T cell receptor sequence clustering and antigen specificity

Milena Vujovic*, Kristine Fredlund Degn, Frederikke Isa Marin*, Anna-Lisa Schaap-Johansen*, Benny Chain*, Thomas Lars Andresen, Joseph Kaplinsky*, Paolo Marcatili*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

There has been increasing interest in the role of T cells and their involvement in cancer, autoimmune and infectious diseases. However, the nature of T cell receptor (TCR) epitope recognition at a repertoire level is not yet fully understood. Due to technological advances a plethora of TCR sequences from a variety of disease and treatment settings has become readily available. Current efforts in TCR specificity analysis focus on identifying characteristics in immune repertoires which can explain or predict disease outcome or progression, or can be used to monitor the efficacy of disease therapy. In this context, clustering of TCRs by sequence to reflect biological similarity, and especially to reflect antigen specificity have become of paramount importance. We review the main TCR sequence clustering methods and the different similarity measures they use, and discuss their performance and possible improvement. We aim to provide guidance for non-specialists who wish to use TCR repertoire sequencing for disease tracking, patient stratification or therapy prediction, and to provide a starting point for those aiming to develop novel techniques for TCR annotation through clustering.
Original languageEnglish
JournalComputational and Structural Biotechnology Journal
Volume18
Pages (from-to)2166-2173
ISSN2001-0370
DOIs
Publication statusPublished - 2020

Keywords

  • T cell receptor (TCR)
  • Clustering
  • Epitope specificity
  • T cell receptor distance
  • T cell receptor similarity
  • T cell repertoire

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