Virtual CGH: an integrative approach to predict genetic abnormalities from gene expression microarray data applied in lymphoma.
Virtual CGH: an integrative approach to predict genetic abnormalities from gene expression microarray data applied in lymphoma.
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DOI：
10.1186/1755-8794-4-32
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发表时间：
2011-04-12
影响因子：
2.7
通讯作者：
Ali HH
中科院分区：
文献类型：
--
作者：
Geng H;Iqbal J;Chan WC;Ali HH
比较基因组杂交（Comparative Genomic Hybridization，CGH）是一种检测肿瘤中DNA拷贝数改变（DNA Copy Number Alterations，CNA）的分子生物学方法。然而，在后基因组时代，大多数癌症生物学的研究一直集中在基因表达谱（GEP），而不是CGH，因此，大量的GEP数据已经积累在公共数据库中的各种肿瘤类型。我们利用GEP数据资源来定义肿瘤中可能复发的CNA。此外，通过GEP鉴定的CNAs在疾病发病机制中将是功能上更相关的CNAs，因为CNAs的功能效应可以通过改变的基因表达来反映。我们提出了一种新的计算方法，创造虚拟CGH（vCGH），它采用隐马尔可夫模型（HHRM）预测DNA CNA从其相应的GEP数据。vCGH首先在从足够数量的肿瘤样本生成的成对GEP和CGH数据上进行训练，然后应用于新肿瘤样本的GEP数据以预测其CNA。通过对190例弥漫性大B细胞淋巴瘤（DLBCL）进行交叉验证，vCGH对CNA预测的灵敏度为80%，特异性为90%，准确性为90%。vCGH确定的DLBCL复发区域与实验CGH结果基本一致，包括1 q，2 p16-p14，3q 27-q29，6p 25-p21，7，11 q，12和18 q21的增加，6 q，8 p23-p21，9 p24-p21和17 p13的丢失。此外，vCGH还预测了CGH中未观察到的一些复发性功能异常，包括1 p、2 q和6 q的增加和1 q、6p和8 q的丢失。其中1 q、6 q和8 q与DLBCL患者的临床预后显著相关（p < 0.05）。我们开发了一种新的计算方法，vCGH，从淋巴瘤的GEP数据预测全基因组遗传异常。vCGH通常可应用于其他类型的肿瘤，并可显著增强癌症研究中功能重要的遗传异常的检测。
Comparative Genomic Hybridization (CGH) is a molecular approach for detecting DNA Copy Number Alterations (CNAs) in tumor, which are among the key causes of tumorigenesis. However in the post-genomic era, most studies in cancer biology have been focusing on Gene Expression Profiling (GEP) but not CGH, and as a result, an enormous amount of GEP data had been accumulated in public databases for a wide variety of tumor types. We exploited this resource of GEP data to define possible recurrent CNAs in tumor. In addition, the CNAs identified by GEP would be more functionally relevant CNAs in the disease pathogenesis since the functional effects of CNAs can be reflected by altered gene expression. We proposed a novel computational approach, coined virtual CGH (vCGH), which employs hidden Markov models (HMMs) to predict DNA CNAs from their corresponding GEP data. vCGH was first trained on the paired GEP and CGH data generated from a sufficient number of tumor samples, and then applied to the GEP data of a new tumor sample to predict its CNAs. Using cross-validation on 190 Diffuse Large B-Cell Lymphomas (DLBCL), vCGH achieved 80% sensitivity, 90% specificity and 90% accuracy for CNA prediction. The majority of the recurrent regions defined by vCGH are concordant with the experimental CGH, including gains of 1q, 2p16-p14, 3q27-q29, 6p25-p21, 7, 11q, 12 and 18q21, and losses of 6q, 8p23-p21, 9p24-p21 and 17p13 in DLBCL. In addition, vCGH predicted some recurrent functional abnormalities which were not observed in CGH, including gains of 1p, 2q and 6q and losses of 1q, 6p and 8q. Among those novel loci, 1q, 6q and 8q were significantly associated with the clinical outcomes in the DLBCL patients (p < 0.05). We developed a novel computational approach, vCGH, to predict genome-wide genetic abnormalities from GEP data in lymphomas. vCGH can be generally applied to other types of tumors and may significantly enhance the detection of functionally important genetic abnormalities in cancer research.
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通讯作者：
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