Dataset Information

EGFR heterogeneity and implications for therapeutic intervention in glioblastoma.

ABSTRACT: Patients with glioblastoma (GBM) have a universally poor prognosis and are in urgent need of effective treatment strategies. Recent advances in sequencing techniques unraveled the complete genomic landscape of GBMs and revealed profound heterogeneity of individual tumors even at the single cell level. Genomic profiling has detected epidermal growth factor receptor (EGFR) gene alterations in more than half of GBMs. Major genetic events include amplification and mutation of EGFR. Yet, treatment strategies targeting EGFR have thus far failed in clinical trials. In this review, we discuss the clonal and functional heterogeneity of EGFRs in GBM development and critically reassess the potential of EGFRs as therapeutic targets.

SUBMITTER: Eskilsson E

PROVIDER: S-EPMC5961011 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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EGFR heterogeneity and implications for therapeutic intervention in glioblastoma.

Eskilsson Eskil E Røsland Gro V GV Solecki Gergely G Wang Qianghu Q Harter Patrick N PN Graziani Grazia G Verhaak Roel G W RGW Winkler Frank F Bjerkvig Rolf R Miletic Hrvoje H

Neuro-oncology 20180501 6

Patients with glioblastoma (GBM) have a universally poor prognosis and are in urgent need of effective treatment strategies. Recent advances in sequencing techniques unraveled the complete genomic landscape of GBMs and revealed profound heterogeneity of individual tumors even at the single cell level. Genomic profiling has detected epidermal growth factor receptor (EGFR) gene alterations in more than half of GBMs. Major genetic events include amplification and mutation of EGFR. Yet, treatment st ...[more]

PMID: 29040782

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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

EGFR heterogeneity and implications for therapeutic intervention in glioblastoma.

Publications

EGFR heterogeneity and implications for therapeutic intervention in glioblastoma.

Similar Datasets

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