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

Journal: Cell reports. Medicine
Published Date:

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
    PathAI, Boston, MA, USA.
  • Maryam Pouryahya
    PathAI, Boston, MA, USA.
  • Yevgeniy Gindin
    Gilead Sciences, Inc., Foster City, CA, USA.
  • David Z Pan
    Gilead Sciences, Inc., Foster City, CA, USA.
  • Oscar M Carrasco-Zevallos
    PathAI, Boston, MA, USA.
  • Victoria Mountain
    PathAI, Boston, MA, USA.
  • G Mani Subramanian
    Gilead Sciences, Foster City, CA, USA.
  • Michael C Montalto
    PathAI, Boston, MA.
  • Murray Resnick
    PathAI, Boston, MA.
  • Andrew H Beck
  • Ryan S Huss
    OrsoBio, Palo Alto, CA, USA.
  • Robert P Myers
    Gilead Sciences, Inc., Foster City, CA.
  • Amaro Taylor-Weiner
    Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge.
  • Ilan Wapinski
    PathAI, Boston, MA.
  • Chuhan Chung
    Gilead Sciences, Inc., Foster City, CA.