Institute for Protein Research, Osaka University, Suita, Japan.
Center for Drug Design and Research, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Japan.
FEBS J. 2022 Jan;289(1):90-101. doi: 10.1111/febs.15831. Epub 2021 May 14.
Cancer progresses due to changes in the dynamic interactions of multidimensional factors associated with gene mutations. Cancer research has actively adopted computational methods, including data-driven and mathematical model-driven approaches, to identify causative factors and regulatory rules that can explain the complexity and diversity of cancers. A data-driven, statistics-based approach revealed correlations between gene alterations and clinical outcomes in many types of cancers. A model-driven mathematical approach has elucidated the dynamic features of cancer networks and identified the mechanisms of drug efficacy and resistance. More recently, machine learning methods have emerged that can be used for mining omics data and classifying patient. However, as the strengths and weaknesses of each method becoming apparent, new analytical tools are emerging to combine and improve the methodologies and maximize their predictive power for classifying cancer subtypes and prognosis. Here, we introduce recent advances in cancer systems biology aimed at personalized medicine, with focus on the receptor tyrosine kinase signaling network.
癌症是由于与基因突变相关的多维因素的动态相互作用的变化而进展的。癌症研究积极采用计算方法，包括数据驱动和数学模型驱动的方法，以确定能够解释癌症复杂性和多样性的因果因素和调节规则。基于数据的统计方法揭示了许多类型的癌症中基因改变与临床结果之间的相关性。基于模型的数学方法阐明了癌症网络的动态特征，并确定了药物疗效和耐药性的机制。最近，出现了可以用于挖掘组学数据和分类患者的机器学习方法。然而，随着每种方法的优缺点变得明显，新的分析工具正在涌现，以组合和改进方法，并最大限度地提高其对癌症亚型和预后分类的预测能力。在这里，我们介绍了癌症系统生物学的最新进展，旨在实现个性化医疗，重点介绍受体酪氨酸激酶信号网络。
