Wistuba-Hamprecht Kilian, Martens Alexander, Weide Benjamin, Teng Karen Wei Weng, Zelba Henning, Guffart Elena, Chen Jinmiao, Garbe Claus, Newell Evan William, Larbi Anis, Pawelec Graham
Department of Internal Medicine II, University Medical Center, 72072 Tübingen, Germany.
Department of Dermatology, University Medical Center, 72076 Tübingen, Germany.
J Immunol. 2017 Jan 15;198(2):927-936. doi: 10.4049/jimmunol.1600875. Epub 2016 Dec 16.
The identification of blood-borne biomarkers correlating with melanoma patient survival remains elusive. Novel techniques such as mass cytometry could help to identify melanoma biomarkers, allowing simultaneous detection of up to 100 parameters. However, the evaluation of multiparametric data generated via time-of-flight mass cytometry requires novel analytical techniques because the application of conventional gating strategies currently used in polychromatic flow cytometry is not feasible. In this study, we have employed 38-channel time-of-flight mass cytometry analysis to generate comprehensive immune cell signatures using matrix boolean analysis in a cohort of 28 stage IV melanoma patients and 17 controls. Clusters of parameters were constructed from the abundance of cellular phenotypes significantly different between patients and controls. This approach identified patient-specific combinatorial immune signatures consisting of high-resolution subsets of the T cell, NK cell, B cell, and myeloid compartments. An association with superior survival was characterized by a balanced distribution of myeloid-derived suppressor cell-like and APC-like myeloid phenotypes and differentiated NK cells. The results of this study in a discovery cohort of melanoma patients suggest that multifactorial immune signatures have the potential to allow more accurate prediction of individual patient outcome. Further investigation of the identified immune signatures in a validation cohort is now warranted.
与黑色素瘤患者生存率相关的血源生物标志物仍难以确定。诸如质谱流式细胞术等新技术有助于识别黑色素瘤生物标志物，可同时检测多达100个参数。然而，对通过飞行时间质谱流式细胞术生成的多参数数据进行评估需要新颖的分析技术，因为目前在多色流式细胞术中使用的传统设门策略在此不可行。在本研究中，我们采用38通道飞行时间质谱流式细胞术分析，通过矩阵布尔分析在28例IV期黑色素瘤患者和17名对照组成的队列中生成全面的免疫细胞特征。根据患者和对照之间显著不同的细胞表型丰度构建参数簇。这种方法识别出了患者特异性的组合免疫特征，其由T细胞、NK细胞、B细胞和髓系区室的高分辨率亚群组成。髓系来源的抑制细胞样和APC样髓系表型以及分化的NK细胞的平衡分布是与更好生存率相关的特征。这项在黑色素瘤患者发现队列中的研究结果表明，多因素免疫特征有可能更准确地预测个体患者的预后。现在有必要在验证队列中对所识别的免疫特征进行进一步研究。
