From signatures to models: understanding cancer using microarrays
From signatures to models: understanding cancer using microarrays
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DOI：
10.1038/ng1561
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发表时间：
2005-06-01
期刊：
影响因子：
30.8
通讯作者：
Koller, D
中科院分区：
文献类型：
--
作者：
Segal, E;Friedman, N;Koller, D
基因组学通过提供前所未有的病理分子基础的全面视图，有可能彻底改变癌症的诊断和管理。计算分析对于将生成的大量数据转化为对疾病的机制理解至关重要。在这里，我们回顾了目前的研究旨在揭示模块化组织和功能的转录网络和反应在癌症。我们首先描述了从更高层次模块的角度分析生物过程的方法如何识别疾病机制的强大特征。然后，我们将讨论旨在确定这些模块和过程背后的监管机制的方法。最后，我们展示了将人类数据与模式生物相结合的比较分析如何能够产生更有力的发现。最后，我们讨论了将这些方法从细胞推广到组织的挑战，以及它们为改善癌症诊断和管理提供的机会。
Genomics has the potential to revolutionize the diagnosis and management of cancer by offering an unprecedented comprehensive view of the molecular underpinnings of pathology. Computational analysis is essential to transform the masses of generated data into a mechanistic understanding of disease. Here we review current research aimed at uncovering the modular organization and function of transcriptional networks and responses in cancer. We first describe how methods that analyze biological processes in terms of higher-level modules can identify robust signatures of disease mechanisms. We then discuss methods that aim to identify the regulatory mechanisms underlying these modules and processes. Finally, we show how comparative analysis, combining human data with model organisms, can lead to more robust findings. We conclude by discussing the challenges of generalizing these methods from cells to tissues and the opportunities they offer to improve cancer diagnosis and management.