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+, 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/mm² Two-thirds of patients (n=67, 65.7%) had ICI counts≥236/mm², 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.

Keywords: Biomarker; Hepatocellular Carcinoma; Immunotherapy; Tumor infiltrating lymphocyte - TIL.

Figure 1. (A) Study flowchart demonstrating the different study cohorts and evaluated endpoints as well as comparison of (B) overall survival (OS) and (C) progression-free survival (PFS) between patients with ICI high versus ICI low in the A+B cohort. CPS, Child-Pugh score; ECOG, Eastern Cooperative Oncology Group; HCC, hepatocellular carcinoma; ICI, immune cell infiltrate cells/mm².

Figure 2. (A) Scheme of the RNA sequencing data analysis. The analysis strategy included transcriptomic profiling to identify genes and pathways differentially expressed between patients with ICI-high and ICI-low (left). The second part (right) used weighted gene coexpression network analysis to identify genes whose expression changes with ICI abundance. (B) Principal component analysis and differentially expressed genes between ICI-high and ICI-low. The genes with the highest significance and Log2 fold changes are labeled. (C) Gene set analysis (GO:BP) of differentially expressed genes between ICI-high and ICI-low. AKT, protein kinase B; BCLC, Barcelona Clinic Liver Cancer; DEGs, differentially expressed genes; E2F, early region 2 binding factor; ICI, immune cell infiltrate cells/mm²; log₂FC, log fold change; MTOR, mechanistic target of rapamycin; MYC, MYC proto-oncogene; NES, Normalized Enrichment Score; NFKB, nuclear factor kappa-light-chain enhancer of activated B cells; PC, principal component; PI3K, phosphatidylinositol 3-kinase; TGF BETA, transforming growth factor beta; TNFA, tumor necrosis factor alpha; UV RESPONSE DN, collection of genes downregulated following exposure to ultraviolet radiation.