Genome-wide analysis of DNA copy number variation in breast cancer using DNA microarrays

Genome-wide analysis of DNA copy number variation in breast cancer using DNA microarrays
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使用 DNA 微阵列对乳腺癌 DNA 拷贝数变异进行全基因组分析

DOI:
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发表时间:
1999
期刊:
影响因子:
30.8
通讯作者:
P. Brown
P. Brown
中科院分区:
生物学1区
文献类型:
--
作者:
J. Pollack;C. Perou;T. Sørlie;Ash A. Alizadeh;Christian A. Rees;Michael B. Eise;Alexander Pergamenschikov;Cheryl F. Williams;M. Rijn;S. Jeffrey;H. Johnsen;P. Lønning;S. Geisler;T. Aas;A. Børresen;D. Botstein;P. Brown

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基因扩增和缺失经常在癌症中发挥致病作用。 30,000 个辐射杂交映射的 cDNA 提供了基因组资源来以高分辨率映射这些病变。我们开发了一种基于 cDNA 微阵列的比较基因组杂交方法,用于同时分析数千个基因的 DNA 拷贝数变化。使用此程序,我们可以可靠地检测两倍或更少的 DNA 拷贝数变化。在乳腺癌细胞系中,我们以高分辨率绘制了 DNA 拷贝数变异区域,揭示了以前未识别的基因组扩增和缺失,以及扩增子结构的新复杂性。 DNA 扩增的重复区域可能含有新的癌基因,很容易被识别出来。 DNA 拷贝数和基因表达的改变可以在平行分析中进行比较和关联。我们现在已经收集了 9 种乳腺癌细胞系和超过 35 种原发性乳腺肿瘤的全基因组 DNA 拷贝数信息。对于乳腺肿瘤,DNA 拷贝数信息正在与已收集的 p53 状态、微阵列基因表达谱、治疗反应和临床结果数据进行比较和关联。将介绍该分析的结果。
Gene amplifications and deletions frequently have pathogenetic roles in cancer. 30,000 radiation-hybrid mapped cDNAs provide a genomic resource to map these lesions with high resolution. We developed a cDNA microarray-based comparative genomic hybridisation method for analysing DNA copy number changes across thousands of genes simultaneously. Using this procedure, we could reliably detect DNA copy number alterations of twofold or less. In breast cancer cell lines, we have mapped regions of DNA copy number variation at high resolution, revealing previously unrecognised genomic amplifications and deletions, and new complexities of amplicon structure. Recurrent regions of DNA amplification, which may harbour novel oncogenes, were readily identified. Alterations of DNA copy number and gene expression could be compared and correlated in parallel analyses. We have now collected genome-wide DNA copy number information on a set of 9 breast cancer cell lines and over 35 primary breast tumours. For the breast tumours, DNA copy number information is being compared and correlated with data already collected on p53 status, microarray gene expression profiles, and treatment response and clinical outcome. The results of this analysis will be presented.