Integration of deep learning-based histopathology and transcriptomics reveals key genes associated with fibrogenesis in patients with advanced NASH.

Abstract

Nonalcoholic steatohepatitis (NASH) is the most common chronic liver disease globally and a leading cause for liver transplantation in the US. Its pathogenesis remains imprecisely defined. We combined two high-resolution modalities to tissue samples from NASH clinical trials, machine learning (ML)-based quantification of histological features and transcriptomics, to identify genes that are associated with disease progression and clinical events. A histopathology-driven 5-gene expression signature predicted disease progression and clinical events in patients with NASH with F3 (pre-cirrhotic) and F4 (cirrhotic) fibrosis. Notably, the Notch signaling pathway and genes implicated in liver-related diseases were enriched in this expression signature. In a validation cohort where pharmacologic intervention improved disease histology, multiple Notch signaling components were suppressed.

• Authors: Jake Conway, Maryam Pouryahya, Yevgeniy Gindin, David Z Pan, Oscar M Carrasco-Zevallos, Victoria Mountain, G Mani Subramanian, Michael C Montalto, Murray Resnick, Andrew H Beck, Ryan S Huss, Robert P Myers, Amaro Taylor-Weiner, Ilan Wapinski, Chuhan Chung
• Journal: Cell reports. Medicine (Cell Rep Med) Vol. 4 Issue 4 Pg. 101016 (04 18 2023) ISSN: 2666-3791 [Electronic] United States
• PMID: 37075704 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: Copyright © 2023. Published by Elsevier Inc.

Topics

• Humans
• Non-alcoholic Fatty Liver Disease (complications)
• Transcriptome (genetics)
• Deep Learning
• Disease Progression
• Liver Cirrhosis (genetics, drug therapy)