Preliminary qualification of a machine learning-based assessment of the tumor immune infiltrate as a predictor of outcome in patients with hepatocellular carcinoma treated with atezolizumab plus bevacizumab.

ABSTRACT: Spontaneously immunogenic hepatocellular carcinoma (HCC), identified by a dense immune cell infiltrate (ICI), responds better to immunotherapy, although no validated biomarker exists to identify these cases. We used machine learning (ML) to quantify ICI from standard H&E-stained tissue and evaluated its correlation with characteristics of the tumor microenvironment (TME) and clinical outcome from atezolizumab plus bevacizumab (A+B).We therefore employed a supervised ML algorithm on 102 pretreatment H&E slides collected from patients treated with A+B. We quantified tumor, stroma and immune cell counts/mm² and dichotomized patients into ICI high and ICI low for clinicopathologic analysis. We correlated ICI signature with characteristics of the T-cell infiltrate (CD4+, FOXP3+, CD8+

SUBMITTER: Scheiner B 

PROVIDER: S-EPMC12506175 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature