Abstract 7377: Deep learning uncovers morphological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients

Abstract Hippo signaling emerged over the last decade as a major tumor-suppressing pathway. Its dysregulation is generally associated with abnormal expression levels of YAP1, WWTR1 (coding for the TAZ protein) and TEAD genes among others. This pathway has been shown to have a prognostic impact in several cancer types. In particular, the role of YAP1/TEAD activity across indications has been emphasized by several recent works, with potential implications on treatment options. Therefore, identifying patients with a deregulated pathway is key for a better clinical impact of the current potential therapies. Recent studies have been able to characterize RNA-seq based signatures of a deregulated Hippo pathway but a reproducible and cost-effective method to measure the activation of the pathway in clinical settings is needed. Here we first evaluate and confirm the robustness of a YAP1/TEAD activity signature recently published by Calvet et al. (RNA-based signature focused on TEAD downstream effectors), to predict the level of activity of this pathway across several cancer types available in TCGA. Our results confirmed that YAP1/TEAD activity is cancer type and subtype specific and that its high activity is correlated with poor prognosis in some of these cancers. We then trained deep learning models to predict YAP1/TEAD gene activation level, from H&E-stained histology slides in various cancer types from The Cancer Genome Atlas. We showed that histological markers associated with dysfunctional Hippo signaling are markers of disease aggressiveness and poor prognosis such as necrosis, poorly differentiated tumor, and inflammation. Altogether our results are opening the avenue of defining image-based biomarkers predictive of the YAP1/TEAD activity which could be used in clinical settings for better inclusion of subgroups of patients for targeted therapeutics against this hallmark of cancer development. Citation Format: 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 Howitt, Olivier Gevaert, Nicolas Girard, Elton Rexhepaj, Iris Valtingojer, Laurent Debussche, Eric Durand, Marion Classe, Katharina Von Loga, Elodie Pronier, Matteo Cesaroni. Deep learning uncovers morphological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7377.