Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065.
School of Natural Sciences, Institute of Advanced Study, Princeton, NJ 08540;
Proc Natl Acad Sci U S A. 2020 Jul 14;117(28):16339-16345. doi: 10.1073/pnas.2002179117. Epub 2020 Jun 29.
We present a technique to construct a simplification of a feature network which can be used for interactive data exploration, biological hypothesis generation, and the detection of communities or modules of cofunctional features. These are modules of features that are not necessarily correlated, but nevertheless exhibit common function in their network context as measured by similarity of relationships with neighboring features. In the case of genetic networks, traditional pathway analyses tend to assume that, ideally, all genes in a module exhibit very similar function, independent of relationships with other genes. The proposed technique explicitly relaxes this assumption by employing the comparison of relational profiles. For example, two genes which always activate a third gene are grouped together even if they never do so concurrently. They have common, but not identical, function. The comparison is driven by an average of a certain computationally efficient comparison metric between Gaussian mixture models. The method has its basis in the local connection structure of the network and the collection of joint distributions of the data associated with nodal neighborhoods. It is benchmarked on networks with known community structures. As the main application, we analyzed the gene regulatory network in lung adenocarcinoma, finding a cofunctional module of genes including the pregnancy-specific glycoproteins (PSGs). About 20% of patients with lung, breast, uterus, and colon cancer in The Cancer Genome Atlas (TCGA) have an elevated PSG+ signature, with associated poor group prognosis. In conjunction with previous results relating PSGs to tolerance in the immune system, these findings implicate the PSGs in a potential immune tolerance mechanism of cancers.
我们提出了一种构建特征网络简化版的技术，该技术可用于交互式数据探索、生物假设生成，以及共同功能特征的社区或模块的检测。这些模块中的特征不一定相关，但在其网络环境中，通过与相邻特征关系的相似性来衡量，它们具有共同的功能。在遗传网络的情况下，传统的途径分析往往假设，理想情况下，模块中的所有基因都具有非常相似的功能，与其他基因无关。所提出的技术通过比较关系档案来明确放宽这一假设。例如，两个基因总是激活第三个基因，即使它们从不同时这样做，也会被归为一组。它们具有共同的但不相同的功能。比较是由高斯混合模型之间的特定计算效率比较度量的平均值驱动的。该方法基于网络的局部连接结构和与节点邻域相关的数据联合分布的集合。它在具有已知社区结构的网络上进行基准测试。作为主要应用，我们分析了肺腺癌的基因调控网络，发现了一个包括妊娠特异性糖蛋白（PSG）的共同功能基因模块。癌症基因组图谱（TCGA）中约 20%的肺癌、乳腺癌、子宫癌和结肠癌患者具有升高的 PSG+特征，预后较差。结合 PSGs 与免疫系统耐受相关的先前结果，这些发现表明 PSGs 可能参与了癌症的潜在免疫耐受机制。
