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Bioinformatics for Cancer Immunotherapy

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Bioinformatics for Cancer Immunotherapy

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

Abstract

Our immune system plays a key role in health and disease as it is capable of responding to foreign antigens as well as acquired antigens from cancer cells. Latter are caused by somatic mutations, the so-called neoepitopes, and might be recognized by T cells if they are presented by HLA molecules on the surface of cancer cells. Personalized mutanome vaccines are a class of customized immunotherapies, which is dependent on the detection of individual cancer-specific tumor mutations and neoepitope (i.e., prediction, followed by a rational vaccine design, before on-demand production. The development of next generation sequencing (NGS) technologies and bioinformatic tools allows a large-scale analysis of each parameter involved in this process. Here, we provide an overview of the bioinformatic aspects involved in the design of personalized, neoantigen-based vaccines, including the detection of mutations and the subsequent prediction of potential epitopes, as well as methods for associated biomarker research, such as high-throughput sequencing of T-cell receptors (TCRs), followed by data analysis and the bioinformatics quantification of immune cell infiltration in cancer samples.

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Authors and Affiliations

  1. TRON—Translationale Onkologie an der Universitätsmedizin der Johannes Gutenberg-Universität Mainz gemeinnützige GmbH, Freiligrathstraße, Mainz, Germany

    Christoph Holtsträter, Barbara Schrörs, Thomas Bukur & Martin Löwer

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  1. Christoph Holtsträter
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  2. Barbara Schrörs
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  3. Thomas Bukur
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  4. Martin Löwer
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Correspondence to Martin Löwer .

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  1. Mainz, Germany

    Sebastian Boegel

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Holtsträter, C., Schrörs, B., Bukur, T., Löwer, M. (2020). Bioinformatics for 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_1

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  • DOI: https://doi.org/10.1007/978-1-0716-0327-7_1

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