Profiling tumor infiltrating immune cells with CIBERSORT

By Binbin Chen, M. Khodadoust, C. Liu, Aaron M. Newman, Ash A. Alizadeh. Methods in molecular biology, 2018

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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.

Subfields: Immunology, Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/eda918e79ed9ab974e6e262d2770cb91f145a2d8

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Citations in the corpus

From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. (2023, methodology): “Methods such as CIBERSORT use SVMs to deconvolve bulk RNA-seq data to estimate cell-type compositions.(77) CIBERSORTx and CODEFACS have expanded CIBERSORT to deconvolve bulk RNA using nu-support vector regression (n-SVR) analysis and achieve cell-type-specific gene expression without single-cell dat”