Evidence map›Paper›PMID 40744664›Full record

ArticleJournal for immunotherapy of cancer2025

Integrated system for screening tumor-specific TCRs, epitopes, and HLA subtypes using single-cell sequencing data.

Xianyao Wang, Xuelian Song, Yi Li, Yanshu Ding, Chenfei Yin, Tianyuan Ren, Weiguo Zhang

Abstract read
In one paragraph

Article in Journal for immunotherapy of cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
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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

7 authors.

Xianyao Wang *National Key Laboratory of Immunity and Inflammation, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou, Jiangsu, China.ORCID http://orcid.org/0000-0002-1742-2045
Xuelian Song *National Key Laboratory of Immunity and Inflammation, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou, Jiangsu, China.
Yi Li *National Key Laboratory of Immunity and Inflammation, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou, Jiangsu, China.
Yanshu DingNational Key Laboratory of Immunity and Inflammation, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou, Jiangsu, China.
Chenfei YinNational Key Laboratory of Immunity and Inflammation, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou, Jiangsu, China.
Tianyuan RenNational Key Laboratory of Immunity and Inflammation, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou, Jiangsu, China.
Weiguo ZhangNational Key Laboratory of Immunity and Inflammation, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou, Jiangsu, China zhangwg@ism.pumc.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundT-cell receptor (TCR)-T immunotherapy has emerged as a promising strategy for cancer treatment. However, identifying TCRs that can be used to generate TCR-T cells remains challenging due to tumor heterogeneity, the scarcity of tumor-specific T cells, and the diversity of human leukocyte antigens (HLA). To advance TCR-T immunotherapy, it is crucial to develop an efficient and scalable method to identify tumor-specific TCRs.

methodsTo identify tumor-specific TCRs, epitopes, and their corresponding HLA subtypes, we developed a method for rapidly assembling TCRs identified through the single-cell analysis of T cells from various tumors. For each TCR, only two pairs of oligonucleotides corresponding to the CDR3 regions of TCR-α and β chains needed to be synthesized, enabling the construction of a TCR library quickly in a cost-effective manner. Additionally, we engineered HLA-knockout HEK-293T cells as antigen-presenting cells to express patient-specific HLA-I and neoantigens, and a Jurkat NFAT-GFP reporter cell line for screening antigen-reactive TCRs. The efficacy of our TCR-screening system was validated through a small-scale screening of HPV16-specific TCRs from patients with cervical cancer.

resultsWe successfully developed a TCR assembly method that enables the rapid cloning and construction of TCR libraries within 2 days, significantly accelerating the process and reducing costs. Our antigen-presenting system also allows for flexible expression of patient-specific HLA-I molecules, facilitating personalized screening. The Jurkat reporter cells demonstrated high sensitivity for screening functional TCRs. Using published datasets from patients with HPV16-positive cervical cancer, we successfully used our system to isolate a human papillomavirus (HPV)-specific TCR. Through deletion, alanine scanning, and mass spectrometry analysis, we determined that this TCR specifically recognized an 8-mer peptide (MHGDTPTL) from HPV-E7 presented by HLA-B*15:18. Moreover, T cells expressing this TCR were able to effectively kill HPV-positive cells.

conclusionsWe developed an integrated antigen-presenting, TCR assembly, and TCR reporter system for screening tumor-specific TCRs using single-cell sequencing datasets. By using this system, we have successfully identified a functional, HPV-specific TCR, demonstrating the potential of our approach for the efficient screening of tumor-specific TCRs to advance TCR-T immunotherapy.

Indexed as

HLA AntigensReceptors, Antigen, T-CellSingle-Cell AnalysisHEK293 CellsHumansHLA AntigensReceptors, Antigen, T-CellAdoptive cell therapy - ACTCervical CancerHuman leukocyte antigen - HLAT cell Receptor - TCR

Identifiers

PMID40744664
PMCPMC12314965

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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.