Olivier Gevaert22Nicolas Girard23Elton Rexhepaj24Iris Valtingojer25Laurent Debussche26Emanuele de Rinaldis27Frank Nestle28Emmanuel Spanakis29Valeria R. Fantin30Eric Y. Durand31Marion Classe32Katharina Von Loga33Elodie Pronier34Matteo Cesaroni35Owkin France, Paris, France; Corresponding authorOwkin France, Paris, FranceOwkin France, Paris, FranceOwkin France, Paris, FranceOwkin France, Paris, France; Corresponding authorOwkin France, Paris, FranceOwkin France, Paris, FranceOwkin France, Paris, FranceBavarian Cancer Research Center (Bayerisches Zentrum für Krebsforschung, BZKF), Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Institute of Pathology, University Hospital Erlangen, Erlangen, GermanyBavarian Cancer Research Center (Bayerisches Zentrum für Krebsforschung, BZKF), Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany; Department of Medicine 1, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, GermanyClaude Bernard University Lyon I & Léon Bérard Cancer Center, Lyon, FranceGrenoble Alpes University and Léon Bérard Cancer Center, Lyon, FranceLéon Bérard Cancer Center, Lyon, FranceCentre de Recherche INSERM LNC-UMR1231, Dijon, FranceCentre de Recherche INSERM LNC-UMR1231, Dijon, France; Cancer Biology Transfer Platform, Centre Georges-François Leclerc, Dijon, France; Genetic and Immunology Medical Institute, Dijon, France; University of Burgundy Franche-Comté, Dijon, FranceCentre de Recherche INSERM LNC-UMR1231, Dijon, France; Cancer Biology Transfer Platform, Centre Georges-François Leclerc, Dijon, France; Genetic and Immunology Medical Institute, Dijon, France; University of Burgundy Franche-Comté, Dijon, FranceDepartment of Cardiothoracic Surgery, New York University Langone Medical Center, New York, NY, USADepartment of Pathology, NYU Langone New York University Langone Medical Center, New York, NY, USADepartment of Pathology, NYU Langone New York University Langone Medical Center, New York, NY, USADepartment of Pathology, Stanford University, Stanford, CA, USADepartment of Pathology, Stanford University, Stanford, CA, USADepartment of Medicine & Biomedical Data Science, Stanford University, Stanford, CA, USADepartment of Medicine & Biomedical Data Science, Stanford University, Stanford, CA, USAInstitut Curie, Paris, FranceSanofi, Paris, FranceSanofi, Paris, FranceSanofi, Paris, FranceSanofi, Cambridge, MA, USASanofi, Cambridge, MA, USASanofi, Paris, FranceSanofi, Cambridge, MA, USAOwkin France, Paris, FranceSanofi, Paris, FranceOwkin France, Paris, FranceOwkin France, Paris, FranceSanofi, Paris, FranceSummary: 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.http://www.sciencedirect.com/science/article/pii/S2589004224028657Health sciencesApplied sciencesMachine learning |
| spellingShingle | 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 L. Moreira Luis Chiriboga Yuanning Zheng Michael Ozawa Brooke E. Howitt Olivier Gevaert Nicolas Girard Elton Rexhepaj Iris Valtingojer Laurent Debussche Emanuele de Rinaldis Frank Nestle Emmanuel Spanakis Valeria R. Fantin Eric Y. Durand Marion Classe Katharina Von Loga Elodie Pronier Matteo Cesaroni Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients iScience Health sciences Applied sciences Machine learning |
| title | Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients |
| title_full | Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients |
| title_fullStr | Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients |
| title_full_unstemmed | Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients |
| title_short | Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients |
| title_sort | deep learning uncovers histological patterns of yap1 tead activity related to disease aggressiveness in cancer patients |
| topic | Health sciences Applied sciences Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2589004224028657 |
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