决定异体 CAR T 细胞排斥与扩增的细胞和分子机制
Cellular and molecular mechanisms determining allogeneic CAR T cell rejection and expansion.
我们评估了11例接受单一批次cemacabtagene ansegedleucel(cema-cel)治疗的大B细胞淋巴瘤患者,cemacabtagene ansegedleucel是一种异体抗CD19 CAR T产品。
英文原题:Single-cell RNA sequencing reveals immunological heterogeneity of the tumor microenvironment in acute myeloid leukemia.
本研究强调了AML微环境中的细胞异质性及复杂的免疫相互作用,为理解免疫逃逸提供了见解,并为未来的免疫治疗及靶向治疗策略提供了依据。
急性髓系白血病(AML)是一种以异常髓系增殖为特征的恶性肿瘤。尽管治疗取得了进展,复发仍很常见。理解肿瘤微环境(TME),特别是自然杀伤(NK)细胞的功能障碍及其相互作用,对于改进免疫治疗策略,如CAR-T 细胞和嵌合抗原受体NK 细胞(CAR-NK)疗法,至关重要。
本研究旨在通过单细胞RNA测序(scRNA-seq)表征AML TME,重点关注NK细胞功能障碍及其与免疫抑制细胞群体的相互作用。
我们分析了来自公共来源的59个样本的单细胞RNA测序数据,其中包括40个AML样本和19个健康供者(HD)样本,并使用标准方法进行处理。经过质量控制和双细胞去除后,保留了284,687个高质量细胞。通过簇特异性占用评分和推断的拷贝数变异来识别急性髓系白血病恶性细胞。使用Harmony校正批次效应,随后使用SingleR和既定标记进行聚类和细胞类型注释。我们通过基因集变异分析(GSVA)进行了通路活性分析,使用CellChat探索了细胞间通讯,并使用Monocle3推断了细胞轨迹。使用精选基因集评估了NK细胞亚群的功能特征,并使用pySCENIC评估了转录调控。
对40例AML和19例HD样本进行单细胞RNA测序,鉴定出76个细胞簇,其中包括具有升高的拷贝数变异(CNVs)的患者特异性恶性细胞。急性髓系白血病样本显示出免疫组成改变,浆细胞和造血干细胞(HSC)/祖细胞群体增加。T细胞分析显示,AML中耗竭性CD8+T_LAG3和免疫抑制性CD4+T_FOXP3细胞比例较高,而CD4+T_GZMK细胞在HD样本中更为丰富。自然杀伤(NK)细胞表现出AML特异性亚型,尤其是NK_CD56dim_DNAJB1,其特征为终末耗竭和高应激评分。髓系分析显示AML中LAMP3+树突状细胞和巨噬细胞升高。值得注意的是,NK_CD56dim_DNAJB1与CD8+ T细胞之间通过TGFB1-TGFBR信号传导增强的相互作用,提示其在AML进展中具有关键的免疫调节机制。
BACKGROUND: Acute myeloid leukemia (AML) is a malignancy characterized by abnormal myeloid proliferation. Despite therapeutic advances, relapse is frequent. Understanding the tumor microenvironment (TME), particularly the dysfunction and interactions of natural killer (NK) cells, is essential for improving immunotherapeutic strategies such as chimeric antigen receptor T-cell (CAR-T) and chimeric antigen receptor natural killer cell (CAR-NK) therapies. OBJECTIVES: This study aimed to characterize the AML TME using single-cell RNA sequencing (scRNA-seq), focusing on NK cell dysfunction and their interactions with immunosuppressive cell populations. MATERIAL AND METHODS: We investigated single-cell RNA sequencing data from 59 samples, comprising 40 AML and 19 healthy donor (HD) samples, obtained from public sources and processed using standard methods. After quality control and doublet removal, 284,687 high-quality cells were retained. Acute myeloid leukemia malignant cells were identified using cluster-specific occupancy scores and inferred copy number variations. Batch effects were corrected with Harmony, followed by clustering and cell-type annotation using SingleR and established markers. We performed pathway activity profiling with gene set variation analysis (GSVA), explored intercellular communication using CellChat, and inferred cellular trajectories with Monocle3. Functional characteristics of NK cell subsets were evaluated using curated gene sets, and transcriptional regulation was assessed with pySCENIC. RESULTS: Single-cell RNA sequencing of 40 AML and 19 HD samples identified 76 cell clusters, including patient-specific malignant cells with elevated copy number variations (CNVs). Acute myeloid leukemia samples showed altered immune composition, with increased plasma cells and hematopoietic stem cell (HSC)/progenitor populations. T-cell analysis revealed higher proportions of exhausted CD8+T_LAG3 and immunosuppressive CD4+T_FOXP3 cells in AML, while CD4+T_GZMK cells were more abundant in HD samples. Natural killer (NK) cells exhibited AML-specific subtypes, notably NK_CD56dim_DNAJB1, characterized by terminal exhaustion and high stress scores. Myeloid analysis showed elevated LAMP3+ dendritic cells and macrophages in AML. Notably, enhanced interactions between NK_CD56dim_DNAJB1 and CD8+ T cells via TGFB1-TGFBR signaling suggest key immunoregulatory mechanisms in AML progression. CONCLUSIONS: This study highlights the cellular heterogeneity and complex immune interactions within the AML microenvironment, offering insights into immune evasion and informing future immunotherapeutic and targeted treatment strategies.
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