% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Scheiner:305098,
      author       = {B. Scheiner and P. Lombardi and A. D'Alessio and G. Kim and
                      M. Tafavvoghi and O. Petrenko and R. D. Goldin and C. A. M.
                      Fulgenzi and A. Torkpour and L. Balcar and F. A. Mauri and
                      K. Pomej and V. Himmelsbach and M. Barsch and C. Celsa and
                      G. Cabibbo and J. Cheon and A. Krall and F. Hucke and L. Di
                      Tommaso and M. Bernasconi and L. Rimassa and A. Samson and
                      B. Stefanini and B. Mozayani and M. Trauner and C. Lackner
                      and R. Stauber and F. Vasuri and F. Piscaglia and B. Bengsch
                      and F. Finkelmeier and M. Peck-Radosavljevic and L.-T.
                      Rasmussen Busund and T. Marafioti and M. Rahbari$^*$ and M.
                      Heikenwalder$^*$ and M. Pinter and H. J. Chon and M. Rakaee
                      and D. J. Pinato},
      title        = {{P}reliminary 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.},
      journal      = {Journal for ImmunoTherapy of Cancer},
      volume       = {13},
      number       = {10},
      issn         = {2051-1426},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {DKFZ-2025-02050},
      pages        = {e010975},
      year         = {2025},
      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     = {Humans / Carcinoma, Hepatocellular: drug therapy /
                      Carcinoma, Hepatocellular: immunology / Carcinoma,
                      Hepatocellular: pathology / Carcinoma, Hepatocellular:
                      mortality / Liver Neoplasms: drug therapy / Liver Neoplasms:
                      immunology / Liver Neoplasms: pathology / Liver Neoplasms:
                      mortality / Bevacizumab: therapeutic use / Bevacizumab:
                      pharmacology / Male / Female / Antibodies, Monoclonal,
                      Humanized: therapeutic use / Antibodies, Monoclonal,
                      Humanized: pharmacology / Machine Learning / Middle Aged /
                      Antineoplastic Combined Chemotherapy Protocols: therapeutic
                      use / Antineoplastic Combined Chemotherapy Protocols:
                      pharmacology / Aged / Tumor Microenvironment: immunology /
                      Prognosis / Treatment Outcome / Biomarker (Other) /
                      Hepatocellular Carcinoma (Other) / Immunotherapy (Other) /
                      Tumor infiltrating lymphocyte - TIL (Other) / Bevacizumab
                      (NLM Chemicals) / Antibodies, Monoclonal, Humanized (NLM
                      Chemicals) / atezolizumab (NLM Chemicals)},
      cin          = {D440},
      ddc          = {610},
      cid          = {I:(DE-He78)D440-20160331},
      pnm          = {314 - Immunologie und Krebs (POF4-314)},
      pid          = {G:(DE-HGF)POF4-314},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41052886},
      doi          = {10.1136/jitc-2024-010975},
      url          = {https://inrepo02.dkfz.de/record/305098},
}