Role of metabolic crosstalk in determining immunity during tumor progression

项目总结 肿瘤细胞和浸润性免疫细胞在肿瘤进展过程中共同进化，与免疫 随着肿瘤的进展，系统逐渐失去效力。免疫细胞高度依赖于细胞 代谢，以显示其效应功能，但在肿瘤微环境(TME)，‘代谢竞争’ 癌细胞的快速增殖导致营养缺乏，同时代谢废物增加，两者都 对免疫细胞功能产生负面影响。此外，癌症和免疫之间的“代谢共生” 细胞可以促进抑制免疫细胞表型的获得，最终不利于抗肿瘤 豁免权。这些动态的代谢相互作用是由TME内细胞所占据的生态位决定的。 因此，肿瘤和免疫细胞在肿瘤进展过程中的代谢串扰可能是一种 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中免疫细胞代谢和串扰的研究一直非常具有挑战性，因为 依赖于分析大量细胞群体新陈代谢的方法，这掩盖了个体细胞的多样性， 由于代谢扰动的复杂性，很难预测代谢扰动的下游结果 代谢网络，以及缺乏在原位绘制代谢变化图的工具。为了克服这些挑战，我们 开发了一种应用基于网络的分析方法的流量平衡分析(FBA)算法Compass 到单细胞RNA测序(scRNA-seq)数据，以预测组织中单个细胞的代谢状态。我们 将Compass和标准计算方法应用于纵向scRNA-seq的分析 来自临床前黑色素瘤小鼠模型的数据，目的是确定颞叶和肿瘤的大小- 基于肿瘤进展过程中肿瘤和浸润性免疫细胞的代谢变化。 我们的初步数据表明，多胺代谢是肿瘤和肿瘤之间代谢串扰的关键枢纽。 在黑色素瘤肿瘤进展过程中处于静态或动态状态的免疫细胞 占据不同的组织壁龛。我们发现，CD4+调节性T细胞(Treg)、耗尽的CD8+T细胞以及 抑制性髓系细胞上调精胺/亚精胺乙酰转移酶(SAT1)，从而催化乙酰化 多胺，随着肿瘤的发展。相反，c-Met+黑色素瘤细胞的子集具有以下特征 多胺循环基因的茎性很高。基于这些观察，我们假设 肿瘤细胞多胺代谢相关改变和串扰的系统分析 肿瘤进展过程中的免疫细胞将发现治疗干预的新手段。我们 提出：1)剖析免疫细胞和肿瘤细胞中多胺代谢在肿瘤发生中的作用 2)构建高分辨率肿瘤空间图：多胺诱导的免疫代谢串扰 路径。

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.