使用CIBERSORT分析肿瘤浸润免疫细胞
Profiling Tumor Infiltrating Immune Cells with CIBERSORT.
作者信息
Chen Binbin, Khodadoust Michael S, Liu Chih Long, Newman Aaron M, Alizadeh Ash A
机构信息
Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, 875 Blake Wilbur Drive, Stanford, CA, 94305, USA.
出版信息
Methods Mol Biol. 2018;1711:243-259. doi: 10.1007/978-1-4939-7493-1_12.
Tumor infiltrating leukocytes (TILs) are an integral component of the tumor microenvironment and have been found to correlate with prognosis and response to therapy. Methods to enumerate immune subsets such as immunohistochemistry or flow cytometry suffer from limitations in phenotypic markers and can be challenging to practically implement and standardize. An alternative approach is to acquire aggregative high dimensional data from cellular mixtures and to subsequently infer the cellular components computationally. We recently described CIBERSORT, a versatile computational method for quantifying cell fractions from bulk tissue gene expression profiles (GEPs). Combining support vector regression with prior knowledge of expression profiles from purified leukocyte subsets, CIBERSORT can accurately estimate the immune composition of a tumor biopsy. In this chapter, we provide a primer on the CIBERSORT method and illustrate its use for characterizing TILs in tumor samples profiled by microarray or RNA-Seq.
肿瘤浸润白细胞（TILs）是肿瘤微环境的一个重要组成部分，并且已发现其与预后及治疗反应相关。诸如免疫组织化学或流式细胞术等用于计数免疫亚群的方法在表型标记方面存在局限性，并且在实际应用和标准化方面可能具有挑战性。另一种方法是从细胞混合物中获取聚合的高维数据，然后通过计算推断细胞成分。我们最近描述了CIBERSORT，这是一种用于从批量组织基因表达谱（GEP）中定量细胞分数的通用计算方法。将支持向量回归与来自纯化白细胞亚群的表达谱的先验知识相结合，CIBERSORT可以准确估计肿瘤活检的免疫组成。在本章中，我们提供了CIBERSORT方法的入门介绍，并说明其用于表征通过微阵列或RNA测序分析的肿瘤样本中的TILs的用途。
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