The DOI matches its Crossref record; 50 of the 52 references it cites were found in a registry or an index.
Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1
Open accessAbstract
The Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme (GBM). We describe a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes and integrate multidimensional genomic data to establish patterns of somatic mutations and DNA copy number. Aberrations and gene expression of EGFR, NF1, and PDGFRA/IDH1 each define the Classical, Mesenchymal, and Proneural subtypes, respectively. Gene signatures of normal brain cell types show a strong relationship between subtypes and different neural lineages. Additionally, response to aggressive therapy differs by subtype, with the greatest benefit in the Classical subtype and no benefit in the Proneural subtype. We provide a framework that unifies transcriptomic and genomic dimensions for GBM molecular stratification with important implications for future studies.
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“High-throughput genomic, epigenomic, and transcriptomic profiling of glioblastoma cells revealed three main subtypes harboring distinct molecular and phenotypic landscapes that affect response to the therapy and clinical outcome: mesenchymal, proneural, and classical subtypes.96,97,98,99,100”
From MiR-128-3p – a gray eminence of the human central nervous system (2024) · cites this paper as [96]
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“Although PDGFRA is a frequent target of genomic amplification and gain-of-function mutations in glioblastoma (15%), such alterations are rare in IDH mutant tumors23,24.”
From Insulator dysfunction and oncogene activation in IDH mutant gliomas (2016) · cites this paper as [24]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Although PDGFRA is a frequent target of genomic amplification and gain-of-function mutations in glioblastoma (15%), such alterations are rare in IDH mutant tumors23,24.”
From Insulator dysfunction and oncogene activation in IDH mutant gliomas (2016) · cites this paper as [24]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Nonetheless, IDH mutant gliomas strongly express PDGFRA (Fig. 2b), and share the proneural transcriptional program characteristic of PDGFRA-amplified tumors23,24.”
From Insulator dysfunction and oncogene activation in IDH mutant gliomas (2016) · cites this paper as [24]
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“The prevalence of p53 mutations differs in the different GBM molecular subtypes (proneural, mesenchymal, neural and classical) (54%, 32%, 21% and 0%, respectively) [15].”
From The p53 Pathway in Glioblastoma (2018) · cites this paper as [15]
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“Crucially, the levels of miR-128-3p forms can also differ depending on the genetic and molecular background, as they correlated with the glioblastoma subtypes, with the most reduced expression in the most aggressive mesenchymal one.96,113”
From MiR-128-3p – a gray eminence of the human central nervous system (2024) · cites this paper as [96]
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“For example, upregulation of PDGFRA, platelet-derived growth factor receptor alpha, is a hallmark of the proneural subtype [15].”
From The p53 Pathway in Glioblastoma (2018) · cites this paper as [15]
In the citing paper
“While glioblastoma is the most common malignant primary brain tumor26–28, it is still classified as a “rare” disease, as its incidence rate (i.e., 3/100,000 people) is substantially lower than the rare disease definition rate (i.e., <10/100,000 people)24.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [27]
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Cites
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J. (1987) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(87)90125-7 · Cited by 21,392
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568
DAVID: Database for Annotation, Visualization, and Integrated Discovery
Dennis, G., Sherman, B. T., Hosack, D. A., Yang, J., Gao, W., Lane, H. C., & Lempicki, R. A. (2003) · Genome Biology · doi:10.1186/gb-2003-4-5-p3 · Cited by 9,327
Comprehensive genomic characterization defines human glioblastoma genes and core pathways
The Cancer Genome Atlas Research Network (2008) · Nature · doi:10.1038/nature07385 · Cited by 7,735
MGMT Gene Silencing and Benefit from Temozolomide in Glioblastoma
Hegi, M. E., Diserens, A.-C., Gorlia, T., Hamou, M.-F., de Tribolet, N., Weller, M., Kros, J. M., Hainfellner, J. A., Mason, W., Mariani, L., et al. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa043331 · Cited by 7,405
Epithelial–mesenchymal transitions in tumour progression
Thiery, J. P. (2002) · Nature reviews. Cancer · doi:10.1038/nrc822 · Cited by 6,767
IDH1 and IDH2 Mutations in Gliomas
Yan, H., Parsons, D. W., Jin, G., McLendon, R., Rasheed, B. A., Yuan, W., Kos, I., Batinic-Haberle, I., Jones, S., Riggins, G. J., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0808710 · Cited by 5,847
Summaries of Affymetrix GeneChip probe level data
Irizarry, R. A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gng015 · Cited by 4,837
Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1
Barbie, D. A., Tamayo, P., Boehm, J. S., Kim, S. Y., Moody, S. E., Dunn, I. F., Schinzel, A. C., Sandy, P., Meylan, E., Scholl, C., et al. (2009) · Nature · doi:10.1038/nature08460 · Cited by 3,922
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GSVA: gene set variation analysis for microarray and RNA-Seq data
Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440
Robinson, J. T., Thorvaldsdóttir, H., Winckler, W., Guttman, M., Lander, E. S., Getz, G., & Mesirov, J. P. (2011) · Nature Biotechnology · doi:10.1038/nbt.1754 · Cited by 15,390
Self-citationThe Molecular Signatures Database Hallmark Gene Set Collection
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015) · Cell Systems · doi:10.1016/j.cels.2015.12.004 · Cited by 13,807
Self-citationInferring tumour purity and stromal and immune cell admixture from expression data
Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., Treviño, V., Shen, H., Laird, P. W., Levine, D. A., et al. (2013) · Nature Communications · doi:10.1038/ncomms3612 · Cited by 9,778
Self-citationIntegrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration
Thorvaldsdottir, H., Robinson, J. T., & Mesirov, J. P. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbs017 · Cited by 9,049
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Metadata
- Authors
- Verhaak, R. G. W., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., Miller, C. R., Ding, L., Golub, T., Mesirov, J. P., Alexe, G., Lawrence, M., O'Kelly, M., Tamayo, P., Weir, B. A., Gabriel, S., Winckler, W., Gupta, S., Jakkula, L., Feiler, H. S., Hodgson, J. G., James, C. D., Sarkaria, J. N., Brennan, C., Kahn, A., Spellman, P. T., Wilson, R. K., Speed, T. P., Gray, J. W., Meyerson, M., Getz, G., Perou, C. M., & Hayes, D. N.
