Abstract

Abstract With the success of immunotherapy in cancer, understanding the tumor immune microenvironment (TIME) has become increasingly important; however in pediatric brain tumors this remains poorly characterized. Accordingly, we developed a clinical immune-oncology gene expression assay, including the 18-gene tumor inflammation signature (TIS) as a marker of overall immune activation, and validated this with immunohistochemistry for key cell-type markers. We used this assay, in conjunction with public data from the Pediatric Brain Tumor Atlas (PBTA), to profile a diverse range of 1382 samples in total with detailed clinical and molecular annotation. Overall among the main types of pediatric brain tumors, low-grade gliomas (LGG) had the highest inflammation levels (regardless of genetic driver alteration) and medulloblastomas the lowest. In LGG we identified three distinct patterns of immune activation with prognostic significance in BRAF V600E-mutant tumors, specifically that higher inflammation predicted worse outcomes. In high-grade gliomas (HGG), we observed immune activation and T-cell infiltrates in tumors that have historically been considered exclusively immune cold. There were genomic correlates of inflammation levels, with BRAF-V600E-mutant HGG having the highest inflammation levels and suggesting these may be candidates for combination MEK inhibition and immunotherapy. In mismatch repair deficient HGG, we found that high TIS was a significant predictor of response to immune checkpoint inhibition, and demonstrated the potential for multimodal biomarkers (TIS plus tumor mutation burden) to improve treatment stratification. Importantly, while overall patterns of immune activation were observed for histologically and genetically defined tumor types, there was significant variability within each entity, indicating that the TIME must be evaluated as an independent feature from diagnosis. In sum, in addition to the histology and molecular profile, this work underscores the importance of reporting on the TIME as an essential axis of cancer diagnosis in the era of personalized medicine.

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