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.
Scheiner B, Lombardi P, D'Alessio A, Kim G, Tafavvoghi M, Petrenko O, Goldin RD, Fulgenzi CAM, Torkpour A, Balcar L, Mauri FA, Pomej K, Himmelsbach V, Barsch M, Celsa C, Cabibbo G, Cheon J, Krall A, Hucke F, Di Tommaso L, Bernasconi M, Rimassa L, Samson A, Stefanini B, Mozayani B, Trauner M, Lackner C, Stauber R, Vasuri F, Piscaglia F, Bengsch B, Finkelmeier F, Peck-Radosavljevic M, Rasmussen Busund LT, Marafioti T, Rahbari M, Heikenwalder M, Pinter M, Chon HJ, Rakaee M, Pinato DJ.
Scheiner B, et al.
J Immunother Cancer. 2025 Oct 5;13(10):e010975. doi: 10.1136/jitc-2024-010975.
J Immunother Cancer. 2025.
PMID: 41052886
Free PMC article.
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- …
We correlated ICI signature with characteristics of the T-cell infiltrate (CD4+, FOXP3+, CD8+, PD1+) using multiplex immunohistochemistry in …