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Wen  PY, Kesari  S.  Malignant gliomas in adults.  N Engl J Med. 2008;359(5):492-507. PubMedGoogle ScholarCrossref
Cancer Genome Atlas Research Network.  Comprehensive genomic characterization defines human glioblastoma genes and core pathways.  Nature. 2008;455(7216):1061-1068. PubMedGoogle ScholarCrossref
Parsons  DW, Jones  S, Zhang  X,  et al.  An integrated genomic analysis of human glioblastoma multiforme.  Science. 2008;321(5897):1807-1812. PubMedGoogle ScholarCrossref
Bredel  M, Bredel  C, Juric  D,  et al.  High-resolution genome-wide mapping of genetic alterations in human glial brain tumors.  Cancer Res. 2005;65(10):4088-4096. PubMedGoogle ScholarCrossref
Nigro  JM, Misra  A, Zhang  L,  et al.  Integrated array-comparative genomic hybridization and expression array profiles identify clinically relevant molecular subtypes of glioblastoma.  Cancer Res. 2005;65(5):1678-1686. PubMedGoogle ScholarCrossref
Chin  K, Devries  S, Fridlyand  J,  et al.  Genomic and transcriptional aberrations linked to breast cancer pathophysiologies.  Cancer Cell. 2006;10(6):529-541. PubMedGoogle ScholarCrossref
Beroukhim  R, Getz  G, Nghiemphu  L,  et al.  Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma.  Proc Natl Acad Sci U S A. 2007;104(50):20007-20012. PubMedGoogle ScholarCrossref
Chin  L, Gray  JW.  Translating insights from the cancer genome into clinical practice.  Nature. 2008;452(7187):553-563. PubMedGoogle ScholarCrossref
Lennon  G, Auffray  C, Polymeropoulos  M, Soares  MB.  The IMAGE Consortium: an integrated molecular analysis of genomes and their expression.  Genomics. 1996;33(1):151-152. PubMedGoogle ScholarCrossref
Bredel  M, Bredel  C, Juric  D,  et al.  Functional network analysis reveals extended gliomagenesis pathway maps and three novel MYC-interacting genes in human gliomas.  Cancer Res. 2005;65(19):8679-8689. PubMedGoogle ScholarCrossref
Stears  RL, Getts  RC, Gullans  SR.  A novel, sensitive detection system for high-density microarrays using dendrimer technology.  Physiol Genomics. 2000;3(2):93-99. PubMedGoogle Scholar
Phillips  HS, Kharbanda  S, Chen  R,  et al.  Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis.  Cancer Cell. 2006;9(3):157-173. PubMedGoogle ScholarCrossref
Freije  WA, Castro-Vargas  FE, Fang  Z,  et al.  Gene expression profiling of gliomas strongly predicts survival.  Cancer Res. 2004;64(18):6503-6510. PubMedGoogle ScholarCrossref
Lee  Y, Scheck  AC, Cloughesy  TF,  et al.  Gene expression analysis of glioblastomas identifies the major molecular basis for the prognostic benefit of younger age.  BMC Med Genomics. 2008;1(1):52. PubMedGoogle ScholarCrossref
Olshen  AB, Venkatraman  ES, Lucito  R, Wigler  M.  Circular binary segmentation for the analysis of array-based DNA copy number data.  Biostatistics. 2004;5(4):557-572. PubMedGoogle ScholarCrossref
Mitelman  FE.  ISCN 1995: An International System for Human Cytogenetic Nomenclature. Basel, Switzerland: S Karger; 1995.
Hyman  E, Kauraniemi  P, Hautaniemi  S,  et al.  Impact of DNA amplification on gene expression patterns in breast cancer.  Cancer Res. 2002;62(21):6240-6245. PubMedGoogle Scholar
Golub  TR, Slonim  DK, Tamayo  P,  et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.  Science. 1999;286(5439):531-537. PubMedGoogle ScholarCrossref
Juric  D, Bredel  C, Sikic  BI, Bredel  M.  Integrated high-resolution genome-wide analysis of gene dosage and gene expression in human brain tumors.  Methods Mol Biol. 2007;377:187-202. PubMedGoogle Scholar
