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In Silico Cell-Type Deconvolution Methods in Cancer Immunotherapy

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Bioinformatics for Cancer Immunotherapy

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2120))

Abstract

Several computational methods have been proposed to infer the cellular composition from bulk RNA-seq data of a tumor biopsy sample. Elucidating interactions in the tumor microenvironment can yield unique insights into the status of the immune system. In immuno-oncology, this information can be crucial for deciding whether the immune system of a patient can be stimulated to target the tumor. Here, we shed a light on the working principles, capabilities, and limitations of the most commonly used methods for cell-type deconvolution in immuno-oncology and offer guidelines for method selection.

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Author information

Authors and Affiliations

  1. Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria

    Gregor Sturm & Francesca Finotello

  2. Big Data in BioMedicine Group, TUM School of Life Sciences, Technical University of Munich, Freising, Germany

    Markus List

Authors
  1. Gregor Sturm
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  2. Francesca Finotello
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  3. Markus List
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Corresponding author

Correspondence to Markus List .

Editor information

Editors and Affiliations

  1. Mainz, Germany

    Sebastian Boegel

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Sturm, G., Finotello, F., List, M. (2020). In Silico Cell-Type Deconvolution Methods in Cancer Immunotherapy. In: Boegel, S. (eds) Bioinformatics for Cancer Immunotherapy. Methods in Molecular Biology, vol 2120. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0327-7_15

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  • DOI: https://doi.org/10.1007/978-1-0716-0327-7_15

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0326-0

  • Online ISBN: 978-1-0716-0327-7

  • eBook Packages: Springer Protocols

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