Xiao Xu, Guo Qian, Cui Chuanliang, Lin Yating, Zhang Lei, Ding Xin, Li Qiyuan, Wang Minshu, Yang Wenxian, Kong Yan, Yu Rongshan
School of Informatics, Xiamen University, Xiamen, China.
National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
Commun Med (Lond). 2022 Oct 21;2:131. doi: 10.1038/s43856-022-00197-2. eCollection 2022.
BACKGROUND: Single-cell technologies have enabled extensive analysis of complex immune composition, phenotype and interactions within tumor, which is crucial in understanding the mechanisms behind cancer progression and treatment resistance. Unfortunately, knowledge on cell phenotypes and their spatial interactions has only had limited impact on the pathological stratification of patients in the clinic so far. We explore the relationship between different tumor environments (TMEs) and response to immunotherapy by deciphering the composition and spatial relationships of different cell types. METHODS: Here we used imaging mass cytometry to simultaneously quantify 35 proteins in a spatially resolved manner on tumor tissues from 26 melanoma patients receiving anti-programmed cell death-1 (anti-PD-1) therapy. Using unsupervised clustering, we profiled 662,266 single cells to identify lymphocytes, myeloid derived monocytes, stromal and tumor cells, and characterized TME of different melanomas. RESULTS: Combined single-cell and spatial analysis reveals highly dynamic TMEs that are characterized with variable tumor and immune cell phenotypes and their spatial organizations in melanomas, and many of these multicellular features are associated with response to anti-PD-1 therapy. We further identify six distinct TME archetypes based on their multicellular compositions, and find that patients with different TME archetypes responded differently to anti-PD-1 therapy. Finally, we find that classifying patients based on the gene expression signature derived from TME archetypes predicts anti-PD-1 therapy response across multiple validation cohorts. CONCLUSIONS: Our results demonstrate the utility of multiplex proteomic imaging technologies in studying complex molecular events in a spatially resolved manner for the development of new strategies for patient stratification and treatment outcome prediction.
背景:单细胞技术能够对肿瘤内复杂的免疫组成、表型及相互作用进行广泛分析,这对于理解癌症进展和治疗耐药背后的机制至关重要。遗憾的是,迄今为止,关于细胞表型及其空间相互作用的知识对临床患者的病理分层影响有限。我们通过解析不同细胞类型的组成和空间关系,探索不同肿瘤微环境(TME)与免疫治疗反应之间的关系。 方法:在此,我们使用成像质谱流式细胞术以空间分辨的方式同时定量分析了26例接受抗程序性细胞死亡蛋白1(抗PD-1)治疗的黑色素瘤患者肿瘤组织中的35种蛋白质。通过无监督聚类,我们对662,266个单细胞进行了分析,以识别淋巴细胞、髓系来源的单核细胞、基质细胞和肿瘤细胞,并对不同黑色素瘤的肿瘤微环境进行了表征。 结果:单细胞和空间分析相结合揭示了高度动态的肿瘤微环境,其特征是黑色素瘤中肿瘤和免疫细胞表型及其空间组织各异,其中许多多细胞特征与抗PD-1治疗反应相关。我们进一步根据其多细胞组成确定了六种不同的肿瘤微环境原型,并发现具有不同肿瘤微环境原型的患者对抗PD-1治疗的反应不同。最后,我们发现基于从肿瘤微环境原型衍生的基因表达特征对患者进行分类可预测多个验证队列中的抗PD-1治疗反应。 结论:我们的结果证明了多重蛋白质组学成像技术在以空间分辨方式研究复杂分子事件以制定患者分层和治疗结果预测新策略方面的实用性。
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