Subtyping Glioblastoma via Genomic Analysis

A recent paper in Cancer Cell reveals the power of integrated genomic datasets for understanding cancer origins and treatment. Members of the TCGA Research Network identified and characterized four glioblastoma subtypes using gene expression, somatic mutation, and copy number data.

Genetic Characteristics of GBM Subtypes

Each subtype was classified by gene expression clustering, and showed specific patterns of genetic alterations, particularly for four genes: platelet-derived growth factor receptor alpha (PDGFRA), isocitrate dehydrogenase 1 (IDH1), epidermal growfth factor receptor (EGFR), and neurofibromin 1 (NF1). Moreover, when analyzed with gene expression patterns of normal brain cells, the four GBM subtypes associate with different cell lineages.

	Expression	Signature	Alterations
Classical	NES; Notch (NOTCH3, JAG1), and Sonic hedgehog (SMO, GAS1) pathways	Astrocytic	EGFR, CDKN2A
Mesenchymal	Mesenchymal markers (CHI3L1, MET)	Astroglial	NF1 and PTEN
Proneural	Oligodendrocytic development genes (PDGFRA, NKX2-2, OLIG2)	Oligodendro- cytic	TP53, PDGFRA or PIK3CA/PIK3R1, IDH1
Neural	Neural markers (NEFL, GABRA1, SYT1, SLC12A5)	Neuron, Astro & Oligo	–

Clinical Features of GBM Subtypes

Overlaying the GBM subtypes with available clinical data revealed some interesting patterns as well. The Proneural subtype had younger patients and thus most of the secondary GBMs; the effect of this was that hypermutators were over-represented in Proneural. Perhaps the most important observation was that subtypes differed in their response to aggressive therapy: it worked well in Classical and Mesenchymal, and showed efficacy in Neural, but did not alter the survival of patients with Proneural GBM.

Clinical Feature	GBM Association
Age of patient	Proneural: Younger patients overrepresented
Hypermutator phenotype	Proneural: Among secondary GBMs
Response to aggressive therapy	Classical: Significantly reduced mortality Mesenchymal: Significantly reduced mortality Neural: Efficacy suggested Proneural: Did not alter survival

These findings have important implications for GBM diagnosis and treatment, and also demonstrate the power of the Cancer Genome Atlas: integrating gene expression, mutation, copy number, and clinical data for some of the world’s deadliest cancers.

References
Verhaak, R., Hoadley, K., Purdom, E., Wang, V., Qi, Y., Wilkerson, M., Miller, C., Ding, L., Golub, T., Mesirov, J., and The Cancer Genome Atlas Research Network (2010). Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1 Cancer Cell, 17 (1), 98-110 DOI: 10.1016/j.ccr.2009.12.020

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This entry was posted on Thursday, January 28th, 2010 at 1:00 pm by Dan Koboldt + and is filed under Cancer Genomics, Uncategorized. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.