TMOD-07. HUMAN DIFFUSE MIDLINE GLIOMA AVATARS AS A PLATFORM TO SEARCH FOR NOVEL THERAPEUTIC TARGETS

Abstract

Abstract Diffuse midline glioma is the leading cause of brain tumor death among the pediatric population. Drugs that show notable promise in preclinical models inevitably fail to demonstrate efficacy in clinical trials, likely due to the inadequacy of preclinical models. We have recently proposed glioblastoma models derived from human induced pluripotent stem cells (hiPSCs) genetically engineered with different combinations of glioblastoma-associated genetic alterations as a platform to search for therapeutic targets. These glioblastoma avatars authentically recapitulated the different pathobiology of glioblastoma subtypes, depending on what genetic alterations to be introduced. To investigate the biology and to develop novel therapeutics for diffuse midline glioma with H3K27M mutation, we have established a novel model by introducing H3.3 K27M mutation together with one of the most common concurrent genetic alterations, TP53 R248Q mutation, into hiPSCs through CRISPR/Cas9 genome engineering. Orthotopic engraftment of the neural progenitor cells derived from these edited hiPSCs formed diffusely invasive brainstem tumors with histological features of the diffuse midline glioma. These tumor avatars presented a global reduction in H3K27me3 accompanied by the expression of H3K27M. Transcriptome analyses of these models revealed that these avatars with H3K27M cluster apart from the pediatric glioma samples without this particular mutation, and that they present signatures of oligodendroglial progenitor differentiation as discovered in patient samples with this mutation. Using these models faithfully recapitulating histology and pathobiology of the patient tumors, we have performed drug screening and confirmed that their sensitivity to known drugs, including an EZH2 inhibitor and histone deacetylase inhibitors. On these faithful human avatars of diffuse midline glioma with H3K27M, we have applied bioinformatics algorithms of drug sensitivity prediction aiming at developing novel therapeutics for this devastating pediatric glioma.