ArticleCell & bioscience2023
Predictive biomarkers for the responsiveness of recurrent glioblastomas to activated killer cell immunotherapy.
Article in Cell & bioscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- Phase I Evaluation of Patients with Newly Diagnosed Glioblastoma Treated with Radiation, Nivolumab, and IDO1 Enzyme Inhibitor BMS-986205.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Natural Killer Cell Dysregulation During ALS Disease Progression: A Gene Expression Analysis.Neurology open access · 2026Article
- Early classical swine fever virus persistence is associated with cytotoxic and immunoregulatory transcriptional signatures distinct from protective C-strain vaccination.Frontiers in cellular and infection microbiology · 2026Article
- Review
- Establishing a new-onset diabetes-related metabolism signature for predicting the prognosis and immune landscape in pancreatic cancer.Carcinogenesis · 2025Article
- The Role of the Dysregulation of circRNAs Expression in Glioblastoma Multiforme.Journal of molecular neuroscience : MN · 2025Review
- NK Cell Degranulation Triggered by Rituximab Identifies Potential Markers of Subpopulations with Enhanced Cytotoxicity toward Malignant B Cells.International journal of molecular sciences · 2024Article
- Whole-Genome Resequencing Reveals Genetic Diversity and Growth Trait-Related Genes in Pinan Cattle.Animals : an open access journal from MDPI · 2024Article
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
backgroundRecurrent glioblastoma multiforme (GBM) is a highly aggressive primary malignant brain tumor that is resistant to existing treatments. Recently, we reported that activated autologous natural killer (NK) cell therapeutics induced a marked increase in survival of some patients with recurrent GBM.
methodsTo identify biomarkers that predict responsiveness to NK cell therapeutics, we examined immune profiles in tumor tissues using NanoString nCounter analysis and compared the profiles between 5 responders and 7 non-responders. Through a three-step data analysis, we identified three candidate biomarkers (TNFRSF18, TNFSF4, and IL12RB2) and performed validation with qRT-PCR. We also performed immunohistochemistry and a NK cell migration assay to assess the function of these genes.
resultsResponders had higher expression of many immune-signaling genes compared with non-responders, which suggests an immune-active tumor microenvironment in responders. The random forest model that identified TNFRSF18, TNFSF4, and IL12RB2 showed a 100% accuracy (95% CI 73.5-100%) for predicting the response to NK cell therapeutics. The expression levels of these three genes by qRT-PCR were highly correlated with the NanoString levels, with high Pearson's correlation coefficients (0.419 (TNFRSF18), 0.700 (TNFSF4), and 0.502 (IL12RB2)); their prediction performance also showed 100% accuracy (95% CI 73.54-100%) by logistic regression modeling. We also demonstrated that these genes were related to cytotoxic T cell infiltration and NK cell migration in the tumor microenvironment.
conclusionWe identified TNFRSF18, TNFSF4, and IL12RB2 as biomarkers that predict response to NK cell therapeutics in recurrent GBM, which might provide a new treatment strategy for this highly aggressive tumor.
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Registered trials
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