Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology.

Researchers

Journal

Modalities

Models

Abstract

Patients with hepatocellular carcinoma (HCC) displaying overexpression of immune gene signatures are likely to be more sensitive to immunotherapy, however the use of such signatures in clinical settings remains challenging. We thus aimed, using artificial intelligence (AI) on whole-slide digital histological Images (WSI), to develop models able to predict the activation of 6 immune gene signatures.AI Models were trained and validated in 2 different series of patients with HCC treated by surgical resection. Gene expression was investigated using RNA sequencing or NanoString technology. Three deep-learning approaches were investigated: patch-based, classic MIL and CLAM. Pathological reviewing of the most predictive tissue areas was performed for all gene signatures..The CLAM model showed the best overall performance in the discovery series. Its best-fold areas under the curve (AUC) for the prediction of tumors with upregulation of the immune gene signatures ranged from 0.74 to 0.87. The different models generalized well in the validation dataset with AUCs ranging from 0.81 to 0.91. Pathological analysis of highly predictive tissue areas showed enrichment in inflammatory cells, plasma cells, and neutrophils.We have developed and validated AI based pathology models able to predict the activation of several immune and inflammatory genes signatures. Our approach also provides insights on the morphological features that impact the model predictions. This proof-of-concept study shows that AI based pathology could represent a novel type of biomarker that will ease the translation of our biological knowledge of HCC into clinical practice.Immune and inflammatory gene signatures may be associated with an increased sensitivity to immunotherapy in patients with advanced hepatocellular carcinoma. We show in the present study that the use of artificial intelligence based pathology allows to predict the activation of these signatures directly from histology.Copyright © 2022 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.