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.
|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.
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