Segal Eran, Friedman Nir, Kaminski Naftali, Regev Aviv, Koller Daphne
Center for Studies in Physics and Biology, Rockefeller University, New York, USA.
Nat Genet. 2005 Jun;37 Suppl:S38-45. doi: 10.1038/ng1561.
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.
基因组学有潜力通过以前所未有的全面视角展现病理学的分子基础，从而彻底改变癌症的诊断和管理方式。计算分析对于将大量生成的数据转化为对疾病的机制性理解至关重要。在此，我们综述了当前旨在揭示癌症中转录网络的模块化组织和功能以及相关反应的研究。我们首先描述了如何从更高级别的模块角度分析生物过程的方法能够识别疾病机制的可靠特征。然后我们讨论旨在识别这些模块和过程背后调控机制的方法。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得出更可靠的结果。我们通过讨论将这些方法从细胞推广到组织所面临的挑战以及它们为改善癌症诊断和管理所带来的机遇来结束本文。
