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

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

Blog Article

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 ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI EFEKTIVITAS WAKTU PROYEK DAN DAMPAKNYA TERHADAP EFEKTIVITAS BIAYA PROYEK KONSTRUKSI (STUDI KASUS: PT PAN PASIFIC NESIA SUBANG – JAWA BARAT) 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 Relations and transformations of extremum energy principles for deformable body/Deformuojamo kūno ekstreminių energinių principų ryšiai ir transformacijos validation cohorts.Finally, we showed that histological markers of disease aggressiveness were associated with dysfunctional Hippo signaling.