2024
EPCO-26. LONGITUDINAL SINGLE-CELL ANALYSES IDENTIFY DRIVERS OF GENETIC, EPIGENOMIC, AND CELLULAR EVOLUTION IN IDH-MUTANT GLIOMA
Johnson K, Spitzer A, Varn F, Nomura M, Garofano L, Chowdhury T, Anderson K, D’Angelo F, Bussema L, Gritsch S, Oh Y, Moon H, Paek S, Bielle F, Laurenge A, Di Stefano A, Mathon B, Picca A, Sanson M, Lipsa A, Hertel F, Zhao Z, Wang Q, Jiang T, Hermes B, Sanai N, Golebiewska A, Niclou S, Huse J, Yung W, Lasorella A, Suvà M, Iavarone A, Tirosh I, Verhaak R. EPCO-26. LONGITUDINAL SINGLE-CELL ANALYSES IDENTIFY DRIVERS OF GENETIC, EPIGENOMIC, AND CELLULAR EVOLUTION IN IDH-MUTANT GLIOMA. Neuro-Oncology 2024, 26: viii6-viii7. PMCID: PMC11552777, DOI: 10.1093/neuonc/noae165.0025.Peer-Reviewed Original ResearchIDH-mutant gliomasGenetic alterationsStem-like populationDifferentially accessible peaksChromatin accessibility dataCopy number alterationsCellular hierarchyCycling populationTumor microenvironment cell typesSingle nucleus RNA sequencingCell cycle alterationsMalignant cell differentiationHigh tumor gradeNucleus RNA sequencingDNA sequencesATAC sequencingGenetic analysisCellular statesAccessibility peaksMicroenvironment cell typesReduced differentiationRNA sequencingIntratumoral cellular heterogeneityCellular heterogeneityTumor grade
2023
EPCO-09. CHARACTERIZING THE GBM CELLULAR LANDSCAPE BY LARGE-SCALE SINGLE-NUCLEUS RNA-SEQUENCING
Spitzer A, Nomura M, Garofano L, Johnson K, Nehar-Belaid D, Oh Y, Anderson K, Najac R, Bussema L, Varn F, D’Angelo F, Chowdhury T, Migliozzi S, Park J, Ermini L, Golebiewska A, Niclou S, Das S, Paek S, Moon H, Mathon B, Di Stefano A, Bielle F, Laurenge A, Sanson M, Tanaka S, Saito N, Keir S, Ashley D, Huse J, Yung W, Lasorella A, Verhaak R, Iavarone A, Tirosh I, Suva M. EPCO-09. CHARACTERIZING THE GBM CELLULAR LANDSCAPE BY LARGE-SCALE SINGLE-NUCLEUS RNA-SEQUENCING. Neuro-Oncology 2023, 25: v125-v125. PMCID: PMC10639394, DOI: 10.1093/neuonc/noad179.0473.Peer-Reviewed Original ResearchCellular statesSingle-cell RNA sequencing technologySpecific cellular statesCell typesDNA sequence dataRNA sequencing technologyMalignant cell statesWhole-genome sequencing dataNucleus RNA sequencingRNA sequencing datasetsFunctional enrichment analysisScRNA-seq datasetsScRNA-seq studiesGBM tumor samplesCertain genetic eventsHallmark of glioblastomaCellular landscapeRNA sequencingCell statesEnrichment analysisBaseline expression profilesSequencing dataExpression profilesGlial developmentIntra-tumor heterogeneityEPCO-37. DISSECTING GBM EVOLUTION FOLLOWING STANDARD-OF-CARE BY LARGE-SCALE LONGITUDINAL SINGLE NUCLEUS RNA-SEQUENCING
Nomura M, Spitzer A, Johnson K, Garofano L, Nehar-Belaid D, Oh Y, Anderson K, Najac R, Bussema L, Varn F, D’Angelo F, Chowdhury T, Migliozzi S, Park J, Ermini L, Golebiewska A, Niclou S, Das S, Paek S, Moon H, Mathon B, Di Stefano A, Bielle F, Laurenge A, Sanson M, Tanaka S, Saito N, Keir S, Ashley D, Huse J, Yung W, Lasorella A, Iavarone A, Verhaak R, Suva M, Tirosh I. EPCO-37. DISSECTING GBM EVOLUTION FOLLOWING STANDARD-OF-CARE BY LARGE-SCALE LONGITUDINAL SINGLE NUCLEUS RNA-SEQUENCING. Neuro-Oncology 2023, 25: v132-v132. PMCID: PMC10639295, DOI: 10.1093/neuonc/noad179.0499.Peer-Reviewed Original ResearchSingle-nucleus RNA sequencingLarge-scale longitudinal cohortTME compositionRecurrent samplesGood clinical courseInitial tumor resectionMajority of patientsTumor microenvironment cellsPrimary tumor samplesMGMT methylation statusTME changesClinical courseRNA sequencingTherapy failureLikely respondersTumor resectionDisease progressionNucleus RNA sequencingLongitudinal cohortReciprocal increaseTumor samplesMicroenvironment cellsMalignant cell fractionGlioblastomaRecurrence