Modeling cancer progression via pathway dependencies.
Modeling cancer progression via pathway dependencies.
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通过途径依赖性建模癌症进展。
DOI：
10.1371/journal.pcbi.0040028
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发表时间：
2008-02
影响因子：
4.3
通讯作者：
Mukherjee, Sayan
中科院分区：
文献类型：
--
作者：
Edelman, Elena J.;Guinney, Justin;Chi, Jen-Tsan;Febbo, Phillip G.;Mukherjee, Sayan
癌症是一种异质性疾病，通常需要复杂的改变来驱动正常细胞变成恶性肿瘤，并最终转移到转移状态。某些遗传扰动与发病和进展有关。然而，在很大程度上，潜在的机制往往仍然难以捉摸。这些遗传扰动很可能反映在一组基因或途径的表达改变上，而不是单个基因的表达改变，因此需要对途径放松管制的模型来帮助理解肿瘤发生的机制。我们介绍了肿瘤进展的综合层次分析，发现哪些先验定义的途径是相关的，无论是在整个或在进展的特定步骤。途径相互作用网络被推断为这些相关的途径在进展的步骤。接下来是细化那些在特定疾病阶段表达差异最大的基因的相关途径。最后的分析推断出这些精细途径的基因相互作用网络。我们将这种方法应用于前列腺癌和黑色素瘤的模型进展，从而更深入地了解肿瘤发生的机制。我们的分析支持了之前的发现，即两种癌症类型中涉及细胞周期控制和增殖的几个通路的放松。我们分析的一个新发现是ErbB4与原发性前列腺癌之间的联系。癌症是一种复杂的疾病，有许多亚型，在发病、进展和对治疗的反应方面有很大的不同。更好地了解癌症的病因和机制应该有助于改善癌症的诊断、预后和治疗。仅在今年，癌症就将导致50多万美国人死亡。我们的研究说明了如何整合多个阶段的数据，并在调控途径或基因集的水平上对肿瘤发生进行建模，为癌症的发生、发展和侵袭的根本遗传原因提供了强有力的、可解释的新假设。我们的建模方法是第一个将多个微阵列数据集结合在一个真正整合的框架中的方法之一，该框架促进了一个或多个数据集中重要因素或途径的可解释性。我们将这种肿瘤进展分析应用于前列腺癌和黑色素瘤，以提供可以导致识别新的生物标志物的信息，并为遗传破坏如何改变特定细胞类型的行为提供基础。
Cancer is a heterogeneous disease often requiring a complexity of alterations to drive a normal cell to a malignancy and ultimately to a metastatic state. Certain genetic perturbations have been implicated for initiation and progression. However, to a great extent, underlying mechanisms often remain elusive. These genetic perturbations are most likely reflected by the altered expression of sets of genes or pathways, rather than individual genes, thus creating a need for models of deregulation of pathways to help provide an understanding of the mechanisms of tumorigenesis. We introduce an integrative hierarchical analysis of tumor progression that discovers which a priori defined pathways are relevant either throughout or in particular steps of progression. Pathway interaction networks are inferred for these relevant pathways over the steps in progression. This is followed by the refinement of the relevant pathways to those genes most differentially expressed in particular disease stages. The final analysis infers a gene interaction network for these refined pathways. We apply this approach to model progression in prostate cancer and melanoma, resulting in a deeper understanding of the mechanisms of tumorigenesis. Our analysis supports previous findings for the deregulation of several pathways involved in cell cycle control and proliferation in both cancer types. A novel finding of our analysis is a connection between ErbB4 and primary prostate cancer. Cancer is a complex disease with many subtypes that differ substantially with respect to their onset, progression, and response to treatment. Better understanding of the etiology and mechanism of cancer should help improve the diagnosis, prognosis, and treatment of cancer that will kill more than half a million Americans this year alone. Our study illustrates how integration of data over multiple stages and modeling tumorigenesis at the level of regulatory pathways or sets of genes provide robust and interpretable novel hypotheses concerning root genetic causes responsible for cancer initiation, progression, and invasion. Our modeling approach is one of the first approaches that combines multiple microarray datasets in a truly integrative framework that promotes the interpretability of important factors or pathways in one or more datasets. We apply this analysis of tumor progression to both prostate cancer and melanoma to provide information that can lead to the identification of novel biomarkers and give a basis for how genetic disruptions serve to alter actions in specific cell types.
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DOI：
10.1111/j.1600-0749.2006.00322.x
发表时间：
2006-08-01
期刊：
PIGMENT CELL RESEARCH
影响因子：
--
作者：
Hoek, Keith S.;Schlegel, Natalie C.;Dummer, Reinhard
通讯作者：
Dummer, Reinhard
影响因子：
7.8
作者：
Bialkowska, K;Kulkarni, S;Fox, JEB
通讯作者：
Fox, JEB
影响因子：
8
作者：
Kannan, K;Amariglio, N;Givol, D
通讯作者：
Givol, D
影响因子：
16
作者：
Garcia-Higuera, I;Taniguchi, T;D'Andrea, AD
通讯作者：
D'Andrea, AD
影响因子：
5.8
作者：
Barry, WT;Nobel, AB;Wright, FA
通讯作者：
Wright, FA