Abstract BACKGROUND Identifying tumor rejection antigens continues to be a significant challenge in the development of effective immunotherapies and their application to pediatric brain tumors. METHODS To identify candidate antigen targets for Medulloblastoma (primary MB, n=170; recurrent MB, n=21), Diffuse Intrinsic Pontine Glioma (DIPG, n=10), and Pediatric High-Grade Astrocytoma (HGA, n=12) for adoptive cellular therapy, we conducted a comprehensive immunogenomic analysis of the transcription profiles of the most aggressive pediatric brain tumors and performed in silico antigen prediction across a wide range of antigen classes. IMPORTANCE OF THE STUDY Pediatric brain tumors are heterogeneous diseases, and current standard treatments do not adequately address this variability. Newer and safer patient-specific treatment approaches are needed for high-risk patients who do not respond to standard therapies. Cellular immunotherapy could be crucial for improving survival and reducing morbidity. For an effective immune response against these brain tumors, appropriate tumor antigens must be targeted. While subgroup-specific genetic mutations in medulloblastoma patients have been reported, the immunogenicity of these genetic alterations remains unknown. Using a custom antigen prediction pipeline, we identified potential tumor rejection antigens with significant implications for the development of cellular therapies for MB, DIPG, and HGA. RESULTS Antigen prediction analysis revealed that most patients express few candidate neoantigen targets that pass all filtering criteria. Overall, the proportion of predicted tumor-associated antigens (TAAs) was higher than that of neoantigens and gene fusions for all molecular subtypes, except for SHH, which exhibited a higher neoantigen burden. Notably, cancer-testis antigens and previously unrecognized neurodevelopmental antigens were found to be expressed in most patients across all medulloblastoma subgroups. Although the absolute immune cell content is predicted to be low, immune gene-signature analysis revealed subgroup-specific differences.
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