Predictive biomarkers for the responsiveness of recurrent glioblastomas to activated killer cell immunotherapy.
Cell Biosci
; 13(1): 17, 2023 Jan 24.
Article
em En
| MEDLINE
| ID: mdl-36694264
ABSTRACT
BACKGROUND:
Recurrent 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.METHODS:
To 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.RESULTS:
Responders 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.CONCLUSION:
We 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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article