Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge MA, 02139, USA.
Discovery, Merrimack Pharmaceuticals, Inc., Cambridge MA, 02139, USA.
Cell Rep. 2018 Nov 6;25(6):1458-1468.e4. doi: 10.1016/j.celrep.2018.10.047.
Tumor ecosystems are composed of multiple cell types that communicate by ligand-receptor interactions. Targeting ligand-receptor interactions (for instance, with immune checkpoint inhibitors) can provide significant benefits for patients. However, our knowledge of which interactions occur in a tumor and how these interactions affect outcome is still limited. We present an approach to characterize communication by ligand-receptor interactions across all cell types in a microenvironment using single-cell RNA sequencing. We apply this approach to identify and compare the ligand-receptor interactions present in six syngeneic mouse tumor models. To identify interactions potentially associated with outcome, we regress interactions against phenotypic measurements of tumor growth rate. In addition, we quantify ligand-receptor interactions between T cell subsets and their relation to immune infiltration using a publicly available human melanoma dataset. Overall, this approach provides a tool for studying cell-cell interactions, their variability across tumors, and their relationship to outcome.
肿瘤生态系统由多种通过配体-受体相互作用进行通信的细胞类型组成。靶向配体-受体相互作用（例如，使用免疫检查点抑制剂）可为患者带来显著益处。然而，我们对于肿瘤中发生哪些相互作用以及这些相互作用如何影响结果的了解仍然有限。我们提出了一种使用单细胞 RNA 测序来描述微环境中所有细胞类型之间配体-受体相互作用的方法。我们应用此方法来鉴定和比较六种同基因小鼠肿瘤模型中存在的配体-受体相互作用。为了鉴定可能与结果相关的相互作用，我们将相互作用与肿瘤生长速度的表型测量值进行回归。此外，我们使用公开的人类黑色素瘤数据集来量化 T 细胞亚群之间的配体-受体相互作用及其与免疫浸润的关系。总体而言，这种方法为研究细胞-细胞相互作用、它们在肿瘤中的可变性以及它们与结果的关系提供了一种工具。
