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Spatio-temporal evolution of the primary glioblastoma genome (aCGH)

ABSTRACT: Tumor recurrence following a standard treatment is the major cause of mortality for glioblastoma (GBM) patients. However, insights on the evolutionary process of the tumor have been limited due to the lack of longitudinally sampled cases. Here, we describe our genomic analyses of 38 GBM patients with pre- and post-treatment samples for each individual (78 tumor samples in total; aCGH data were obtained for 36 among the 78). A substantial shift in the landscape of driver alterations was associated with distant appearances of the recurrent tumors from the initial tumor, suggesting that the genomic profile of an initial tumor can mislead targeted therapies for the distant recurrent tumor. In addition, in contrast to the previous work on IDH1-R132H low-grade gliomas, our GBM patients rarely developed hypermutation following the standard treatment, supporting the safety of temozolomide for IDH1-wild type, primary GBMs under the current standard regimen.

ORGANISM(S): Homo sapiens

PROVIDER: GSE63035 | GEO | 2015/10/06

SECONDARY ACCESSION(S): PRJNA266484

REPOSITORIES: GEO

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Project description:Background: Glioblastoma (GBM) is the most aggressive and currently incurable brain tumor with a median survival of one year in adult patients. Elucidating novel transcriptomic and epigenetic contributors to the molecular heterogeneity underlying its aggressiveness may lead to improved clinical outcomes. Methods: To identify GBM-associated 5-hydroxymethylcytosine (5hmC) and transcriptomic features as well as their cross-talks, genome-wide 5hmC and transcriptomic profiles of tissue samples from 61 patients with GBM and 9 normal controls were obtained for differential and co-regulation/co-modification analyses. Prognostic models on overall survival (OS) based on transcriptomic features and the 5hmC modifications summarized over genic regions (promoters, gene bodies) and brain-derived histone marks were developed using machine learning algorithms. Results: Despite global reduction, the majority of differential 5hmC features showed higher modification levels in GBM tumors as compared to normal controls. In addition, the bi-directional correlations between 5hmC modifications over promoter regions or gene bodies and gene expression were greatly disturbed in GBM tumors regardless of IDH1 mutation status. Phenotype-associated co-regulated 5hmC-5hmC modules and 5hmC-mRNA modules not only are enriched with different molecular pathways that are indicative of the pathogenesis of GBM, but also are of prognostic significance comparable to IDH1 mutation status. Lastly, the best-performing 5hmC model can predict patient survival at a much higher accuracy (c-index = 74%) when compared to conventional prognostic factor IDH1 (c-index = 57%), capturing the molecular characteristics of tumors that are independent of IDH1 mutation status and gene expression-based molecular subtypes. Conclusions: The 5hmC-based prognostic model offers a robust tool to predict survival in GBM patients, outperforming existing prognostic factors such as IDH1 mutations. The crosstalk between 5hmC and gene expression revealed another layer of complexity underlying the molecular heterogeneity in GBM, offering opportunities for identifying novel therapeutic targets.

Dataset Information

Spatio-temporal evolution of the primary glioblastoma genome (aCGH)

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