Integration of Deep Learning-based Histopathology and Transcriptomics Reveals Key Genes Associated with Fibrogenesis in Patients with Advanced NASH

Overview

Journal Cell Rep Med

Publisher Cell Press

Specialties Biology
General Medicine

Date 2023 Apr 19

PMID 37075704

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

Affiliations

Soon will be listed here.

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.

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