McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins School of Medicine, 550 N Broadway, Suite 1101E, Baltimore, MD 21205, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 1650 Orleans Street, Room 485, Baltimore, MD 21287, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Immunotherapy Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 1650 Orleans Street, Room 485, Baltimore, MD 21287, USA; Convergence Institute, Johns Hopkins University, Baltimore, MD, USA; Bloomberg-Kimmel Immunotherapy Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Cancer Cell. 2021 Aug 9;39(8):1062-1080. doi: 10.1016/j.ccell.2021.07.004. Epub 2021 Jul 29.
Single-cell technologies are emerging as powerful tools for cancer research. These technologies characterize the molecular state of each cell within a tumor, enabling new exploration of tumor heterogeneity, microenvironment cell-type composition, and cell state transitions that affect therapeutic response, particularly in the context of immunotherapy. Analyzing clinical samples has great promise for precision medicine but is technically challenging. Successfully identifying predictors of response requires well-coordinated, multi-disciplinary teams to ensure adequate sample processing for high-quality data generation and computational analysis for data interpretation. Here, we review current approaches to sample processing and computational analysis regarding their application to translational cancer immunotherapy research.
单细胞技术正在成为癌症研究的有力工具。这些技术可以描述肿瘤中每个细胞的分子状态，从而可以更深入地研究肿瘤异质性、微环境细胞类型组成以及影响治疗反应的细胞状态转变，尤其是在免疫治疗的背景下。分析临床样本在精准医疗方面具有很大的潜力，但在技术上具有挑战性。成功确定治疗反应的预测因子需要协调良好的多学科团队，以确保对高质量数据生成和数据解释的计算分析进行充分的样本处理。在这里，我们回顾了目前在转化癌症免疫治疗研究中应用的样本处理和计算分析方法。