- Affiliations
- Broad Institute (US)
- University of North Carolina at Chapel Hill (US)
- University of California, Berkeley (US)
- Washington University in St. Louis (US)
- Lawrence Berkeley National Laboratory (US)
- University of California, San Francisco (US)
- Mayo Clinic (US)
- Memorial Sloan Kettering Cancer Center (US)
- SRA International (United States) (US)
- Walter and Eliza Hall Institute of Medical Research (AU)
- Corresponding author
- Hayes, D. Neil
- ORCID
- Verhaak, Roel G.W.
- Hoadley, Katherine A.
- Purdom, Elizabeth
- Wang, Victoria
- Qi, Yuan
- Wilkerson, Matthew D.
- Miller, C. Ryan
- Ding, Li
- Golub, Todd
- Mesirov, Jill P.
- Lawrence, Michael
- Tamayo, Pablo
- Gabriel, Stacey
- Winckler, Wendy
- Gupta, Supriya
- Feiler, Heidi S.
- Hodgson, J. Graeme
- James, C. David
- Sarkaria, Jann N.
- Brennan, Cameron
- Spellman, Paul T.
- Wilson, Richard K.
- Speed, Terence P.
- Gray, Joe W.
- Meyerson, Matthew
- Getz, Gad
- Perou, Charles M.
- Hayes, D. Neil
- Venue
- Cancer Cell
- Publisher
- Elsevier BV
- Published
- 2010-01
- Volume
- 17
- Issue
- 1
- Pages
- 98-110
- ISSN
- 1535-6108 (ISSN-L)
- PMC
- PMC2818769
- OpenAlex
- W2161289668
- Cited by
- 7,575
- References
- 52
- Citations by year
- 2026: 312 · 2025: 399 · 2024: 451 · 2023: 551 · 2022: 523 · 2021: 697 · 2020: 593 · 2019: 493 · 2018: 482 · 2017: 522 · 2016: 502 · 2015: 455 · 2014: 459 · 2013: 406 · 2012: 379
- FWCI
- 159.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cancer Cell (publishedVersion, public-domain)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Energy
- National Human Genome Research Institute (U54HG003067, U54HG003079, U54HG003273)
- National Cancer Institute (CA097257, L30 CA104183, U24CA126554, R01CA127716, CA108961, U24CA126546, U24CA126543, U24CA126563, U24CA126544, U24CA143867, U24CA143848, P50CA108961, U24CA126551, CA127716, P50 CA 58223, U24CA126561, P50CA058223, P50CA097257)
- National Institute of Neurological Disorders and Stroke (R01NS049720, NS49720)
- National Institute of Environmental Health Sciences (F30ES019449)
- National Center for Research Resources (RR023248, K12RR023248, UL1RR024992)
- Topics
- Health Sciences › Medicine › Oncology › Glioma Diagnosis and Treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
- Keywords
- glioblastoma, molecular subtypes, proneural subtype, mesenchymal subtype, classical subtype, PDGFRA, IDH1, EGFR, neurofibromatosis type 1, somatic mutations, gene expression signatures, neural lineage, treatment response, molecular stratification
- MeSH terms
- Adult, Aged, Brain Neoplasms, DNA Mutational Analysis, Factor Analysis, Statistical, Glioblastoma, Humans, Isocitrate Dehydrogenase, Middle Aged, Mutation, Neurofibromatosis 1, Prognosis, Gene Expression, Gene Dosage, Oligonucleotide Array Sequence Analysis, Receptor, Platelet-Derived Growth Factor alpha, Gene Expression Profiling, ErbB Receptors
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