Evidence map›Paper›PMID 33537170›Full record

ArticleOncoimmunology2021

Peripheral T cell receptor repertoire features predict durable responses to anti-PD-1 inhibitor monotherapy in advanced renal cell carcinoma.

Taigo Kato, Kazuma Kiyotani, Eisuke Tomiyama, Yoko Koh, Makoto Matsushita, Yujiro Hayashi, Kosuke Nakano, Yu Ishizuya, Cong Wang, Koji Hatano and 5 more

Open access · goldAbstract read
In one paragraph

Article in Oncoimmunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
2.3field-weighted citation impact, top 9% of its field
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

32 citing papers in PubMed, 38 citations in OpenAlex.

  1. Trial
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  3. Towards liquid biopsy-based analysis of antitumour immunity.Nature reviews. Clinical oncology · 2026
    Review
  4. Article
  5. 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
    Article
  6. Review
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  8. Review
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  10. Article
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  13. Machine learning in AIRR diagnostics: Advances and applications.Immunoinformatics (Amsterdam, Netherlands) · 2025
    Article
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  15. Article
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  17. Review
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  19. Article
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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

15 authors at 2 institutions in 1 country.

Taigo KatoDepartment of Urology, Osaka University Graduate School of Medicine, Osaka, Japan.
Kazuma KiyotaniCancer Precision Medicine Center, Japanese Foundation for Cancer Research, Tokyo, Japan.
Eisuke TomiyamaDepartment of Urology, Osaka University Graduate School of Medicine, Osaka, Japan.
Yoko KohDepartment of Urology, Osaka University Graduate School of Medicine, Osaka, Japan.
Makoto MatsushitaDepartment of Urology, Osaka University Graduate School of Medicine, Osaka, Japan.
Yujiro HayashiDepartment of Urology, Osaka University Graduate School of Medicine, Osaka, Japan.
Kosuke NakanoDepartment of Urology, Osaka University Graduate School of Medicine, Osaka, Japan.
Yu IshizuyaDepartment of Urology, Osaka University Graduate School of Medicine, Osaka, Japan.
Cong WangDepartment of Urology, Osaka University Graduate School of Medicine, Osaka, Japan.
Koji HatanoDepartment of Urology, Osaka University Graduate School of Medicine, Osaka, Japan.
Atsunari KawashimaDepartment of Urology, Osaka University Graduate School of Medicine, Osaka, Japan.
Takeshi UjikeDepartment of Urology, Osaka University Graduate School of Medicine, Osaka, Japan.
Kazutoshi FujitaDepartment of Urology, Osaka University Graduate School of Medicine, Osaka, Japan.
Norio NonomuraDepartment of Urology, Osaka University Graduate School of Medicine, Osaka, Japan.
Motohide UemuraDepartment of Urology, Osaka University Graduate School of Medicine, Osaka, Japan.
Osaka University · JPJapanese Foundation For Cancer Research · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immune checkpoint inhibitors (ICIs) offer significant clinical benefits to a subset of cancer patients via the induction of a systemic T cell-mediated anti-cancer immune response. Thus, the dynamic characterization of T cell repertoires in the peripheral blood has the potential to demonstrate noninvasive predictive biomarkers for the clinical efficacy of ICIs. In this study, we collected tumor tissues and peripheral blood samples from 25 patients with advanced kidney cancer before anti-programmed cell death protein 1 (PD-1) treatment and 1, 3, and 6 months after treatment initiation. Furthermore, we applied a next-generation sequencing approach to characterize T cell receptor (TCR) alpha and beta repertoires. TCR repertoire analysis revealed that the responders to anti-PD-1 showed an expansion of certain T cell clones even in the blood, as evidenced by the significant decrease in the TCR diversity index and increase in the number of expanded TCR clonotypes 1 month after treatment. Interestingly, these expanded TCR clonotypes in the peripheral blood were significantly shared with tumor-infiltrating T cells in responders, indicating that they have many circulating T cells that may recognize cancer antigens. Expression analysis also revealed that 1 month after treatment, T cells from the peripheral blood of responders showed significantly elevated transcriptional levels of

Indexed as

Carcinoma, Renal CellKidney NeoplasmsHumansImmune Checkpoint InhibitorsReceptors, Antigen, T-CellReceptors, Antigen, T-Cell, alpha-betaImmune Checkpoint InhibitorsReceptors, Antigen, T-CellReceptors, Antigen, T-Cell, alpha-betaImmune checkpoint inhibitorsnext-generation sequencingpredictive markerrenal cell carcinomaT cell receptor

Identifiers

PMID33537170
PMCPMC7833759
OpenAlexW3125723584

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.