Storey  JD, Tibshirani  R.  Statistical significance for genomewide studies.  Proc Natl Acad Sci U S A. 2003;100(16):9440-9445. PubMedGoogle ScholarCrossref
Benjamini  Y, Hochberg  Y.  Controlling the false discovery rate: a practical and powerful approach to multiple testing.  J R Stat Soc Series B Stat Methodol. 1995;57(1):289-300.Google Scholar
Stark  C, Breitkreutz  BJ, Reguly  T, Boucher  L, Breitkreutz  A, Tyers  M.  BioGRID: a general repository for interaction datasets.  Nucleic Acids Res. 2006;34(database issue):D535-D539. PubMedGoogle ScholarCrossref
Fleiss  JL.  Statistical Methods for Rates and Proportions. 2nd ed. New York, NY: John Wiley & Sons; 1981:sect 5.6
Eisen  MB, Spellman  PT, Brown  PO, Botstein  D.  Cluster analysis and display of genome-wide expression patterns.  Proc Natl Acad Sci U S A. 1998;95(25):14863-14868. PubMedGoogle ScholarCrossref
Tusher  VG, Tibshirani  R, Chu  G.  Significance analysis of microarrays applied to the ionizing radiation response.  Proc Natl Acad Sci U S A. 2001;98(9):5116-5121. PubMedGoogle ScholarCrossref
Louis  DN, Ohgaki  H, Wiestler  OD,  et al.  The 2007 WHO classification of tumours of the central nervous system.  Acta Neuropathol. 2007;114(2):97-109. PubMedGoogle ScholarCrossref
Carlson  MR, Zhang  B, Fang  Z, Mischel  PS, Horvath  S, Nelson  SF.  Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks.  BMC Genomics. 2006;7:40. PubMedGoogle ScholarCrossref
Mischel  PS, Cloughesy  TF, Nelson  SF.  DNA-microarray analysis of brain cancer: molecular classification for therapy.  Nat Rev Neurosci. 2004;5(10):782-792. PubMedGoogle ScholarCrossref
Srivastava  M, Bubendorf  L, Srikantan  V,  et al.  ANX7, a candidate tumor suppressor gene for prostate cancer.  Proc Natl Acad Sci U S A. 2001;98(8):4575-4580. PubMedGoogle ScholarCrossref
Leighton  X, Srikantan  V, Pollard  HB, Sukumar  S, Srivastava  M.  Significant allelic loss of ANX7region (10q21) in hormone receptor negative breast carcinomas.  Cancer Lett. 2004;210(2):239-244. PubMedGoogle ScholarCrossref
Srivastava  M, Bubendorf  L, Raffeld  M,  et al.  Prognostic impact of ANX7-GTPase in metastatic and HER2-negative breast cancer patients.  Clin Cancer Res. 2004;10(7):2344-2350. PubMedGoogle ScholarCrossref
Srivastava  M, Torosyan  Y, Raffeld  M, Eidelman  O, Pollard  HB, Bubendorf  L.  ANXA7 expression represents hormone-relevant tumor suppression in different cancers.  Int J Cancer. 2007;121(12):2628-2636. PubMedGoogle ScholarCrossref
Torosyan  Y, Dobi  A, Naga  S,  et al.  Distinct effects of annexin A7 and p53 on arachidonate lipoxygenation in prostate cancer cells involve 5-lipoxygenase transcription.  Cancer Res. 2006;66(19):9609-9616. PubMedGoogle ScholarCrossref
Furge  LL, Chen  K, Cohen  S.  Annexin VII and annexin XI are tyrosine phosphorylated in peroxovanadate-treated dogs and in platelet-derived growth factor-treated rat vascular smooth muscle cells.  J Biol Chem. 1999;274(47):33504-33509. PubMedGoogle ScholarCrossref
Nakagawa  H, Liyanarachchi  S, Davuluri  RV,  et al.  Role of cancer-associated stromal fibroblasts in metastatic colon cancer to the liver and their expression profiles.  Oncogene. 2004;23(44):7366-7377. PubMedGoogle ScholarCrossref
Yang  HS, Matthews  CP, Clair  T,  et al.  Tumorigenesis suppressor Pdcd4 down-regulates mitogen-activated protein kinase kinase kinase kinase 1 expression to suppress colon carcinoma cell invasion.  Mol Cell Biol. 2006;26(4):1297-1306. PubMedGoogle ScholarCrossref
