Abstract

Abstract Diffuse midline glioma (DMG) is a fatal childhood brain cancer with a survival rate of less than one year from diagnosis. Pharmacological approaches as well as immunotherapy have failed to make a clinical impact. Incomplete understanding of the tumor microenvironment (TME) and tumor associated antigens have contributed to the observed poor prognosis. We hypothesize that characterization of the DMG TME will identify biology-informed targeted treatments. We thus obtained postmortem specimens from 70 patients diagnosed with brain cancers, including 50 DMGs, 20 other types and 10 non-CNS-cancer patients. Up to four anatomical brain locations were selected including the primary tumor, metastatic and adjacent healthy sites. Formalin-fixed-paraffin-embedded (FFPE) specimens were processed for constructing a tissue microarray (TMA). The TMA was stained for a number of markers (H&E, H3K27M, H3K27me3, KI67) and was scored by a neuropathologist. We then used a multiplexed immunofluorescence (MxIF) technology, Cell DIVE™, to iteratively probe 33 biomarkers on a single tissue slide, focusing on immune cell type profiling and activation, and histone mutation status. Analysis of biomarker density and spatial relationships are underway. The main expressed immune markers across all patients were CD163, CD68, and CD8. CD8, a cytotoxic T-cell biomarker, was highly detected in pons, cerebellum, thalamus and the frontal lobe (tumor and healthy) of DMG patients and varied according to clinical intervention, including ONC201 treatment. Furthermore, in comparison to other tumors, DMGs exhibited a higher expression of CD3, T- cell marker, and CD4, a T-helper cell biomarker. Analysis of Iba1, a microglial marker, confirmed a higher difference in microglial activation in primary tumor compared to metastatic and adjacent healthy tissue. In contrast, CD68 was significantly increased in metastatic sites. Analysis is still ongoing. We report establishment of the most comprehensive TMA for pediatric brain tumors, which provides insights into TME comparing critically important clinical variables.

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