Cross-study projections of genomic biomarkers: an evaluation in cancer genomics.

Cross-study projections of genomic biomarkers: an evaluation in cancer genomics.
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DOI:
10.1371/journal.pone.0004523
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发表时间:
2009
期刊:
影响因子:
3.7
通讯作者:
West M
West M
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Lucas JE;Carvalho CM;Chen JL;Chi JT;West M

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在临床/观察和实验/对照研究中使用DNA微阵列的人类疾病研究正在对我们对人类疾病复杂性的理解产生越来越大的影响。一个基本概念是使用基因表达作为一种“通用货币”,将体外控制实验的结果与体内观察性人体研究联系起来。许多关于癌症和其他疾病的研究已经显示出利用体外细胞操作来提高对体内生物学的理解的希望,但实验往往无法反映人类疾病中所见的巨大表型变异。我们用一个框架和方法来解决这个问题,以解剖,增强和扩展体外衍生基因表达特征的体内效用。从实验定义的基因表达特征中,我们使用统计因子分析来生成人类癌症基因表达数据中的多个定量因子。这些因素保留了它们与原始的、一维的体外特征的关系,但更好地描述了体内生物学的多样性。在一项乳腺癌分析中,我们表明,这些因素可以反映与人类癌症的分子和临床特征相关的根本不同的生物学过程,并将它们结合起来可以改善临床结果的预测。
Human disease studies using DNA microarrays in both clinical/observational and experimental/controlled studies are having increasing impact on our understanding of the complexity of human diseases. A fundamental concept is the use of gene expression as a “common currency” that links the results of in vitro controlled experiments to in vivo observational human studies. Many studies – in cancer and other diseases – have shown promise in using in vitro cell manipulations to improve understanding of in vivo biology, but experiments often simply fail to reflect the enormous phenotypic variation seen in human diseases. We address this with a framework and methods to dissect, enhance and extend the in vivo utility of in vitro derived gene expression signatures. From an experimentally defined gene expression signature we use statistical factor analysis to generate multiple quantitative factors in human cancer gene expression data. These factors retain their relationship to the original, one-dimensional in vitro signature but better describe the diversity of in vivo biology. In a breast cancer analysis, we show that factors can reflect fundamentally different biological processes linked to molecular and clinical features of human cancers, and that in combination they can improve prediction of clinical outcomes.
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