Role of metabolic crosstalk in determining immunity during tumor progression

代谢串扰在肿瘤进展过程中决定免疫的作用

• 批准号: 10718070
• 负责人: ANA C ANDERSON
• 金额: $ 51.39万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-14 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10718070
• 关键词: Acetylation; Acetyltransferase; Affect; Algorithmic Analysis; Autoimmunity; Biochemical Pathway; CD8-Positive T-Lymphocytes; Cell physiology; Cells; Cellular Metabolic Process; Computing Methodologies; Data; Data Set; Disabled Persons; Ecosystem; Enzymes; Equilibrium; Genes; Goals; Immune; Immune Targeting; Immune Tolerance; Immune system; Immunity; In Situ; Individual; Knowledge; Malignant Neoplasms; Maps; Mediating; Melanoma Cell; Metabolic; Metabolism; Methods; Modeling; Morbidity - disease rate; Mus; Mutant Strains Mice; Myelogenous; Myeloid Cells; Myeloid-derived suppressor cells; Network-based; Outcome; Oxidative Phosphorylation; Pathway interactions; Phenotype; Polyamines; Population; Production; Productivity; Proliferating; Recycling; Regulatory T-Lymphocyte; Resolution; Role; S100A9 gene; Shapes; Spermidine; Spermine; Symbiosis; System; Therapeutic Intervention; Time; Tissue Harvesting; Tissues; Tumor Immunity; cancer cell; cancer therapy; cell type; conditional mutant; design; effector T cell; exhaust; fighting; immune cell infiltrate; in silico; in vivo; longitudinal analysis; melanoma; metabolic profile; metabolomics; mortality; mouse model; neoplastic cell; novel; nutrient deprivation; pre-clinical; response; single-cell RNA sequencing; stemness; targeted treatment; tool; transcriptomics; tumor; tumor microenvironment; tumor progression; wasting

PROJECT SUMMARY Tumor cells and infiltrating immune cells co-evolve during the course of tumor progression, with the immune system progressively losing its efficacy as tumors advance. Immune cells are highly dependent on cellular metabolism to manifest their effector functions, but in the tumor microenvironment (TME), ‘metabolic competition’ with rapidly proliferating cancer cells leads to nutrient deprivation alongside increased metabolic waste, both of which negatively impact immune cell function. Moreover, ‘metabolic symbiosis’ between cancer and immune cells can promote the acquisition of suppressive immune cell phenotypes, ultimately disfavoring anti-tumor immunity. These dynamic metabolic interactions are shaped by the niches occupied by cells within the TME. Thus, the metabolic cross talk between tumor and immune cells over the course of tumor progression can be a major determinant of immune cell function and, consequently, response to immune-targeted therapies. Unfortunately, the study of immune cell metabolism and crosstalk in the TME has been very challenging due to reliance on methods that analyze metabolism in bulk cell populations, which obscures individual cellular diversity, difficulties in predicting downstream outcomes of metabolic perturbations because of the complexity of the metabolic network, and the lack of tools to map metabolic alterations in situ. To overcome these challenges, we developed Compass, a flux balance analysis (FBA) algorithm that applies a network-based analytical approach to single-cell RNA-sequencing (scRNA-seq) data to predict metabolic states of individual cells in tissue1. We have applied Compass as well as standard computational methods to the analysis of longitudinal scRNA-seq data from a pre-clinical murine model of melanoma with the goal of determining the temporal- and tumor-size- based metabolic alterations in both tumor and infiltrating immune cells during tumor progression. Our preliminary data indicate that polyamine metabolism is a key hub of metabolic crosstalk between tumor and immune cells that are either static or dynamic over the course of melanoma tumor progression and that may occupy distinct tissue niches. We find that CD4+ regulatory T cells (Treg), exhausted CD8+ T cells, and suppressive myeloid cells upregulate spermine/spermidine acetyltransferase (Sat1), which catalyzes acetylation of polyamines, with tumor progression. Conversely, a subset of c-Met+ melanoma cells that has features of stemness is high for polyamine recycling genes. Based on these observations, we hypothesize that that systems-based analysis of the alterations and crosstalk involving polyamine metabolism in tumor and immune cells during tumor progression will uncover novel means for therapeutic intervention. We propose to: 1) Dissect the functional role of polyamine metabolism in immune cells and tumor cells during tumor progression; 2) Construct a high resolution spatial map of tumor:immune metabolic crosstalk via the polyamine pathway.

项目摘要 肿瘤细胞和浸润性免疫细胞在肿瘤进展过程中共同进化，其中免疫细胞在肿瘤进展过程中相互作用。 随着肿瘤的进展，系统逐渐失去功效。免疫细胞高度依赖于 在肿瘤微环境（TME）中，“代谢竞争” 与快速增殖的癌细胞一起导致营养缺乏以及代谢废物增加， 对免疫细胞功能有负面影响此外，癌症和免疫系统之间的“代谢共生” 细胞可以促进获得抑制性免疫细胞表型，最终不利于抗肿瘤 免疫力这些动态代谢相互作用由TME内细胞占据的小生境形成。 因此，在肿瘤进展过程中肿瘤和免疫细胞之间的代谢串扰可能是一个重要的因素。 免疫细胞功能的主要决定因素，因此，对免疫靶向治疗的反应。 不幸的是，TME中的免疫细胞代谢和串扰的研究由于以下原因而非常具有挑战性： 依赖于分析大量细胞群中代谢的方法，这掩盖了个体细胞的多样性， 由于代谢紊乱的复杂性，难以预测代谢紊乱的下游结果。 代谢网络，以及缺乏原位绘制代谢改变的工具。为了克服这些挑战，我们 开发了Compass，这是一种应用基于网络的分析方法的通量平衡分析（FBA）算法 单细胞RNA测序（scRNA-seq）数据来预测组织中单个细胞的代谢状态。我们 我已经将Compass以及标准计算方法应用于纵向scRNA-seq分析， 来自黑素瘤的临床前鼠模型的数据，其目的是确定时间和肿瘤大小， 基于肿瘤进展期间肿瘤和浸润免疫细胞的代谢改变。 我们的初步数据表明，多胺代谢是肿瘤和肿瘤细胞之间代谢串扰的关键枢纽。 免疫细胞在黑色素瘤肿瘤进展过程中是静态的或动态的， 占据着不同的组织生态位我们发现，CD4+调节性T细胞（Treg），耗尽的CD8 + T细胞， 抑制性骨髓细胞上调精胺/亚精胺乙酰转移酶（Sat1），其催化乙酰化 与肿瘤进展有关。相反，具有以下特征的c-Met+黑色素瘤细胞亚群： 多胺再循环基因的干性高。基于这些观察，我们假设， 系统分析肿瘤多胺代谢的改变和干扰， 肿瘤进展过程中的免疫细胞将揭示治疗干预的新手段。我们 本文拟从以下几个方面进行研究：1）探讨多胺代谢在免疫细胞和肿瘤细胞中的功能作用 2）构建肿瘤的高分辨率空间图：通过多胺的免疫代谢串扰 通路