免疫系统
生物
流式细胞术
肿瘤微环境
基因表达谱
癌症研究
肿瘤浸润淋巴细胞
计算生物学
免疫疗法
免疫学
基因表达
基因
遗传学
作者
Binbin Chen,Michael S. Khodadoust,Chih Long Liu,Aaron M. Newman,Ash A. Alizadeh
标识
DOI:10.1007/978-1-4939-7493-1_12
摘要
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.
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