Introduction. Glioblastoma (GBM) is the most common primary adult brain tumour with a median overall survival (OS) of 12–15 months. Molecular characterization of multiple immunooncology targets in GBM may help target novel immunotherapeutic strategies. We used NanoString GeoMx® Digital Spatial Profiling (DSP) to assess multiple immunooncology protein targets in methylated versus unmethylated IDH-wild-type glioblastoma. Methods. NanoString GeoMx® DSP technology uses multiple primary antibodies conjugated to indexing DNA oligos with a UV photocleavable linker. Tissue regions of interest (ROIs) are selected with bound fluorescent antibodies; oligos are released via a UV-mediated linker and quantitated. We used DSP multiplex analysis of 31 immunooncology proteins and controls (CD4, CD14, CD68, CD8A, B7-H3, PD-L1, CD19, FOXP3, CD44, STAT3 (phospho Y705), CD45, Pan Cytokeratin, MS4A1/CD20, CD45RO, PD1, CD3, beta-2 microglobulin, VISTA, Bcl2, GZMB, PTEN, beta-catenin, CD56, Ki-67, STAT3, AKT, p-Akt, S6, Histone H3, IgG Rabbit control, and Mouse IgG control) from ROIs in a cohort of 10 IDH-wild-type glioblastomas (5 methylated and 5 unmethylated). An nCounter platform allowed quantitative comparisons of antibodies between ROIs in MGMT methylated and unmethylated tumours. Mean protein expression counts between methylated and unmethylated GBM were compared using technical and biological replicates. Results. The analysis showed 10/27 immunooncology target proteins were significantly increased in methylated versus unmethylated IDH-wild-type glioblastoma tumour core (false discovery rate (FDR) <0.1 by Benjamini–Hochberg procedure). Conclusions. NanoString GeoMx® DSP was used to analyse multiple immunooncology protein target expression in methylated versus unmethylated IDH-wild-type glioblastoma. In this small study, there was a statistical increase in CD4, CD14, CD68, CD8A, B7-H3, PDL-1, CD19, FOXP3, CD44, and STAT3 protein expression in methylated versus unmethylated GBM tumour core; however, this requires larger cohort validation. Advanced multiplex immunooncological biomarker analysis may be useful in identifying biomarkers for novel immunotherapeutic agents in GBMs.

Introduction Glioblastoma (GBM) is the most common primary adult brain tumour with 12–15 months median overall survival. Molecular characterization of multiple immuno-onology targets in GBM is required to optimise immunotherapeutic strategies. Advanced molecular characterisation using Digital Spatial Profiling Technology allows assessment of multiple immuno-oncology protein targets in methylated compared with unmethylated Glioblastoma IDH-wildtype. Methods Nanostring DSP uses multiple primary antibodies conjugated to indexing DNA oligos with a UV photocleavable linker. Tissue Regions of interest (ROI) are selected with bound fluorescent antibodies, oligos are released via a UV-mediated linker and quantitated. We used DSP multiplex analysis of 31 immuno-oncology proteins and controls (PD-1, PD-L1, B7-H3, VISTA, CD45, CD45RO, CD3, MS4A1 (CD20), CD4, CD8A, CD68, STAT3, STAT3 (Phopshory705), GZMB, Beta-2-microglobulin, CD56, Beta-catenin, FOXP3, CD14, CD19, AKT, P-AKT, PTEN, Bcl-2, CD44, Histone H3, S6, IgG Rabbit Control, Mouse IgG Control, Pan-Cytokeratin, Ki67) from ROIs in a cohort of 10 Glioblastomas IDH-wildtype (5 methylated, 5 unmethylated). An nCounter platform allowed quantitative comparisons of antibodies between ROIs in MGMT methylated and unmethylated tumours. Mean protein expression counts between methylated and unmethylated GBM were compared using a linear mixed effect model for technical and biological replicates. Results GeoMx DSP showed 10/27 immuno-oncology target proteins were significantly increased in methylated versus unmethylated Glioblastoma IDH-wildtype (false discovery rate FDR <0.1 by Benjamini-Hocheberg Procedure). Conclusions Increased immuno-oncology protein target expression in methylated versus unmethylated glioblastoma IDH-wildtype using DSP platform has been identified. Advanced multiplex immuno-oncological biomarker analysis is required to identify predictive biomarkers for novel immunotherapeutic agents in GBMs.

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