Predictive Biomarkers for the Responsiveness of Recurrent Glioblastomas to Activated Killer Cell Immunotherapy

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 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 an 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 therapeutics. The expression levels of these three genes by qRT-PCR were highly correlated with the NanoString levels, with high Pearsons 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. ConclusionsWe 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.