Systems analysis of cell-cell communication networks and immune activity in the melanoma tumor microenvironment

项目总结 了解在肿瘤中建立免疫活性的动态细胞-细胞通讯网络 微环境(TME)将改变治疗策略，以帮助无反应的癌症患者 到检查点抑制剂(CPIs)。为此，本提案的总体目标是确定 细胞间分泌信号，区分无效的肿瘤免疫反应和有效的肿瘤免疫反应，以便 确定新的靶点以提高癌症免疫治疗(CIT)的疗效。为了实现这一目标，广泛的 单细胞分析将在黑色素瘤小鼠模型和人类黑色素瘤样本上进行 患者对CPI靶向T细胞和CITs靶向肿瘤相关单核细胞和 巨噬细胞(TAM)。这些数据将被计算分析，以构建细胞-细胞相互作用网络 间质、肿瘤和免疫细胞之间的相互作用，以确定维持免疫抑制Tme的相互作用， 预测如何瞄准它们，并对这些预测进行实验测试。在这里检验的中心假说 建议认为TAMs和TME中其他细胞之间的细胞间信号网络是TME 抑制免疫活性，以及介导的网络对于重建有效的TME至关重要 免疫反应，特别是在由于T细胞反应不足而导致CPI抵抗的情况下。其基本原理是 这项拟议的研究是识别细胞之间的相互作用，区分免疫抑制和 免疫支持性TMES将作为测试无反应肿瘤的新靶点的路线图。目标1将 开发可以区分无效和有效抗肿瘤免疫的计算模型 回应。这些模型将通过构建受体-配体相互作用的细胞间网络来开发 来自单细胞RNA测序(scRNA-seq)和小鼠和人生长和退化的病理数据 黑色素瘤。这些模型将被用来确定将被验证的介导细胞-细胞相互作用的目标 试验性的。目标2的目标将是确定巨噬细胞的功能可塑性和 其他髓系细胞对小鼠和人类黑色素瘤的免疫抑制TME有贡献。互动体 MAP将被扩展以识别髓系细胞亚群与肿瘤中其他细胞类型之间的相互作用 随着时间的推移和治疗。这项拟议的研究具有创新性，因为它不是仅仅专注于 孤立的终点(例如，T细胞渗透)，它将识别稳定的细胞间通信网络 这些终端。在癌症系统生物学方面，这项拟议的研究具有创新性，因为它将 结合新的计算方法-根据scRNA-seq数据定义细胞子集和网络交互 和构建预测性分类模型-使用同基因小鼠黑色素瘤模型和人类患者 最适合评估CIT响应的样本。这项拟议的研究意义重大，因为它将 将TME中免疫活动的特征重新定义为多个细胞-细胞相互作用的紧急特性 可以在药理学上有针对性地设计对无反应患者有效的免疫疗法。

PROJECT SUMMARY Understanding the dynamic cell-cell communication networks that establish immunological activity in the tumor microenvironment (TME) would transform therapeutic strategies to help cancer patients that are unresponsive to checkpoint inhibitors (CPIs). To this end, the overall objective of this proposal is to determine networks of intercellular secreted signals that distinguish ineffective tumor-immune responses from effective ones in order to identify new targets to improve efficacy of cancer immunotherapy (CIT). To achieve this objective, extensive single-cell analysis will be performed on mouse models of melanoma and samples from human melanoma patients in response to CPI-targeting of T cells, and CITs targeting tumor associated monocytes and macrophages (TAMs). These data will be computationally analyzed to construct cell-cell interaction networks between stromal, tumor, and immune cells to identify interactions that maintain immunosuppressive TMEs, predict how to target them, and test these predictions experimentally. The central hypothesis tested in this proposal is that intercellular signaling networks between TAMs and other cells in the TME are central to suppressing immune activity, and that TAM-mediated networks are critical to reestablishing an effective TME immune response, especially in cases of CPI resistance due to inadequate T cell responses. The rationale for the proposed research is that identifying cell-cell interactions that distinguish immunosuppressive versus immunosupportive TMEs will serve as a roadmap of new targets to test in unresponsive tumors. Aim 1 will develop computational models that can distinguish between an ineffective versus effective anti-tumor immune response. These models will be developed by constructing intercellular networks of receptor-ligand interactions from single-cell RNA sequencing (scRNA-seq) and pathology data in growing and regressing murine and human melanoma tumors. The models will be used to identify targets mediating cell-cell interactions that will be validated experimentally. The objective of Aim 2 will be to determine how the functional plasticity of macrophages and other myeloid cells contributes to an immunosuppressive TME in mice and human melanomas. Interactome maps will be expanded to identify interactions between myeloid cell subsets and other cell types in the tumor over time and with treatment. The proposed research is innovative because, rather than focusing solely on isolated end points (e.g., T cell infiltration), it will identify the network of intercellular communication that stabilizes those endpoints. With respect to cancer systems biology, the proposed research is innovative because it will combine new computational methods–for defining cell subsets and network interactions from scRNA-seq data and constructing predictive classification models–with syngeneic mouse melanoma models and human patient samples that are ideally suited to evaluate CIT responses. The proposed research is significant because it will redefine the hallmarks of immune activity in the TME as emergent properties of multiple cell-cell interactions that can be pharmacologically targeted to design immunotherapies that will be effective on non-responding patients.