The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel.
Front Immunol. 2018 Dec 11;9:2913. doi: 10.3389/fimmu.2018.02913. eCollection 2018.
The T cell repertoire potentially presents complexity compatible, or greater than, that of the human brain. T cell based immune response is involved with practically every part of human physiology, and high-throughput biology needed to follow the T-cell repertoire has made great leaps with the advent of massive parallel sequencing [1]. Nevertheless, tools to handle and observe the dynamics of this complexity have only recently started to emerge [e.g., 2, 3, 4] in parallel with sequencing technologies. Here, we present a network-based view of the dynamics of the T cell repertoire, during the course of mammary tumors development in a mouse model. The transition from the T cell receptor as a feature, to network-based clustering, followed by network-based temporal analyses, provides novel insights to the workings of the system and provides novel tools to observe cancer progression via the perspective of the immune system. The crux of the approach here is at the network-motivated clustering. The purpose of the clustering step is not merely data reduction and exposing structures, but rather to detect hubs, or attractors, within the T cell receptor repertoire that might shed light on the behavior of the immune system as a dynamic network. The is in fact an extension of the clone concept, i.e., instead of looking at particular clones we observe the extended clonal network by assigning clusters to graph nodes and edges to adjacent clusters (editing distance metric). Viewing the system as dynamical brings to the fore the notion of an attractors landscape, hence the possibility to chart this space and map the sample state at a given time to a vector in this large space. Based on this representation we applied two different methods to demonstrate its effectiveness in identifying changes in the repertoire that correlate with changes in the phenotype: (1) network analysis of the TCR repertoire in which two measures were calculated and demonstrated the ability to differentiate control from transgenic samples, and, (2) machine learning classifier capable of both stratifying control and trangenic samples, as well as to stratify pre-cancer and cancer samples.
T 细胞受体库具有与人类大脑相媲美的复杂性。T 细胞免疫反应几乎涉及人体生理学的每一个部分，高通量生物学需要跟踪 T 细胞受体库，随着大规模平行测序的出现[1]取得了巨大的飞跃。然而，只有在测序技术出现之后，才刚刚开始出现处理和观察这种复杂性的动态的工具[例如，2、3、4]。在这里，我们在小鼠模型的乳腺肿瘤发展过程中，呈现了 T 细胞受体库动态的基于网络的视图。从 T 细胞受体作为特征的转变，到基于网络的聚类，再到基于网络的时间分析，为系统的工作原理提供了新的见解，并提供了通过免疫系统观察癌症进展的新工具。这种方法的核心在于基于网络的聚类。聚类步骤的目的不仅是数据减少和暴露结构，而且是检测 T 细胞受体库中的枢纽或吸引子，这可能有助于了解免疫系统作为动态网络的行为。这种方法实际上是克隆概念的扩展，即我们不是观察特定的克隆，而是通过将集群分配给图节点并将边缘分配给相邻的集群（编辑距离度量）来观察扩展的克隆网络。将系统视为动态系统，突出了吸引子景观的概念，因此有可能绘制这个空间，并将给定时间的样本状态映射到这个大空间中的向量。基于这种表示，我们应用了两种不同的方法来证明其在识别与表型变化相关的受体库变化方面的有效性：（1）TCR 受体库的网络分析，其中计算了两个度量，并证明了能够区分对照和转基因样本的能力，以及（2）能够分层对照和转基因样本，以及分层癌前和癌症样本的机器学习分类器。
