Profiling Tumor Infiltrating Immune Cells with CIBERSORT.
Profiling Tumor Infiltrating Immune Cells with CIBERSORT.
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DOI：
10.1007/978-1-4939-7493-1_12
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发表时间：
2018
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影响因子：
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通讯作者：
Alizadeh AA
中科院分区：
文献类型：
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作者：
Chen B;Khodadoust MS;Liu CL;Newman AM;Alizadeh AA
肿瘤浸润性白细胞（TIL）是肿瘤微环境的组成部分，并已被发现与预后和对治疗的反应相关。计数免疫亚群的方法如免疫组织化学或流式细胞术在表型标志物方面受到限制，并且在实际实施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高维数据，并随后通过计算推断细胞组分。我们最近描述了CIBERSORT，一种多功能的计算方法，用于从大量组织基因表达谱（GEPs）中定量细胞组分。将支持向量回归与来自纯化的白细胞亚群的表达谱的先验知识相结合，CIBERSORT可以准确地估计肿瘤活检的免疫组成。在这一章中，我们提供了一个引物的CIBERSORT方法，并说明其用于表征TILs的肿瘤样品的微阵列或RNA-Seq。
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.