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

黑色素瘤肿瘤微环境中细胞间通讯网络和免疫活性的系统分析

• 批准号: 10696224
• 负责人: MARCUS W BOSENBERG
• 金额: $ 55万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10696224
• 关键词: Address; Area; Biology; CTLA4 blockade; Cancer Patient; Cell Communication; Cell model; Cell secretion; Cells; Classification; Clinical; Collecting Cell; Communication; Complex; Computer Models; Computing Methodologies; Data; Fibroblasts; Goals; Human; Immune; Immune checkpoint inhibitor; Immune response; Immune system; Immunologics; Immunotherapy; Laboratory Study; Ligands; Lymphocyte; Macrophage; Malignant Neoplasms; Maps; Measurement; Mediating; Metastatic Melanoma; Mission; Modeling; Mus; Myeloid Cells; Pathology; Patients; Population; Property; Public Health; Publishing; Receptor Cell; Research; Resistance; Sampling; Shapes; Signal Transduction; Systems Analysis; Systems Biology; T cell infiltration; T cell response; T-Lymphocyte; Testing; Therapeutic; Time; Translating; adaptive immunity; anti-cancer; anti-tumor immune response; cancer cell; cancer immunotherapy; cancer regression; cancer therapy; cell type; checkpoint therapy; chemokine; combinatorial; cytokine; design; functional plasticity; improved; in vivo; innovation; intercellular communication; melanoma; monocyte; mouse model; neoplastic cell; next generation; novel therapeutic intervention; patient subsets; pharmacologic; predictive modeling; programmed cell death protein 1; receptor; receptor expression; response; single cell analysis; single-cell RNA sequencing; support network; therapeutic target; translational impact; tumor; tumor heterogeneity; tumor microenvironment; tumor progression; tumor-immune system interactions

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.

项目摘要 了解在肿瘤中建立免疫活性的动态细胞间通讯网络 微环境（TME）将改变治疗策略，以帮助对治疗无反应的癌症患者。 检查点抑制剂（CPIs）。为此，本提案的总体目标是确定 细胞间分泌的信号，区分无效的肿瘤免疫应答与有效的肿瘤免疫应答， 确定新的靶点，以提高癌症免疫疗法（CIT）的疗效。为了实现这一目标， 将对黑色素瘤小鼠模型和人黑色素瘤样品进行单细胞分析 患者响应于T细胞的CPI靶向，和靶向肿瘤相关单核细胞的CIT， 巨噬细胞（TAM）。这些数据将被计算分析以构建细胞间相互作用网络 间质细胞、肿瘤细胞和免疫细胞之间的相互作用，以确定维持免疫抑制性TME的相互作用， 预测如何瞄准它们，并通过实验测试这些预测。本研究中检验的中心假设 TME中TAM和其他细胞之间的细胞间信号网络是 抑制免疫活性，TAM介导的网络对于重建有效的TME至关重要 免疫应答，特别是在由于T细胞应答不足导致CPI抗性的情况下。的理由 提出的研究是，确定细胞间的相互作用，区分免疫抑制与 免疫支持性TME将作为在无反应肿瘤中测试的新靶点的路线图。目标1将 开发计算模型，可以区分无效与有效的抗肿瘤免疫 反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 来自生长和退化鼠和人的单细胞RNA测序（scRNA-seq）和病理学数据， 黑素瘤这些模型将用于识别介导细胞-细胞相互作用的靶点，这些靶点将被验证。 实验性的目标2的目的是确定巨噬细胞的功能可塑性如何， 其它骨髓细胞有助于小鼠和人黑素瘤中的免疫抑制性TME。相互作用组 将扩大图谱以确定肿瘤中髓样细胞亚群和其他细胞类型之间的相互作用 随着时间的推移和治疗。这项研究之所以具有创新性，是因为它不仅关注 隔离的端点（例如，T细胞浸润），它将识别稳定细胞间通讯的网络， 这些端点。关于癌症系统生物学，拟议的研究是创新的，因为它将 联合收割机新的计算方法-用于从scRNA-seq数据定义细胞亚群和网络相互作用 用同基因小鼠黑色素瘤模型和人类患者 非常适合评估CIT反应的样本。这项研究意义重大，因为它将 将TME中免疫活性的标志重新定义为多个细胞-细胞相互作用的新兴特性， 可以被靶向设计对无反应患者有效的免疫疗法。

项目成果

Reframing macrophage diversity with network motifs.

• DOI: 10.1016/j.it.2023.10.009
• 发表时间: 2023-11
• 期刊: Trends in immunology
• 影响因子: 16.8
• 作者: Gabriela A Pizzurro;K. Miller-Jensen

Langerhans cells are essential components of the angiogenic niche during murine skin repair.

• DOI: 10.1016/j.devcel.2022.11.012
• 发表时间: 2022-12
• 期刊: Developmental cell
• 影响因子: 11.8
• 作者: Renee R. Wasko;Kate Bridges;Rebecca Pannone;Ikjot Sidhu;Yue Xing;S. Naik;K. Miller-Jensen;V. Horsley

Mapping and Validation of scRNA-Seq-Derived Cell-Cell Communication Networks in the Tumor Microenvironment.

• DOI: 10.3389/fimmu.2022.885267
• 发表时间: 2022
• 期刊: Frontiers in immunology
• 影响因子: 7.3

3D Model of the Early Melanoma Microenvironment Captures Macrophage Transition into a Tumor-Promoting Phenotype.

• DOI: 10.3390/cancers13184579
• 发表时间: 2021-09-12
• 期刊: Cancers
• 影响因子: 5.2
• 作者: Pizzurro GA;Liu C;Bridges K;Alexander AF;Huang A;Baskaran JP;Ramseier J;Bosenberg MW;Mak M;Miller-Jensen K
• 通讯作者: Miller-Jensen K