Integrative network-based approach identifies key genetic elements in breast invasive carcinoma.
Integrative network-based approach identifies key genetic elements in breast invasive carcinoma.
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DOI：
10.1186/1471-2164-16-s5-s2
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发表时间：
2015
期刊：
影响因子：
4.4
通讯作者：
Helms V
中科院分区：
文献类型：
--
作者：
Hamed M;Spaniol C;Zapp A;Helms V
乳腺癌是一种遗传异质性癌症，属于最常见且死亡率很高的类型。乳腺癌的治疗和预后将很大程度上受益于对驱动肿瘤发生过程的遗传关键驱动因素和主要决定因素的正确分类和识别。鉴于来自不同来源和实验的肿瘤基因组和表观基因组数据的可用性，需要新的综合方法来提高识别此类遗传关键驱动因素的可能性。我们在此提出了一种基于网络的综合方法，该方法能够通过整合来自基因表达、DNA 甲基化、miRNA 表达和体细胞突变数据集的信息，将调控网络相互作用与乳腺癌的发展联系起来。我们的结果表明，来自不同数据源的调控元件之间在相互调控影响和基因组邻近性方面具有很强的关联性。通过分析不同类型的调控相互作用、TF 基因、miRNA-mRNA 和体细胞变异的邻近分析，我们确定了 106 个基因、68 个 miRNA 和 9 个突变，它们是乳腺癌致癌过程的候选驱动因素。此外，我们还揭示了这些关键驱动因素与乳腺癌网络中其他要素之间的监管相互作用。有趣的是，大约三分之一已确定的驱动基因是已知抗癌药物的靶标，并且大多数已确定的关键 miRNA 与多个器官的癌发生有关。此外，已确定的驱动突变可能会对蛋白质功能造成破坏性影响。将构建的基因网络和确定的关键驱动因素与成熟的基于网络的方法进行比较。所提出的基于网络的方法实现的综合分子分析极大地扩展了我们对基因、miRNA 和突变的前瞻性基因组驱动因素的知识库。对于大部分已确定的关键驱动因素，有确凿的证据表明它们参与了乳腺癌的发展。我们的方法还揭示了由已确定的关键驱动因素组成的复杂的监管相互作用。这些基因组驱动因素可以在湿实验室中作为新药物靶点的潜在候选者进行进一步研究。这种综合方法可以以类似的方式应用于其他癌症类型、复杂疾病，或用于研究细胞分化过程。
Breast cancer is a genetically heterogeneous type of cancer that belongs to the most prevalent types with a high mortality rate. Treatment and prognosis of breast cancer would profit largely from a correct classification and identification of genetic key drivers and major determinants driving the tumorigenesis process. In the light of the availability of tumor genomic and epigenomic data from different sources and experiments, new integrative approaches are needed to boost the probability of identifying such genetic key drivers. We present here an integrative network-based approach that is able to associate regulatory network interactions with the development of breast carcinoma by integrating information from gene expression, DNA methylation, miRNA expression, and somatic mutation datasets. Our results showed strong association between regulatory elements from different data sources in terms of the mutual regulatory influence and genomic proximity. By analyzing different types of regulatory interactions, TF-gene, miRNA-mRNA, and proximity analysis of somatic variants, we identified 106 genes, 68 miRNAs, and 9 mutations that are candidate drivers of oncogenic processes in breast cancer. Moreover, we unraveled regulatory interactions among these key drivers and the other elements in the breast cancer network. Intriguingly, about one third of the identified driver genes are targeted by known anti-cancer drugs and the majority of the identified key miRNAs are implicated in cancerogenesis of multiple organs. Also, the identified driver mutations likely cause damaging effects on protein functions. The constructed gene network and the identified key drivers were compared to well-established network-based methods. The integrated molecular analysis enabled by the presented network-based approach substantially expands our knowledge base of prospective genomic drivers of genes, miRNAs, and mutations. For a good part of the identified key drivers there exists solid evidence for involvement in the development of breast carcinomas. Our approach also unraveled the complex regulatory interactions comprising the identified key drivers. These genomic drivers could be further investigated in the wet lab as potential candidates for new drug targets. This integrative approach can be applied in a similar fashion to other cancer types, complex diseases, or for studying cellular differentiation processes.