Computational deconvolution of tumor-infiltrating immune components with bulk tumor gene expression data

Bo Li, Taiwen Li, Jun S. Liu, X. Shirley Liu

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

Tumor-infiltrating immune cells play critical roles in immune-mediated tumor rejection and/or progression, and are key targets of immunotherapies. Estimation of different immune subsets becomes increasingly important with the decreased cost of high-throughput molecular profiling and the rapidly growing amount of cancer genomics data. Here, we present Tumor IMmune Estimation Resource (TIMER), an in silico deconvolution method for inference of tumor-infiltrating immune components. TIMER takes bulk tissue gene expression profiles measured with RNA-seq or microarray to evaluate the abundance of six immune cell types in the tumor microenvironment: B cell, CD4+ T cell, CD8+ T cell, neutrophil, macrophage, and dendritic cell. We further introduce its associated webserver for convenient, user-friendly analysis of tumor immune infiltrates across multiple cancer types.

Original languageEnglish (US)
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Pages249-262
Number of pages14
DOIs
StatePublished - Jan 1 2020

Publication series

NameMethods in Molecular Biology
Volume2120
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

Keywords

  • Cancer immunotherapy
  • Deconvolution
  • Infiltrating immune cells
  • Interactive website
  • RNA-seq
  • Tumor immune interaction

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

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  • Cite this

    Li, B., Li, T., Liu, J. S., & Liu, X. S. (2020). Computational deconvolution of tumor-infiltrating immune components with bulk tumor gene expression data. In Methods in Molecular Biology (pp. 249-262). (Methods in Molecular Biology; Vol. 2120). Humana Press Inc.. https://doi.org/10.1007/978-1-0716-0327-7_18