Integration of deep learning-based histopathology and transcriptomics reveals key genes associated with fibrogenesis in patients with advanced NASH
Jake R. Conway, Maryam Pouryahya, Yevgeniy Gindin, David Z. Pan, Oscar Carrasco‐Zevallos, Vicki Mountain, G. Mani Subramanian, Michael Montalto, Murray B. Resnick, Andrew H. Beck, Ryan S. Huss, Robert P. Myers, Amaro Taylor‐Weiner, Ilan Wapinski, Chuhan Chung
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.