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

ABSTRACT: 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/T

SUBMITTER: Schmauch B 

PROVIDER: S-EPMC11758823 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature