Identifying Early Events of Gene Expression in Breast Cancer with Systems Biology Phylogenetics
Identifying Early Events of Gene Expression in Breast Cancer with Systems Biology Phylogenetics
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DOI：
10.1159/000348433
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发表时间：
2013-01-01
影响因子：
1.7
通讯作者：
Amri, H.
中科院分区：
文献类型：
--
作者：
Abu-Asab, M. S.;Abu-Asab, N.;Amri, H.
先进的组学技术，如深度测序和光谱核型分析，在遗传、基因组、基因表达、表观遗传、蛋白质组和代谢组学水平上揭示了更多的癌症异质性。随着越来越多的新兴数据，数据分析的任务对于挖掘和建模变得至关重要，以更好地了解相关的潜在生物过程。然而，健康和患病人群内部和人群之间明显存在多层次的异质性，使生物数据的挖掘和解释变得复杂，特别是在处理数百至数万个变量时。异源性视网膜病变发生在许多疾病中，如癌症、自闭症、黄斑变性等。在癌症中，异质性阻碍了寻找早期检测的有效生物标志物，并且使发现克隆（驱动）和非克隆（非扩增或乘客）畸变的任务变得复杂。我们表明，亚型的癌症（标本的分类）应该是一个先验的步骤，以确定癌症的早期事件。研究肿瘤发生的早期事件可以在患病个体的组织学正常组织（HNTDI）上进行，因为它们很可能暴露于在其邻近组织中引起癌症的相同诱变损伤。HNTDI数据变量的极性评估，通过使用健康标本作为外群，其次是简约系统发育分析的应用程序，产生了一个分层分类的标本，揭示了早期事件的疾病个体发生在其亚型的共享衍生变化（异常变化）或synapomorphies在系统发育术语。版权所有（C）2013 S. Karger AG，巴塞尔
Advanced omics technologies such as deep sequencing and spectral karyotyping are revealing more of cancer heterogeneity at the genetic, genomic, gene expression, epigenetic, proteomic, and metabolomic levels. With this increasing body of emerging data, the task of data analysis becomes critical for mining and modeling to better understand the relevant underlying biological processes. However, the multiple levels of heterogeneity evident within and among populations, healthy and diseased, complicate the mining and interpretation of biological data, especially when dealing with hundreds to tens of thousands of variables. Heterogeneity occurs in many diseases, such as cancers, autism, macular degeneration, and others. In cancer, heterogeneity has hampered the search for validated biomarkers for early detection, and it has complicated the task of finding clonal (driver) and nonclonal (nonexpanded or passenger) aberrations. We show that subtyping of cancer (classification of specimens) should be an a priori step to the identification of early events of cancers. Studying early events in oncogenesis can be done on histologically normal tissues from diseased individuals (HNTDI), since they most likely have been exposed to the same mutagenic insults that caused the cancer in their neighboring tissues. Polarity assessment of HNTDI data variables by using healthy specimens as outgroup(s), followed by the application of parsimony phylogenetic analysis, produces a hierarchical classification of specimens that reveals the early events of the disease ontogeny within its subtypes as shared derived changes (abnormal changes) or synapomorphies in phylogenetic terminology. Copyright (C) 2013 S. Karger AG, Basel