Predictive biomarkers for the responsiveness of recurrent glioblastomas to activated killer cell immunotherapy

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.