Roversi G, Pfundt R, Moroni R F, Magnani I, van Reijmersdal S, Pollo B, Straatman H, Larizza L, Schoenmakers E F P M
Department of Biology and Genetics, University of Milan, Milan, Italy.
Oncogene. 2006 Mar 9;25(10):1571-83. doi: 10.1038/sj.onc.1209177.
Identification of genetic copy number changes in glial tumors is of importance in the context of improved/refined diagnostic, prognostic procedures and therapeutic decision-making. In order to detect recurrent genomic copy number changes that might play a role in glioma pathogenesis and/or progression, we characterized 25 primary glioma cell lines including 15 non glioblastoma (non GBM) (I-III WHO grade) and 10 GBM (IV WHO grade), by array comparative genomic hybridization, using a DNA microarray comprising approx. 3500 BACs covering the entire genome with a 1 Mb resolution and additional 800 BACs covering chromosome 19 at tiling path resolution. Combined evaluation by single clone and whole chromosome analysis plus 'moving average (MA) approach' enabled us to confirm most of the genetic abnormalities previously identified to be associated with glioma progression, including +1q32, +7, -10, -22q, PTEN and p16 loss, and to disclose new small genomic regions, some correlating with grade malignancy. Grade I-III gliomas exclusively showed losses at 3p26 (53%), 4q13-21 (33%) and 7p15-p21 (26%), whereas only GBMs exhibited 4p16.1 losses (40%). Other recurrent imbalances, such as losses at 4p15, 5q22-q23, 6p23-25, 12p13 and gains at 11p11-q13, were shared by different glioma grades. Three intervals with peak of loss could be further refined for chromosome 10 by our MA approach. Data analysis of full-coverage chromosome 19 highlighted two main regions of copy number gain, never described before in gliomas, at 19p13.11 and 19q13.13-13.2. The well-known 19q13.3 loss of heterozygosity area in gliomas was not frequently affected in our cell lines. Genomic hotspot detection facilitated the identification of small intervals resulting in positional candidate genes such as PRDM2 (1p36.21), LRP1B (2q22.3), ADARB2 (10p15.3), BCCIP (10q26.2) and ING1 (13q34) for losses and ECT2 (3q26.3), MDK, DDB2, IG20 (11p11.2) for gains. These data increase our current knowledge about cryptic genetic changes in gliomas and may facilitate the further identification of novel genetic elements, which may provide us with molecular tools for the improved diagnostics and therapeutic decision-making in these tumors.
在改进/完善诊断、预后程序及治疗决策的背景下,鉴定胶质肿瘤中的基因拷贝数变化具有重要意义。为了检测可能在胶质瘤发病机制和/或进展中起作用的复发性基因组拷贝数变化,我们通过阵列比较基因组杂交对25个原发性胶质瘤细胞系进行了特征分析,其中包括15个非胶质母细胞瘤(非GBM)(WHO I - III级)和10个GBM(WHO IV级),使用了一个包含约3500个BAC的DNA微阵列,该微阵列以1 Mb的分辨率覆盖整个基因组,并以平铺路径分辨率额外覆盖了19号染色体的800个BAC。通过单克隆和全染色体分析以及“移动平均(MA)方法”进行综合评估,使我们能够确认大多数先前确定与胶质瘤进展相关的基因异常,包括+1q32、+7、-10、-22q、PTEN和p16缺失,并揭示了新的小基因组区域,其中一些与恶性程度相关。I - III级胶质瘤仅在3p26(53%)、4q13 - 21(33%)和7p15 - p21(26%)出现缺失,而只有GBM出现4p16.1缺失(40%)。其他复发性失衡,如4p15、5q22 - q23、6p23 - 25、12p13缺失以及11p11 - q13增益,在不同级别的胶质瘤中都有出现。通过我们的MA方法,可以进一步细化10号染色体上三个缺失峰值区间。对19号染色体全覆盖的数据分析突出了两个以前在胶质瘤中从未描述过的拷贝数增加的主要区域,分别位于19p13.11和19q13.13 - 13.2。在我们的细胞系中,胶质瘤中众所周知的19q13.3杂合性缺失区域并不常受影响。基因组热点检测有助于识别导致定位候选基因的小区间,如缺失相关的PRDM2(1p36.21)、LRP1B(2q22.3)、ADARB2(10p15.3)、BCCIP(10q26.2)和ING1(13q34),以及增益相关的ECT2(3q26.3)、MDK、DDB2、IG20(11p11.2)。这些数据增加了我们目前对胶质瘤中隐秘基因变化的了解,并可能有助于进一步鉴定新的遗传元件,这可能为我们提供分子工具,以改善这些肿瘤的诊断和治疗决策。