A mathematical methodology for determining the temporal order of pathway alterations arising during gliomagenesis.
A mathematical methodology for determining the temporal order of pathway alterations arising during gliomagenesis.
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DOI：
10.1371/journal.pcbi.1002337
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发表时间：
2012-01
影响因子：
4.3
通讯作者：
Michor F
中科院分区：
文献类型：
--
作者：
Cheng YK;Beroukhim R;Levine RL;Mellinghoff IK;Holland EC;Michor F
人类癌症是由细胞中遗传变异的积累引起的。特别重要的是在恶性转化早期发生的变化，因为它们可能导致癌基因成瘾，因此代表了有希望的治疗干预靶点。我们之前已经描述了一种计算方法，称为追溯癌症的进化步骤（RESIC），以确定肿瘤发生过程中的遗传改变的时间序列，从肿瘤的横截面基因组数据在其完全转化阶段。由于属于特定信号通路的一组基因内的改变可能具有相似或等效的影响，因此我们将基于通路的系统生物学方法应用于RESIC方法。该方法用于确定在恶性转化期间特定途径的改变是否在早期或晚期发生。当应用于癌症基因组图谱（TCGA）项目的原发性胶质母细胞瘤（GBM）拷贝数数据时，RESIC确定了与继发性GBM事件顺序一致的途径改变的时间顺序。然后，我们将样本进一步细分为四个主要的GBM亚型，并确定每个亚型对总体结果的相对贡献：我们发现，总体排序适用于前神经亚型，但间充质样本不同。可能由于样本数量有限，无法确定神经和经典亚型的事件时间序列。此外，对于前神经亚型的样品，我们检测到两种不同的时间序列的事件：（i）RAS途径激活之后是TP 53失活，最后是PI 3 K2激活，以及（ii）RAS激活仅先于AKT激活。RESIC方法的这种扩展提供了一种进化的数学方法来识别驱动肿瘤发生的途径变化的时间序列，并可能有助于指导对癌症发展中信号重排的理解。癌症是一种致命的疾病，随着时间的推移，通过遗传变化的积累而发展。许多生物学模型不包含肿瘤形成和进展的时间方面，部分原因是难以通过生物学实验确定大多数癌症类型的事件顺序。我们以前开发了一种计算算法，即使考虑大量突变，我们也可以快速且经济有效地确定肿瘤中突变出现的顺序。在本文中，我们扩展了我们的方法，将癌症进展的共同途径的生物学知识结合起来。我们将这些技术应用于原发性胶质母细胞瘤，这是最常见的脑癌形式。我们发现，当考虑到所有样本时，出现了通路事件的时间序列;然而，胶质母细胞瘤的不同亚型在其事件的时间序列上有所不同。随着临床数据的可用，该算法也可以很容易地应用于其他癌症类型，显示出计算和数学工具在癌症研究中的优势。利用时间信息，癌症生物学家将能够开发更准确的肿瘤形成动物模型，并更多地了解突变如何及时相互作用，从而更好地治疗癌症。
Human cancer is caused by the accumulation of genetic alterations in cells. Of special importance are changes that occur early during malignant transformation because they may result in oncogene addiction and thus represent promising targets for therapeutic intervention. We have previously described a computational approach, called Retracing the Evolutionary Steps in Cancer (RESIC), to determine the temporal sequence of genetic alterations during tumorigenesis from cross-sectional genomic data of tumors at their fully transformed stage. Since alterations within a set of genes belonging to a particular signaling pathway may have similar or equivalent effects, we applied a pathway-based systems biology approach to the RESIC methodology. This method was used to determine whether alterations of specific pathways develop early or late during malignant transformation. When applied to primary glioblastoma (GBM) copy number data from The Cancer Genome Atlas (TCGA) project, RESIC identified a temporal order of pathway alterations consistent with the order of events in secondary GBMs. We then further subdivided the samples into the four main GBM subtypes and determined the relative contributions of each subtype to the overall results: we found that the overall ordering applied for the proneural subtype but differed for mesenchymal samples. The temporal sequence of events could not be identified for neural and classical subtypes, possibly due to a limited number of samples. Moreover, for samples of the proneural subtype, we detected two distinct temporal sequences of events: (i) RAS pathway activation was followed by TP53 inactivation and finally PI3K2 activation, and (ii) RAS activation preceded only AKT activation. This extension of the RESIC methodology provides an evolutionary mathematical approach to identify the temporal sequence of pathway changes driving tumorigenesis and may be useful in guiding the understanding of signaling rearrangements in cancer development. Cancer is a deadly disease that develops through the accumulation of genetic changes over time. Many biological models do not incorporate this temporal aspect of tumor formation and progression, in part due to the difficulty of determining the sequence of events through biological experimentation for most cancer types. We previously developed a computational algorithm with which we can quickly and cost-effectively determine the order in which mutations arise in the tumor even when large numbers of mutations are considered. In this paper, we extended our method to incorporate biological knowledge of the common pathways by which cancer progresses. We applied these techniques to primary glioblastoma, the most common form of brain cancer. We found that when all samples are taken into account, a temporal sequence of pathway events emerges; however, different subtypes of glioblastoma vary in their temporal sequence of events. This algorithm can also be easily applied to other cancer types as clinical data becomes available, showing the benefit of computational and mathematical tools in cancer research. Using temporal information, cancer biologists will be able to develop more accurate animal models of tumor formation and learn more about how mutations interact in time, thus leading to better treatments for cancer.
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影响因子：
3.7
作者：
Brennan C;Momota H;Hambardzumyan D;Ozawa T;Tandon A;Pedraza A;Holland E
通讯作者：
Holland E
影响因子：
3.7
作者：
Cerami E;Demir E;Schultz N;Taylor BS;Sander C
通讯作者：
Sander C
宇宙（癌症中的体细胞突变目录）数据库和网站。
影响因子：
8.8
作者：
Bamford, S;Dawson, E;Forbes, S;Clements, J;Pettett, R;Dogan, A;Flanagan, A;Teague, J;Futreal, PA;Stratton, MR;Wooster, R
通讯作者：
Wooster, R
DOI：
10.1073/pnas.0710052104
发表时间：
2007-12-11
影响因子：
11.1
作者：
Beroukhim, Rameen;Getz, Gad;Sellers, William R.
通讯作者：
Sellers, William R.
影响因子：
1.7
作者：
Beerenwinkel, N;Rahnenführer, J;Lengauer, T
通讯作者：
Lengauer, T