A network model of a cooperative genetic landscape in brain tumors.
BACKGROUND
Gliomas, particularly glioblastomas, are among the deadliest of human tumors. Gliomas emerge through the accumulation of recurrent chromosomal alterations, some of which target yet-to-be-discovered cancer genes. A persistent question concerns the biological basis for the coselection of these alterations during gliomagenesis.


OBJECTIVE
To describe a network model of a cooperative genetic landscape in gliomas and to evaluate its clinical relevance.


METHODS
Multidimensional genomic profiles and clinical profiles of 501 patients with gliomas (45 tumors in an initial discovery set collected between 2001 and 2004 and 456 tumors in validation sets made public between 2006 and 2008) from multiple academic centers in the United States and The Cancer Genome Atlas Pilot Project (TCGA).


METHODS
Identification of genes with coincident genetic alterations, correlated gene dosage and gene expression, and multiple functional interactions; association between those genes and patient survival.


RESULTS
Gliomas select for a nonrandom genetic landscape-a consistent pattern of chromosomal alterations-that involves altered regions ("territories") on chromosomes 1p, 7, 8q, 9p, 10, 12q, 13q, 19q, 20, and 22q (false-discovery rate-corrected P<.05). A network model shows that these territories harbor genes with putative synergistic, tumor-promoting relationships. The coalteration of the most interactive of these genes in glioblastoma is associated with unfavorable patient survival. A multigene risk scoring model based on 7 landscape genes (POLD2, CYCS, MYC, AKR1C3, YME1L1, ANXA7, and PDCD4) is associated with the duration of overall survival in 189 glioblastoma samples from TCGA (global log-rank P = .02 comparing 3 survival curves for patients with 0-2, 3-4, and 5-7 dosage-altered genes). Groups of patients with 0 to 2 (low-risk group) and 5 to 7 (high-risk group) dosage-altered genes experienced 49.24 and 79.56 deaths per 100 person-years (hazard ratio [HR], 1.63; 95% confidence interval [CI], 1.10-2.40; Cox regression model P = .02), respectively. These associations with survival are validated using gene expression data in 3 independent glioma studies, comprising 76 (global log-rank P = .003; 47.89 vs 15.13 deaths per 100 person-years for high risk vs low risk; Cox model HR, 3.04; 95% CI, 1.49-6.20; P = .002) and 70 (global log-rank P = .008; 83.43 vs 16.14 deaths per 100 person-years for high risk vs low risk; HR, 3.86; 95% CI, 1.59-9.35; P = .003) high-grade gliomas and 191 glioblastomas (global log-rank P = .002; 83.23 vs 34.16 deaths per 100 person-years for high risk vs low risk; HR, 2.27; 95% CI, 1.44-3.58; P<.001).


CONCLUSIONS
The alteration of multiple networking genes by recurrent chromosomal aberrations in gliomas deregulates critical signaling pathways through multiple, cooperative mechanisms. These mutations, which are likely due to nonrandom selection of a distinct genetic landscape during gliomagenesis, are associated with patient prognosis.
Bredel
Markus
M
Department of Neurological Surgery, Northwestern Brain Tumor Institute and Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL 60611-3015, USA. m-bredel@northwestern.edu
Scholtens
Denise M
DM
Harsh
Griffith R
GR
Bredel
Claudia
C
Chandler
James P
JP
Renfrow
Jaclyn J
JJ
Yadav
Ajay K
AK
Vogel
Hannes
H
Scheck
Adrienne C
AC
Tibshirani
Robert
R
Sikic
Branimir I
BI
D015096:3-Hydroxysteroid Dehydrogenases / Q000235:genetics; D000074183:ATPases Associated with Diverse Cellular Activities; D000074425:Aldo-Keto Reductase Family 1 Member C3; D017310:Annexin A7 / Q000235:genetics; D051017:Apoptosis Regulatory Proteins / Q000235:genetics; D001932:Brain Neoplasms / Q000235:genetics* / Q000401:mortality / Q000473:pathology; D002869:Chromosome Aberrations; D045304:Cytochromes c / Q000235:genetics; D004303:Dosage Compensation, Genetic; D004843:Epistasis, Genetic; D005260:Female; D018628:Gene Dosage; D015972:Gene Expression Regulation, Neoplastic; D052138:Genes, Neoplasm; D016259:Genes, myc; D055106:Genome-Wide Association Study; D005910:Glioma / Q000235:genetics* / Q000401:mortality / Q000473:pathology; D006801:Humans; D006911:Hydroxyprostaglandin Dehydrogenases / Q000235:genetics; D008297:Male; D008666:Metalloendopeptidases / Q000235:genetics; D008875:Middle Aged; D024101:Mitochondrial Proteins; D008957:Models, Genetic; D009154:Mutation; D009687:Nuclear Proteins; D011379:Prognosis; D016016:Proportional Hazards Models; D016601:RNA-Binding Proteins / Q000235:genetics; D012306:Risk; D015398:Signal Transduction; D016019:Survival Analysis
D017310:Annexin A7; D051017:Apoptosis Regulatory Proteins; D024101:Mitochondrial Proteins; D009687:Nuclear Proteins; C114487:PDCD4 protein, human; C473592:POLDIP2 protein, human; D016601:RNA-Binding Proteins; D045304:Cytochromes c; D015096:3-Hydroxysteroid Dehydrogenases; D006911:Hydroxyprostaglandin Dehydrogenases; C487341:AKR1C3 protein, human; D000074425:Aldo-Keto Reductase Family 1 Member C3; D008666:Metalloendopeptidases; C410807:YME1L1 protein, human; D000074183:ATPases Associated with Diverse Cellular Activities