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

Published:February 07, 2022DOI:https://doi.org/10.1016/j.jhep.2022.01.018

      Highlights

      • AI-based pathology can predict the activation of immune gene signatures directly from hepatocellular carcinoma histology.
      • Our models generalize well in an independent series of samples with different gene expression profiling technology and staining protocols.
      • These approaches could represent a novel type of biomarker.

      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.

      Graphical abstract

      Keywords

      Linked Article

      • Translating artificial intelligence from code to bedside: The road towards AI-driven predictive biomarkers for immunotherapy of hepatocellular carcinoma
        Journal of HepatologyVol. 77Issue 1
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          Hepatocellular carcinoma (HCC) is the second most common cause of cancer-related death worldwide with growing incidence rates particularly affecting North America and Western Europe.1,2 This increase in incidence rates is primarily the result of sequelae of hepatitis C viral infections (which are now curable), and the growing rates of non-alcoholic steatohepatitis (NASH) associated with diabetes and obesity.3 The last decade has seen rapid growth in the therapeutic options available for HCC. As such, increasingly data-driven approaches to surgical resection and orthotopic liver transplantation, as well as modern image-guided locoregional therapies, have dramatically improved outcomes in populations with early and intermediate stage disease.
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