Matsushima-Nishiu  M, Unoki  M, Ono  K,  et al.  Growth and gene expression profile analyses of endometrial cancer cells expressing exogenous PTEN.  Cancer Res. 2001;61(9):3741-3749. PubMedGoogle Scholar
Wan  D, Gong  Y, Qin  W,  et al.  Large-scale cDNA transfection screening for genes related to cancer development and progression.  Proc Natl Acad Sci U S A. 2004;101(44):15724-15729. PubMedGoogle ScholarCrossref
Guo  QM, Malek  RL, Kim  S,  et al.  Identification of c-myc responsive genes using rat cDNA microarray.  Cancer Res. 2000;60(21):5922-5928. PubMedGoogle Scholar
Deng  Y, Wu  X.  Peg3/Pw1 promotes p53-mediated apoptosis by inducing Bax translocation from cytosol to mitochondria.  Proc Natl Acad Sci U S A. 2000;97(22):12050-12055. PubMedGoogle ScholarCrossref
Sanai  N, Alvarez-Buylla  A, Berger  MS.  Neural stem cells and the origin of gliomas.  N Engl J Med. 2005;353(8):811-822. PubMedGoogle ScholarCrossref
Nicholas  MK, Lukas  RV, Jafri  NF, Faoro  L, Salgia  R.  Epidermal growth factor receptor-mediated signal transduction in the development and therapy of gliomas.  Clin Cancer Res. 2006;12(24):7261-7270. PubMedGoogle ScholarCrossref
Jenkins  RB, Blair  H, Ballman  KV,  et al.  A t(1;19)(q10;p10) mediates the combined deletions of 1p and 19q and predicts a better prognosis of patients with oligodendroglioma.  Cancer Res. 2006;66(20):9852-9861. PubMedGoogle ScholarCrossref
Rich  JN, Hans  C, Jones  B,  et al.  Gene expression profiling and genetic markers in glioblastoma survival.  Cancer Res. 2005;65(10):4051-4058. PubMedGoogle ScholarCrossref
Mischel  PS, Shai  R, Shi  T,  et al.  Identification of molecular subtypes of glioblastoma by gene expression profiling.  Oncogene. 2003;22(15):2361-2373. PubMedGoogle ScholarCrossref
Nutt  CL, Mani  DR, Betensky  RA,  et al.  Gene expression-based classification of malignant gliomas correlates better with survival than histological classification.  Cancer Res. 2003;63(7):1602-1607. PubMedGoogle Scholar
Colman  H, Zhang  L, Sulman  EP,  et al.  A multigene predictor of outcome in glioblastoma.  Neuro Oncol. In pressGoogle Scholar
Original Contribution
July 15, 2009

A Network Model of a Cooperative Genetic Landscape in Brain Tumors

Author Affiliations

Author Affiliations: Department of Neurological Surgery, Northwestern Brain Tumor Institute, Lurie Center for Cancer Genetics Research and Center for Genetic Medicine (Drs M. Bredel, Chandler, and Yadav and Ms Renfrow), and Department of Preventive Medicine (Dr Scholtens), Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Neurosurgery (Drs M. Bredel and Harsh), Oncology Division, and Departments of Medicine (Drs C. Bredel and Sikic), Pathology (Dr Vogel), and Health Research and Policy and Statistics (Dr Tibshirani), Stanford University School of Medicine, Stanford, California; Department of General Neurosurgery, Neurocenter and Comprehensive Cancer Center Freiburg, University of Freiburg, Freiburg, Germany (Drs M. Bredel and C. Bredel); and Ina Levine Brain Tumor Center, Neuro-Oncology and Neurosurgery Research, Barrow Neurological Institute of St Joseph's Medical Center, Phoenix, Arizona (Dr Scheck).

JAMA. 2009;302(3):261-275. doi:10.1001/jama.2009.997

Context  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.

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

Design, Setting, and Patients  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).

Main Outcome Measures  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.