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

Cell Reports Medicine


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.
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Jake Conway,1,4 Maryam Pouryahya,1,4 Yevgeniy Gindin,2 David Z. Pan,2 Oscar M. Carrasco-Zevallos,1 Victoria Mountain,1 G. Mani Subramanian,3 Michael C. Montalto,1 Murray Resnick,1 Andrew H. Beck,1 Ryan S. Huss,3 Robert P. Myers,3 Amaro Taylor-Weiner,1 Ilan Wapinski,1,5,* and Chuhan Chung2,*

1PathAI, Boston, MA, USA
2Gilead Sciences, Inc., Foster City, CA, USA
3OrsoBio, Palo Alto, CA, USA
4These authors contributed equally
5Lead contact
*Correspondence: [email protected] (I.W.), [email protected] (C.C.)