The Somatic Genomic Landscape of Glioblastoma

By C. Brennan, R. Verhaak, A. McKenna, Benito Campos, Houtan Noushmehr, S. Salama, Siyuan Zheng, D. Chakravarty, J. Z. Sanborn, Samuel H. Berman and 48 more. Cell, 2013

4,593 citations recorded by the source corpus.

We describe the landscape of somatic genomic alterations based on multi-dimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer.

Subfields: Cancer Research, Genetics

View Paper: https://www.semanticscholar.org/paper/e72e63e565ff3f77a45f78a6a5692f463c2711ee

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In Cancer Research

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Citations in the corpus

• A Patient-Derived Glioblastoma Organoid Model and Biobank Recapitulates Inter- and Intra-tumoral Heterogeneity. (2019, methodology): “EGFRvIII transcripts were detected by RNA-seq and quantified by examining the number of exon 1 to exon 8 spanning reads versus the number of exon 7 to exon 8 spanning reads (Brennan et al., 2013).”
• Epigenetic Determinants of Cancer. (2016, background): “…H3K9me3) or in a more open chromatin environment, as depicted in Figure 4B. Data indicate that both exist in cancer, the significance and consequence of which are actively being investigated (Berman et al. 2012; Hon et al. 2012; Brennan et al. 2013; Reddy and Feinberg 2013; Timp and Feinberg 2013).”
• The BioGRID interaction database: 2017 update (2016, background): “BioGRID curators have coordinated with scientists and clinicians in the SU2C team to identify a core set of 31 genes implicated in GBM, as drawn from cancer genome analyses for genes either mutated or of altered copy number in patient-derived tumour samples (43,44).”
• Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes (2018, background): “GBM is a highly aggressive and genetically heterogeneous (Brennan et al., 2013) brain cancer commonly classified into four histologic subtypes Figure 9 continued for the entire tumor or for a-SMA and a-SMA regions.”
• Federated learning enables big data for rare cancer boundary detection (2022, background): “This reflects the major obstacle in treating these tumors that is their intrinsic heterogeneity 40,42 , and the need for analyses of larger and more diverse data towards better understanding the disease.”
• Glioblastoma: Overview of Disease and Treatment (2016, background): “For a more detailed analysis of GBM signaling pathways and subtypes, readers are referred to Brennan et al., 2013; Verhaak et al., 2010; and Wang et al., 2015.”