Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology.
artificial intelligence
deep learning
immune gene signatures
pathology
whole slide image
Journal
Journal of hepatology
ISSN: 1600-0641
Titre abrégé: J Hepatol
Pays: Netherlands
ID NLM: 8503886
Informations de publication
Date de publication:
07 2022
07 2022
Historique:
received:
29
07
2021
revised:
17
12
2021
accepted:
17
01
2022
pubmed:
11
2
2022
medline:
22
6
2022
entrez:
10
2
2022
Statut:
ppublish
Résumé
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, 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 receiver operating characteristic curves (AUCs) for the prediction of tumors with upregulation of the immune gene signatures ranged from 0.78 to 0.91. The different models generalized well in the validation dataset with AUCs ranging from 0.81 to 0.92. Pathological analysis of highly predictive tissue areas showed enrichment in lymphocytes, plasma cells, and neutrophils. We have developed and validated AI-based pathology models able to predict the activation of several immune and inflammatory gene signatures. Our approach also provides insights into 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 increased sensitivity to immunotherapy in patients with advanced hepatocellular carcinoma. In the present study, the use of artificial intelligence-based pathology enabled us to predict the activation of these signatures directly from histology.
Sections du résumé
BACKGROUND & AIMS
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, to develop models able to predict the activation of 6 immune gene signatures.
METHODS
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.
RESULTS
The CLAM model showed the best overall performance in the discovery series. Its best-fold areas under the receiver operating characteristic curves (AUCs) for the prediction of tumors with upregulation of the immune gene signatures ranged from 0.78 to 0.91. The different models generalized well in the validation dataset with AUCs ranging from 0.81 to 0.92. Pathological analysis of highly predictive tissue areas showed enrichment in lymphocytes, plasma cells, and neutrophils.
CONCLUSION
We have developed and validated AI-based pathology models able to predict the activation of several immune and inflammatory gene signatures. Our approach also provides insights into 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.
LAY SUMMARY
Immune and inflammatory gene signatures may be associated with increased sensitivity to immunotherapy in patients with advanced hepatocellular carcinoma. In the present study, the use of artificial intelligence-based pathology enabled us to predict the activation of these signatures directly from histology.
Identifiants
pubmed: 35143898
pii: S0168-8278(22)00031-9
doi: 10.1016/j.jhep.2022.01.018
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
116-127Commentaires et corrections
Type : CommentIn
Informations de copyright
Copyright © 2022 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Conflicts of interest JC consults for Crosscope, Keen Eye, and has received research funding from Fondation Bristol Myers Squibb pour la Recherche en Immuno-Oncologie. Please refer to the accompanying ICMJE disclosure forms for further details.