Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma.
Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma.
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多路成像细胞术揭示了与黑色素瘤免疫治疗反应相关的不同肿瘤免疫微环境。
DOI：
10.1038/s43856-022-00197-2
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Yu, Rongshan
中科院分区：
文献类型：
--
作者：
Xiao, Xu;Guo, Qian;Cui, Chuanliang;Lin, Yating;Zhang, Lei;Ding, Xin;Li, Qiyuan;Wang, Minshu;Yang, Wenxian;Kong, Yan;Yu, Rongshan
单细胞技术已经能够广泛分析肿瘤内复杂的免疫组成、表型和相互作用，这对于理解癌症进展和治疗耐药性背后的机制至关重要。不幸的是，细胞表型及其空间相互作用的知识，只有有限的影响，在临床上的患者的病理分层。我们通过解读不同细胞类型的组成和空间关系来探索不同肿瘤环境（TME）与免疫治疗反应之间的关系。在这里，我们使用成像质谱细胞术以空间分辨的方式同时定量来自26名接受抗程序性细胞死亡-1（抗PD-1）治疗的黑色素瘤患者的肿瘤组织中的35种蛋白质。使用无监督聚类，我们分析了662，266个单细胞，以识别淋巴细胞，髓源性单核细胞，基质细胞和肿瘤细胞，并表征了不同黑色素瘤的TME。组合的单细胞和空间分析揭示了高度动态的TME，其特征在于黑色素瘤中的可变肿瘤和免疫细胞表型及其空间组织，并且这些多细胞特征中的许多与对抗PD-1治疗的应答相关。我们进一步根据其多细胞组成确定了六种不同的TME原型，并发现具有不同TME原型的患者对抗PD-1治疗的反应不同。最后，我们发现基于来自TME原型的基因表达特征对患者进行分类可以预测多个验证队列中的抗PD-1治疗应答。我们的研究结果表明，多重蛋白质组成像技术在研究复杂的分子事件在空间分辨的方式为患者分层和治疗结果预测的新策略的发展的效用。免疫疗法帮助免疫系统对抗癌症。然而，它们仅使黑色素瘤患者的一个子集受益，并且目前没有单一标记物足以确定哪些患者将对这些治疗作出反应。在这里，我们使用成像质量细胞术，一种测量单个细胞中多种标志物水平的技术，来分析接受免疫治疗的黑色素瘤患者的肿瘤组织。通过确定存在的不同细胞类型以及它们之间的空间关系，我们确定了六种不同的黑色素瘤细胞环境，这些细胞环境与免疫治疗的不同临床反应相关。我们的研究结果证明了如何整合有关细胞类型空间关系的复杂信息，以帮助识别可能从免疫治疗中受益的患者。Xiao，Guo等人使用成像质谱细胞术评价黑色素瘤中肿瘤微环境的空间组成。作者确定了与抗PD-1免疫治疗反应相关的微环境特征。
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. 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. 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. 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. Immunotherapies help the immune system to fight cancer. However, they only benefit a subset of melanoma patients, and currently no single marker is sufficient to determine which patients will respond to these treatments. Here, we use imaging mass cytometry, a technique to measure the levels of multiple markers in individual cells, to analyze tumor tissue from melanoma patients receiving immunotherapy. By determining the different cell types present and the spatial relationships between them, we identify six distinct melanoma cellular environments that are associated with different clinical responses to immunotherapy. Our results demonstrate how complex information about the spatial relationships of cell types can be integrated to help to identify patients that might benefit from immunotherapy. Xiao, Guo et al. use imaging mass cytometry to evaluate the spatial composition of the tumor microenvironment in melanoma. The authors identify features of the microenvironment associated with response to anti-PD-1 immunotherapy.
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DOI：
10.1056/nejmoa1003466
发表时间：
2010-08-19
期刊：
The New England journal of medicine
影响因子：
--
作者：
Hodi FS;O'Day SJ;McDermott DF;Weber RW;Sosman JA;Haanen JB;Gonzalez R;Robert C;Schadendorf D;Hassel JC;Akerley W;van den Eertwegh AJ;Lutzky J;Lorigan P;Vaubel JM;Linette GP;Hogg D;Ottensmeier CH;Lebbé C;Peschel C;Quirt I;Clark JI;Wolchok JD;Weber JS;Tian J;Yellin MJ;Nichol GM;Hoos A;Urba WJ
通讯作者：
Urba WJ
影响因子：
64.8
作者：
Helmink BA;Reddy SM;Gao J;Zhang S;Basar R;Thakur R;Yizhak K;Sade-Feldman M;Blando J;Han G;Gopalakrishnan V;Xi Y;Zhao H;Amaria RN;Tawbi HA;Cogdill AP;Liu W;LeBleu VS;Kugeratski FG;Patel S;Davies MA;Hwu P;Lee JE;Gershenwald JE;Lucci A;Arora R;Woodman S;Keung EZ;Gaudreau PO;Reuben A;Spencer CN;Burton EM;Haydu LE;Lazar AJ;Zapassodi R;Hudgens CW;Ledesma DA;Ong S;Bailey M;Warren S;Rao D;Krijgsman O;Rozeman EA;Peeper D;Blank CU;Schumacher TN;Butterfield LH;Zelazowska MA;McBride KM;Kalluri R;Allison J;Petitprez F;Fridman WH;Sautès-Fridman C;Hacohen N;Rezvani K;Sharma P;Tetzlaff MT;Wang L;Wargo JA
通讯作者：
Wargo JA
影响因子：
22.7
作者：
Ali, H. Raza;Jackson, Hartland W.;Bodenmiller, Bernd
通讯作者：
Bodenmiller, Bernd
影响因子：
22.7
作者：
Han J;Zhao Y;Shirai K;Molodtsov A;Kolling FW;Fisher JL;Zhang P;Yan S;Searles TG;Bader JM;Gui J;Cheng C;Ernstoff MS;Turk MJ;Angeles CV
通讯作者：
Angeles CV
影响因子：
64.8
作者：
Cabrita, Rita;Lauss, Martin;Jonsson, Goran
通讯作者：
Jonsson, Goran