Profiling Tumor Infiltrating Immune Cells with CIBERSORT

Binbin Chen; Michael S Khodadoust; Chih Long Liu; Aaron M Newman; Ash A Alizadeh

doi:10.1007/978-1-4939-7493-1_12

Profiling Tumor Infiltrating Immune Cells with CIBERSORT

Methods Mol Biol. 2018:1711:243-259. doi: 10.1007/978-1-4939-7493-1_12.

Authors

Binbin Chen¹, Michael S Khodadoust^{2

3

4}, Chih Long Liu², Aaron M Newman^{5

6}, Ash A Alizadeh^{7

8

9

10}

Affiliations

¹ Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
² Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, 875 Blake Wilbur Drive, Stanford, CA, 94305, USA.
³ Division of Hematology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
⁴ Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
⁵ Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, 875 Blake Wilbur Drive, Stanford, CA, 94305, USA. amnewman@stanford.edu.
⁶ Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. amnewman@stanford.edu.
⁷ Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, 875 Blake Wilbur Drive, Stanford, CA, 94305, USA. arasha@stanford.edu.
⁸ Division of Hematology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA. arasha@stanford.edu.
⁹ Stanford Cancer Institute, Stanford University, Stanford, CA, USA. arasha@stanford.edu.
¹⁰ Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. arasha@stanford.edu.

Abstract

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.

Keywords: Cancer immunology; Deconvolution; Gene expression; Microarray; RNA-Seq; Support vector regression (SVR); TCGA; Tumor heterogeneity; Tumor infiltrating leukocytes (TILs); Tumor microenvironment.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Gene Expression Profiling / methods*
Gene Expression Regulation, Neoplastic
Genomics / methods*
Humans
Lymphocytes, Tumor-Infiltrating / metabolism*
Lymphocytes, Tumor-Infiltrating / pathology
Neoplasms / genetics*
Neoplasms / pathology
Software*
Support Vector Machine
Transcriptome
Tumor Microenvironment*

Abstract

Publication types

MeSH terms

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