Evidence map›Paper›PMID 33163002›Full record

ReviewFrontiers in immunology2020

Characteristics of TCR Repertoire Associated With Successful Immune Checkpoint Therapy Responses.

Joel Kidman, Nicola Principe, Mark Watson, Timo Lassmann, Robert A Holt, Anna K Nowak, Willem Joost Lesterhuis, Richard A Lake, Jonathan Chee

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

64 citing papers in PubMed.

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  7. Preexisting TCR Clones Drive Major Pathologic Responses in Patients with HNSCC Treated with Dual Immune Checkpoint Inhibitors.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026
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4 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Joel KidmanNational Centre for Asbestos Related Diseases, Institute of Respiratory Health, University of Western Australia, Perth, WA, Australia.
Nicola PrincipeNational Centre for Asbestos Related Diseases, Institute of Respiratory Health, University of Western Australia, Perth, WA, Australia.
Mark WatsonInstitute for Immunology and Infectious Diseases, Murdoch University, Perth, WA, Australia.
Timo LassmannTelethon Kids Institute, Perth, WA, Australia.
Robert A HoltCanada's Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.
Anna K NowakNational Centre for Asbestos Related Diseases, Institute of Respiratory Health, University of Western Australia, Perth, WA, Australia.
Willem Joost LesterhuisNational Centre for Asbestos Related Diseases, Institute of Respiratory Health, University of Western Australia, Perth, WA, Australia.
Richard A LakeNational Centre for Asbestos Related Diseases, Institute of Respiratory Health, University of Western Australia, Perth, WA, Australia.
Jonathan CheeNational Centre for Asbestos Related Diseases, Institute of Respiratory Health, University of Western Australia, Perth, WA, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immunotherapies have revolutionized cancer treatment. In particular, immune checkpoint therapy (ICT) leads to durable responses in some patients with some cancers. However, the majority of treated patients do not respond. Understanding immune mechanisms that underlie responsiveness to ICT will help identify predictive biomarkers of response and develop treatments to convert non-responding patients to responding ones. ICT primarily acts at the level of adaptive immunity. The specificity of adaptive immune cells, such as T and B cells, is determined by antigen-specific receptors. T cell repertoires can be comprehensively profiled by high-throughput sequencing at the bulk and single-cell level. T cell receptor (TCR) sequencing allows for sensitive tracking of dynamic changes in antigen-specific T cells at the clonal level, giving unprecedented insight into the mechanisms by which ICT alters T cell responses. Here, we review how the repertoire influences response to ICT and conversely how ICT affects repertoire diversity. We will also explore how changes to the repertoire in different anatomical locations can better correlate and perhaps predict treatment outcome. We discuss the advantages and limitations of current metrics used to characterize and represent TCR repertoire diversity. Discovery of predictive biomarkers could lie in novel analysis approaches, such as network analysis of amino acids similarities between TCR sequences. Single-cell sequencing is a breakthrough technology that can link phenotype with specificity, identifying T cell clones that are crucial for successful ICT. The field of immuno-sequencing is rapidly developing and cross-disciplinary efforts are required to maximize the analysis, application, and validation of sequencing data. Unravelling the dynamic behavior of the TCR repertoire during ICT will be highly valuable for tracking and understanding anti-tumor immunity, biomarker discovery, and ultimately for the development of novel strategies to improve patient outcomes.

Indexed as

AnimalsHumansImmune Checkpoint InhibitorsImmunotherapyNeoplasmsReceptors, Antigen, T-CellImmune Checkpoint InhibitorsReceptors, Antigen, T-Cellcheckpoint immunotherapydynamic analysisimmunogenomicsT cell receptortumor immunology

Identifiers

PMID33163002
PMCPMC7591700

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.