Inferring pathway activity toward precise disease classification.
Inferring pathway activity toward precise disease classification.
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DOI：
10.1371/journal.pcbi.1000217
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发表时间：
2008-11
影响因子：
4.3
通讯作者：
Lee D
中科院分区：
文献类型：
--
作者：
Lee E;Chuang HY;Kim JW;Ideker T;Lee D
微阵列技术的出现使得基于患者的基因表达谱对疾病状态进行分类成为可能。通常，通过测量标记基因的表达谱的能力来选择标记基因，以区分不同疾病状态的患者。然而，由于组织样本内的细胞异质性和患者间的遗传异质性等因素，基于表达的分类在复杂疾病中可能具有挑战性。一个有前途的技术，以应对这些挑战是将路径信息纳入疾病分类程序，以分类疾病的基础上的活动，整个信号通路或蛋白质复合物，而不是对个别基因或蛋白质的表达水平。我们提出了一种新的分类方法的基础上推断每个病人的通路活动。对于每种途径，活性水平从其条件响应基因（CORG）的基因表达水平总结，条件响应基因定义为途径中基因的子集，其组合表达为疾病表型提供最佳区分能力。我们表明，对于简单和复杂的病例对照研究，包括区分受干扰细胞和非受干扰细胞以及对几种不同类型的癌症进行亚型分型，使用途径活性的分类器比基于个体基因表达的分类器具有更好的性能。此外，新方法优于使用静态（即，无条件）路径的定义。在一个途径中，鉴定的CORG可能有助于开发更好的诊断标志物和发现人类疾病的核心改变。微阵列技术的出现引起了人们对鉴定可作为疾病生物标志物的基因表达水平的极大兴趣。通过检查每个单独的基因来选择标记基因，以查看其表达水平如何区分不同的疾病类型。在癌症等复杂疾病中，由于组织内的细胞异质性和患者之间的遗传异质性，很难找到良好的标记基因。一个有前途的技术，以解决这些挑战是将生物学途径的信息纳入标记识别程序，允许疾病分类的基础上的整个途径的活动，而不是简单地对单个基因的表达水平。然而，以前的基于途径的方法并没有显着优于基于基因的方法。在这里，我们提出了一个新的基于路径的分类程序，其中标记编码不是作为单个基因，也不是作为一组基因组成一个已知的途径，但作为子集的“条件响应基因（CORG）”在这些途径。使用来自七个不同微阵列研究的表达谱，我们表明该方法的准确性显著优于传统的基于基因和途径的诊断。此外，所鉴定的CORG可能有助于开发有效的诊断标记物和发现疾病的分子机制。
The advent of microarray technology has made it possible to classify disease states based on gene expression profiles of patients. Typically, marker genes are selected by measuring the power of their expression profiles to discriminate among patients of different disease states. However, expression-based classification can be challenging in complex diseases due to factors such as cellular heterogeneity within a tissue sample and genetic heterogeneity across patients. A promising technique for coping with these challenges is to incorporate pathway information into the disease classification procedure in order to classify disease based on the activity of entire signaling pathways or protein complexes rather than on the expression levels of individual genes or proteins. We propose a new classification method based on pathway activities inferred for each patient. For each pathway, an activity level is summarized from the gene expression levels of its condition-responsive genes (CORGs), defined as the subset of genes in the pathway whose combined expression delivers optimal discriminative power for the disease phenotype. We show that classifiers using pathway activity achieve better performance than classifiers based on individual gene expression, for both simple and complex case-control studies including differentiation of perturbed from non-perturbed cells and subtyping of several different kinds of cancer. Moreover, the new method outperforms several previous approaches that use a static (i.e., non-conditional) definition of pathways. Within a pathway, the identified CORGs may facilitate the development of better diagnostic markers and the discovery of core alterations in human disease. The advent of microarray technology has drawn immense interest to identify gene expression levels that can serve as biomarkers for disease. Marker genes are selected by examining each individual gene to see how well its expression level discriminates different disease types. In complex diseases such as cancer, good marker genes can be hard to find due to cellular heterogeneity within the tissue and genetic heterogeneity across patients. A promising technique for addressing these challenges is to incorporate biological pathway information into the marker identification procedure, permitting disease classification based on the activity of entire pathways rather than simply on the expression levels of individual genes. However, previous pathway-based methods have not significantly outperformed gene-based methods. Here, we propose a new pathway-based classification procedure in which markers are encoded not as individual genes, nor as the set of genes making up a known pathway, but as subsets of “condition-responsive genes (CORGs)” within those pathways. Using expression profiles from seven different microarray studies, we show that the accuracy of this method is significantly better than both the conventional gene- and pathway- based diagnostics. Furthermore, the identified CORGs may facilitate the development of effective diagnostic markers and the discovery of molecular mechanisms underlying disease.
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影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
11.2
作者：
Banka, CL;Lund, CV;Eliceiri, BP
通讯作者：
Eliceiri, BP
影响因子：
64.8
作者：
Alizadeh, AA;Eisen, MB;Staudt, LM
通讯作者：
Staudt, LM
影响因子：
82.9
作者：
Beer, DG;Kardia, SLR;Hanash, S
通讯作者：
Hanash, S
DOI：
10.1016/j.biocel.2007.03.021
发表时间：
2007-01-01
影响因子：
4
作者：
Gatenby, Robert A.;Gillies, Robert J.
通讯作者：
Gillies, Robert J.