An integrative multi-dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer.
An integrative multi-dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer.
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DOI：
10.1186/1752-0509-4-67
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发表时间：
2010-05-17
影响因子：
--
通讯作者：
Lam WL
中科院分区：
文献类型：
--
作者：
Chari R;Coe BP;Vucic EA;Lockwood WW;Lam WL
基因组学极大地改变了我们研究癌症的方法。例如，基因表达谱已被用于描绘癌症的亚型，并促进预测和预后特征的推导。用于遗传和表观遗传特征的高分辨率和全基因组描述的技术的出现使得能够鉴定肿瘤中的大量因果DNA事件。这提供了从各种技术平台产生的基因组和转录组数据的大规模整合的潜力，以更好地了解癌症。在这里，我们展示了多维基因组学数据分析如何能够破译破坏调控/信号级联和下游效应的机制。由于不是所有的基因表达变化在肿瘤中观察到的癌症发展的原因，我们展示了一种方法的基础上多协同破坏（MCD）分析的基因，有利于合理推断的异常基因和途径，否则将被忽视在单基因组维度的调查。值得注意的是，这是第一个全面的研究乳腺癌细胞的DNA拷贝数，洛和DNA甲基化状态的平行整合全基因组分析，以解释基因表达模式的变化。我们的研究结果表明，一个多维的方法来阐明事件，将逃脱传统的一维分析，因此，减少癌症基因发现的队列样本量的力量。
Genomics has substantially changed our approach to cancer research. Gene expression profiling, for example, has been utilized to delineate subtypes of cancer, and facilitated derivation of predictive and prognostic signatures. The emergence of technologies for the high resolution and genome-wide description of genetic and epigenetic features has enabled the identification of a multitude of causal DNA events in tumors. This has afforded the potential for large scale integration of genome and transcriptome data generated from a variety of technology platforms to acquire a better understanding of cancer. Here we show how multi-dimensional genomics data analysis would enable the deciphering of mechanisms that disrupt regulatory/signaling cascades and downstream effects. Since not all gene expression changes observed in a tumor are causal to cancer development, we demonstrate an approach based on multiple concerted disruption (MCD) analysis of genes that facilitates the rational deduction of aberrant genes and pathways, which otherwise would be overlooked in single genomic dimension investigations. Notably, this is the first comprehensive study of breast cancer cells by parallel integrative genome wide analyses of DNA copy number, LOH, and DNA methylation status to interpret changes in gene expression pattern. Our findings demonstrate the power of a multi-dimensional approach to elucidate events which would escape conventional single dimensional analysis and as such, reduce the cohort sample size for cancer gene discovery.
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影响因子：
8.8
作者：
通讯作者：
--
影响因子：
3.8
作者：
Cooke SL;Pole JC;Chin SF;Ellis IO;Caldas C;Edwards PA
通讯作者：
Edwards PA
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
3.6
作者：
Bachman, KE;Argani, P;Park, BH
通讯作者：
Park, BH
SIGMA2：癌症基因组，表观基因组和转录组综合基因组多维分析的系统。
影响因子：
3
作者：
Chari, Raj;Coe, Bradley P.;Wedseltoft, Craig;Benetti, Marie;Wilson, Ian M.;Vucic, Emily A.;MacAulay, Calum;Ng, Raymond T.;Lam, Wan L.
通讯作者：
Lam, Wan L.