| Title: |
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 |
| Authors: |
Scheiner, Bernhard; Lombardi, Pasquale; D'Alessio, Antonio; Kim, Gwangil; Tafavvoghi, Masoud; Petrenko, Oleksandr; Goldin, Robert D; Fulgenzi, Claudia A M; Torkpour, Aria; Balcar, Lorenz; Mauri, Francesco A; Pomej, Katharina; Himmelsbach, Vera; Barsch, Maryam; Celsa, Ciro; Cabibbo, Giuseppe; Cheon, Jaekyung; Krall, Anja; Hucke, Florian; Di Tommaso, Luca; Bernasconi, Monica; Rimassa, Lorenza; Samson, Adel; Stefanini, Bernardo; Mozayani, Behrang; Trauner, Michael; Lackner, Carolin; Stauber, Rudolf; Vasuri, Francesco; Piscaglia, Fabio; Bengsch, Bertram; Finkelmeier, Fabian; Peck-Radosavljevic, Markus; Rasmussen Busund, Lill-Tove; Marafioti, Teresa; Rahbari, Mohammad; Heikenwalder, Mathias; Pinter, Matthias; Chon, Hong Jae; Rakaee, Mehrdad; Pinato, David J |
| Contributors: |
Scheiner, B.; Lombardi, P.; D'Alessio, A.; Kim, G.; Tafavvoghi, M.; Petrenko, O.; Goldin, R.D.; Fulgenzi, C.A.M.; Torkpour, A.; Balcar, L.; Mauri, F.A.; 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, L.; Marafioti, T.; Rahbari, M.; Heikenwalder, M.; Pinter, M.; Chon, H.J.; Rakaee, M.; Pinato, D.J. |
| Publisher Information: |
BMJ Publishing Group |
| Publication Year: |
2025 |
| Collection: |
IRIS Università degli Studi di Palermo |
| Subject Terms: |
Biomarker; Hepatocellular Carcinoma; Immunotherapy; Tumor infiltrating lymphocyte - TIL |
| Description: |
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 100ng/mL: aHR 1.00, 95%CI: 1.00 to 1.00). ICI-high tumors were characterized by STC1 underexpression and enrichment in proinflammatory ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/pmid/41052886; info:eu-repo/semantics/altIdentifier/wos/WOS:001587245500001; volume:13; issue:10; numberofpages:10; journal:JOURNAL FOR IMMUNOTHERAPY OF CANCER; https://hdl.handle.net/10447/691889 |
| DOI: |
10.1136/jitc-2024-010975 |
| Availability: |
https://hdl.handle.net/10447/691889; https://doi.org/10.1136/jitc-2024-010975 |
| Rights: |
info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.BB4FD7 |
| Database: |
BASE |