Evidence map›Paper›PMID 38317746›Full record

ArticleTranslational gastroenterology and hepatology2024

Clinical significance of peripheral T-cell receptor repertoire profiling and individualized nomograms in patients with gastrointestinal cancer treated with anti-programmed death 1 antibody.

Jing Wu, Yiyi Yu, Shilong Zhang, Pengfei Zhang, Shan Yu, Wei Li, Yan Wang, Qian Li, Binbin Lu, Limeng Chen and 4 more

Abstract read
In one paragraph

Article in Translational gastroenterology and hepatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

14 authors.

Jing Wu *Department of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China.
Yiyi Yu *Department of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China.
Shilong Zhang *Department of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China.
Pengfei ZhangDepartment of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China.
Shan YuDepartment of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China.
Wei LiDepartment of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China.
Yan WangDepartment of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China.
Qian LiDepartment of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China.
Binbin LuShanghai Dunwill Medical Technology Co., Ltd., Shanghai, China.
Limeng ChenShanghai Dunwill Medical Technology Co., Ltd., Shanghai, China.
Chonglin LuoShanghai Dunwill Medical Technology Co., Ltd., Shanghai, China.
Haixiang PengShanghai Dunwill Medical Technology Co., Ltd., Shanghai, China.
Tianshu LiuDepartment of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China.
Yuehong CuiDepartment of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Immune checkpoint inhibitors (ICIs) have significant clinical benefit for a subset of patients with gastrointestinal cancers (GICs) including esophageal cancer, gastric cancer and colorectal cancer. However, it is difficult to predict which patients will respond favorably to immune checkpoint blockade therapy. Thus, this study was initiated to determine if peripheral T-cell receptor (TCR) repertoire profiling could predict the clinical efficacy of anti-programmed death 1 (PD-1) treatment. Methods: Blood samples from 31 patients with GICs were collected before anti-PD-1 antibody treatment initiation. The clinical significance of the combinatorial diversity evenness of the TCR repertoire [the diversity evenness 50 (DE50), with high values corresponding to less clonality and higher TCR diversity] from peripheral blood mononuclear cells (PBMCs) was evaluated in all the enrolled patients. A highly predictive nomogram was set up based on peripheral TCR repertoire profiling. The performance of the nomogram was assessed by receiver operating characteristic (ROC) curve, concordance index (C-index), and calibration curves, and decision curve analysis (DCA) was used to assess its clinical applicability. Results: Compared to non-responders [progression disease (PD)], the DE50 scores were significantly higher in responders [stable disease (SD) and partial response (PR)] (P=0.018). Patients with a high DE50 score showed better progression-free survival (PFS) than those with a low DE50 score (P=0.0022). The multivariable Cox regression demonstrated that high DE50 and low platelet-lymphocyte ratio (PLR) were significant independent predictors for better PFS when treated with anti-PD-1 antibody. Furthermore, a highly predictive nomogram was set up based on peripheral TCR repertoire profiling. The area under the curves (AUCs) of this system at 3-, 6- and 12-month PFS reached 0.825, 0.802, and 0.954, respectively. The nomogram had a C-index of 0.768 [95% confidence interval (CI): 0.658-0.879]. Meanwhile, the calibration curves also demonstrated the reliability and stability of the model. Conclusions: High DE50 scores were predictive of a favorable response and longer PFS to anti-PD-1 treatment in GIC patients. The nomogram based on TCR repertoire profiling was a reliable and practical tool, which could provide risk assessment and clinical decision-making for individualized treatment of patients.

Indexed as

anti-programmed death 1 treatment (anti-PD-1 treatment)diversity evenness 50 (DE50)gastrointestinal cancers (GICs)T-cell receptor repertoire (TCR repertoire)

Identifiers

PMID38317746
PMCPMC10838612

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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.