据7月15日刊JAMA上的一则研究披露，在一个变异基因网络之间的相互作用看来在脑肿瘤的发生与进展上扮演着一种重要的角色。
恶性胶质瘤（脑肿瘤）与不成比例的高发病率和死亡率有关，它们属于最具毁灭性的肿瘤之一。特殊的基因组变化是其形成及恶性发展的基础。文章的作者写道：“染色体的改变被人们假定会通过修饰独特基因的表达或功能而施加其促进胶质细胞生长的作用，它们也可通过解除对生长因子的信号传播途径和存活途径的调节而促进胶质细胞的生长。”
对脑肿瘤的肿瘤发生学的研究一直聚焦于个别染色体内的标靶基因的变化所带来的肿瘤促进或肿瘤抑制的功能上。然而，这些基因的改变并非以孤立的方式存在，人们也无法用单个基因的变异来解释胶质细胞瘤的发生。相反，根据文章的背景资料，可能存在着同时发生的基因变异等在机制上的联系。
Northwestern University Feinberg School of Medicine, Chicago之Northwestern Brain Tumor Institute的Markus Bredel, M.D., Ph.D.及其同僚对胶质瘤中的促进肿瘤发生的基因之间的关系进行了调查。该研究包括了采自美国和The Cancer Genome Atlas Pilot Project (TCGA) 的多个学术中心的罹患胶质瘤的501名患者的基因组和临床形态特征（有45个肿瘤病例属于初始发现的数据组，它们的数据是在2001年至2004年之间采集的；有456个肿瘤病例属于确诊数据组，它们在2006年至2008年之间对外公开）。此项分析包括辨识带有同时发生的基因变化的基因、相关的基因剂量（以某种分析方法所决定的某一特别基因的拷贝数目）与基因表达和多重功能性相互作用；以及这些基因与患者生存率之间的相关性。
研究人员发现：“胶质瘤中的因反复发生的染色体畸变所导致的多重网络状联系的基因变化会通过多重性的且相互合作的机制解除对关键性信号通路的调节。这些在胶质细胞瘤发生过程中的基因变异可能是因为对一个独特基因形貌 [ 即一种一致性的染色体变异模式 ] 的非随机性选择而造成的，这些基因变异与患者的预后有关。”
文章的作者补充说，在胶质瘤中发现的这些基因变异可促使人们将这些变异当作可能的治疗标靶进行评估。 “一种基因的网络背景可能会影响以其编码的蛋白作为标靶的治疗方法的功效。我们的基因形貌模型的复杂性可帮助人们解释为什么以单个基因产物作为标靶的治疗策略缺乏功效。”（生物谷Bioon.com）
生物谷推荐原始出处：
JAMA. 2009;302(3):261-275.
A Network Model of a Cooperative Genetic Landscape in Brain Tumors
Markus Bredel, MD, PhD; Denise M. Scholtens, PhD; Griffith R. Harsh, MD; Claudia Bredel, PhD; James P. Chandler, MD; Jaclyn J. Renfrow, MA; Ajay K. Yadav, PhD; Hannes Vogel, MD, PhD; Adrienne C. Scheck, PhD; Robert Tibshirani, PhD; Branimir I. Sikic, MD
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.
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).
