TB-1 ADDITIONAL GENETIC ALTERATIONS DIFFERENTIALLY ALTER THE TRANSCRIPTOMIC LANDSCAPE OF H3 K27M-MUTANT DIFFUSE MIDLINE GLIOMA

Abstract

Abstract Histone H3 K27M mutation is a hallmark mutation for H3 K27M-mutant diffuse midline glioma (DMG), but targeting this mutation has yet to achieve a significant survival benefit in clinical trials. Recent analyses revealed alterations in several genes, such as NF1 and PDGFRA, are observed in substantial subpopulations of H3 K27M-mutant DMG patients in addition to H3 mutation and recurrent TP53 mutations, indicating patient-to-patient tumor heterogeneity and the potential necessity of tailored target therapy for the treatment of this disease. Here, using our human induced pluripotent stem cells (iPSC)-derived glioma avatar platform, we designed DMG models by introducing TP53R248Q with or without heterozygous H3 K27M mutation in combination with further genetic modifications of either NF1 knockout or PDGFRAD842V overexpression to recapitulate DMG subpopulations. Mice with TP53R248Q; H3F3AK27M (QM) tumors survived significantly longer than those harboring QM;NF1-/- (QMN) tumors and QM; PDGFRAD842Voe (QMP) tumors. RNA-sequencing of those induced DMG (iDMG) neurospheres revealed altered patterns of upregulation of MAPK pathway genes both in QMN and QMP-iDMG neurospheres compared to their H3 wildtype counterparts with the same combinations of genetic alterations, suggesting that those additional mutations modifies the oncogenic signaling associated with H3 K27M mutation. Further, differential expression analysis comparing QMN and QMP-iDMG neurospheres revealed 405 differentially expressed genes. Gene set enrichment analysis showed upregulation of transcriptional programs related to mesenchymal signature in QMN-iDMG neurospheres and proneural signature in QMP-iDMG neurosphere as expected. These data show that NF1 deletion and PDGFRAD842V overexpression significantly alter gene expression in H3 K27M-mutant iDMG tumors, potentially opening up a new therapeutic avenue in these devastating tumors with patient-to-patient heterogeneity. Further work using these models will shed light on the development of tailored therapy based on detailed genetic information on each patient sample, such as combining targeted kinase inhibition with HDAC inhibitors that have shown promise in the clinic.