Mutations that arise during tumorigenesis may provide a selective advantage to the tumor cell (driver mutations) or have no net effect on tumor growth (passenger mutations). The mutational data obtained from sequencing and analysis of copy number alterations were integrated to identify GBM candidate cancer genes (CAN
-genes) that are most likely to be drivers and therefore worthy of further investigation. To determine whether a gene was likely to harbor driver mutations, we compared the number and type of mutations observed (including sequence changes, amplifications, and homozygous deletions) and determined the probability that these alterations would result from passenger mutation rates alone (12
) (fig. S1).
-genes, together with their passenger probabilities, are listed in table S7. The CAN
-genes included several with established roles in gliomas, including TP53
, and PIK3R1
). Of these genes, the most frequently altered were CDKN2A
(altered in 50% of GBMs); TP53
, and PTEN
(altered in 30 to 40%); NF1
, and RB1
(altered in 12 to 15%); and PIK3CA
(altered in 8 to 10%) (). Overall, these frequencies, which are similar to or in some cases higher than those previously reported, validate the sensitivity of our approach for detecting somatic alterations.
Most frequently altered GBM CAN-genes. All CAN-genes are listed in table S7.
Through analysis of additional gene members within cell signaling pathways affected by these genes, we identified alterations of critical genes in the TP53 pathway (TP53, MDM2, and MDM4), the RB1 pathway (RB1, CDK4, and CDKN2A), and the PI3K/PTEN pathway (PIK3CA, PIK3R1, PTEN, and IRS1). These alterations affected pathways in a majority of tumors (64%, 68%, and 50%, respectively), and in all cases but one, mutations within each tumor affected only a single member of each pathway in a mutually exclusive manner (P < 0.05) ().
Mutations of the TP53, PI3K, and RB1 pathways in GBM samples. Mut, mutated; Amp, amplified; Del, deleted; Alt, altered.
Systematic analyses of functional gene groups and pathways contained within the well-annotated MetaCore database (35
) identified enrichment of alterations in a variety of cellular processes in GBMs, including additional members of the TP53
pathways. Many of the pathways identified were similar to core signaling pathways found to be altered in pancreas, colorectal, and breast tumors, such as those regulating control of cellular growth, apoptosis, and cell adhesion (17
). However, several pathways were enriched only in GBMs. These included channels involved in transport of sodium, potassium, and calcium ions, as well as nervous system–specific cellular pathways such as synaptic transmission, transmission of nerve impulses, and axonal guidance (table S8). Mutations in these latter pathways may represent a subversion of normal glial cell processes to promote dysregulated growth and invasion.
Gene expression patterns can inform the analysis of pathways because they can reflect epigenetic alterations not detectable by sequencing or copy number analyses. To analyze the transcriptome of GBMs, we performed SAGE (37
) on all GBM samples for which sufficient RNA was available (total of 16 samples), as well as on two independent normal brain RNA controls (table S9). When combined with sequencing-by-synthesis methods (39
), SAGE provides a highly quantitative and sensitive measure of gene expression. We first used the transcript analysis to help identify previously uncharacterized target genes from the amplified and deleted regions that were revealed by our study. In tables S5 and S6, a candidate target gene could be identified within several of these regions through the use of the mutational as well as transcriptional data. Second, we used the transcript analysis to help identify genes that were differentially expressed in GBMs compared to normal brain. A large number of genes (143) were expressed on average at levels 10 times as high in the GBMs. Among the overexpressed genes, 16 encoded proteins that are predicted to be secreted or expressed on the cell surface, suggesting new opportunities for diagnostic and therapeutic applications. Third, we used expression data to help assess the significance of genes containing missense mutations (table S3). Finally, we assessed whether the gene sets implicated in the pathways enriched for genetic alterations were also altered through expression changes. Notably, the gene sets in these pathways were more highly enriched for differentially expressed genes than the remaining sets (P
< 0.001) (12
). These expression data thus independently highlight the potential importance of these pathways in the development of GBMs.