Identifying causal networks linking cancer processes and anti-tumor immunity using Bayesian network inference and metagene constructs.
Identifying causal networks linking cancer processes and anti-tumor immunity using Bayesian network inference and metagene constructs.
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DOI：
10.1002/btpr.2230
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发表时间：
2016-03
影响因子：
2.9
通讯作者：
Klinke DJ 2nd
中科院分区：
文献类型：
--
作者：
Kaiser JL;Bland CL;Klinke DJ 2nd
癌症的发生源于维持系统内平衡的细胞内和细胞间网络的放松调控。确定这些网络的架构以及它们在癌症中是如何改变的，是设计恢复体内平衡的药物的先决条件。由于细胞间网络只出现在完整的系统中，使用许多常见的实验模型很难确定这些网络在人类癌症中是如何改变的。为了克服这一点，我们使用来自人类乳腺癌的癌症基因组图谱(TCGA)数据库的正常和恶性人类组织样本的多样性来确定与体内细胞间网络相关的拓扑结构。为了改善潜在的生物信号，我们使用元基因结构构建了贝叶斯网络，元基因结构代表了与不同免疫和癌症状态相关联的基因组。我们还使用Bootstrap重采样来建立与所推断的网络相关联的重要性。简而言之，我们发现细胞增殖和上皮向间充质转化(EMT)与巨噬细胞极化之间存在相反的关系。这些结果在多种癌症中是一致的，因为增殖与1型细胞介导的抗肿瘤免疫反应有关，而EMT与促肿瘤抗炎反应有关。为了解决这些网络从其他数据集中的可识别性，当贝叶斯网络仅从恶性样本中产生时，我们可以用更少的样本来识别EMT和巨噬细胞极化之间的关系。然而，当样本取自正常和恶性样本的组合时，用较少的样本确定了巨噬细胞增殖和极化之间的关系。
Cancer arises from a deregulation of both intracellular and intercellular networks that maintain system homeostasis. Identifying the architecture of these networks and how they are changed in cancer is a pre-requisite for designing drugs to restore homeostasis. Since intercellular networks only appear in intact systems, it is difficult to identify how these networks become altered in human cancer using many of the common experimental models. To overcome this, we used the diversity in normal and malignant human tissue samples from the Cancer Genome Atlas (TCGA) database of human breast cancer to identify the topology associated with intercellular networks in vivo. To improve the underlying biological signals, we constructed Bayesian networks using metagene constructs, which represented groups of genes that are concomitantly associated with different immune and cancer states. We also used bootstrap resampling to establish the significance associated with the inferred networks. In short, we found opposing relationships between cell proliferation and epithelial-to-mesenchymal transformation (EMT) with regards to macrophage polarization. These results were consistent across multiple carcinomas in that proliferation was associated with a type 1 cell-mediated anti-tumor immune response and EMT was associated with a pro-tumor anti-inflammatory response. To address the identifiability of these networks from other datasets, we could identify the relationship between EMT and macrophage polarization with fewer samples when the Bayesian network was generated from malignant samples alone. However, the relationship between proliferation and macrophage polarization was identified with fewer samples when the samples were taken from a combination of the normal and malignant samples.