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/mm(2) 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+, PD1+) using multiplex immunohistochemistry in 62 resected specimens and evaluated gene expression profiles by bulk RNA sequencing in 44 samples.All patients treated with A+B were Child-Pugh A and received first-line A+B treatment for Barcelona Clinic Liver Cancer Stage C HCC (n=77, 75.5%) on a background of viral (n=53, 52%) and non-viral (n=49, 48%) liver disease. Median ICI density was 429.9 (IQR: 194.6-666.7) cells/mm(2) Two-thirds of patients (n=67, 65.7%) had ICI counts≥236/mm(2), derived as the optimal prognostic cut-off (ICI-high). Baseline characteristics, including disease etiology, liver function, performance status, stage, prior therapy and alpha-fetoprotein (AFP) levels, were comparable between ICI-high versus ICI-low patients. Patients with ICI-high demonstrated a significantly longer overall survival (OS) compared with ICI-low: 20.9 (95% CI: 13.8 to 27.9) versus 15.3 (95% CI: 6.0 to 24.6 months, p=0.026). Multivariable analyses demonstrated ICI-low status to remain as an independent prognostic parameter (adjusted HR (aHR): 2.02, 95% CI: 1.03 to 3.96) alongside AFP concentration (per 100 ng/mL: aHR 1.00, 95% CI: 1.00 to 1.00). ICI-high tumors were characterized by STC1 underexpression and enrichment in proinflammatory gene expression sets previously associated with response to immunotherapy. The proinflammatory environment identified by ICI status was not exclusively mediated by T-cell phenotype polarization as shown by a lack of correlation between ICI-high status and CD4+, CD4+FOXP3+, CD8+ and CD8+PD1+ T-cell density.In conclusion, we propose a ML-based algorithm to identify proinflamed HCC TMEs bearing a positive correlation with the patient's OS. Digital characterization of the TME should be validated as a tool to improve precision delivery of anticancer immunotherapy.