Evidence map›Paper›PMID 38443094›Full record

ArticleJournal for immunotherapy of cancer2024

Dynamic monitoring of circulating tumor DNA reveals outcomes and genomic alterations in patients with relapsed or refractory large B-cell lymphoma undergoing CAR T-cell therapy.

Hesong Zou, Wei Liu, Xiaojuan Wang, Yi Wang, Chunyang Wang, Chen Qiu, Huimin Liu, Dandan Shan, Ting Xie, Wenyang Huang and 8 more

Open access · goldAbstract read
In one paragraph

Article in Journal for immunotherapy of cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
4.2field-weighted citation impact, top 5% 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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Review
  4. Decoding IGLL5 Mutation-Mediated BCR Signaling: A Novel Mechanism of CD8Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  5. Review
  6. Review
  7. Review
  8. Review
  9. Review
  10. CAR T-cell therapy induced pseudoprogression.Frontiers in immunology · 2026
    Review
  11. Review
  12. Review
  13. Review
  14. Article
  15. Liquid Biopsy in B and T Cell Lymphomas: From Bench to Bedside.International journal of molecular sciences · 2025
    Review
  16. Review
  17. Review
  18. [Progression and application of circulating tumor DNA in lymphoma].Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi · 2024
    Review
  19. Review
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

18 authors at 2 institutions in 1 country.

Hesong Zou *State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Wei Liu *State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.ORCID 0000-0002-0448-7210
Xiaojuan Wang *Jichen Biotechnology Co, Ltd, Hangzhou, Zhejiang, China.
Yi WangState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Chunyang WangJichen Biotechnology Co, Ltd, Hangzhou, Zhejiang, China.
Chen QiuState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Huimin LiuState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Dandan ShanState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Ting XieState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Wenyang HuangState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Weiwei SuiState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Shuhua YiState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Gang AnState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Yan XuState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Tonghui MaJichen Biotechnology Co, Ltd, Hangzhou, Zhejiang, China.
Jianxiang WangState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China zoudehui@ihcams.ac.cn qiulg@ihcams.ac.cn wangjx@ihcams.ac.cn.ORCID 0000-0001-9437-9151
Lugui QiuState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China zoudehui@ihcams.ac.cn qiulg@ihcams.ac.cn wangjx@ihcams.ac.cn.
Dehui ZouState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China zoudehui@ihcams.ac.cn qiulg@ihcams.ac.cn wangjx@ihcams.ac.cn.ORCID 0000-0002-8135-4913
Chinese Academy of Medical Sciences & Peking Union Medical College · CNHangzhou DAC Biotech (China) · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOver 50% of patients with relapsed or refractory large B-cell lymphoma (r/r LBCL) receiving CD19-targeted chimeric antigen receptor (CAR19) T-cell therapy fail to achieve durable remission. Early identification of relapse or progression remains a significant challenge. In this study, we prospectively investigate the prognostic value of dynamic circulating tumor DNA (ctDNA) and track genetic evolution non-invasively, for the first time in an Asian population of r/r patients undergoing CAR19 T-cell therapy.

methodsLongitudinal plasma samples were prospectively collected both before lymphodepletion and at multiple timepoints after CAR19 T-cell infusion. ctDNA was detected using a capture-based next-generation sequencing which has been validated in untreated LBCL.

resultsThe study enrolled 23 patients with r/r LBCL and collected a total of 101 ctDNA samples. Higher pretreatment ctDNA levels were associated with inferior progression-free survival (PFS) (p=0.031) and overall survival (OS) (p=0.023). Patients with undetectable ctDNA negative (ctDNA-) at day 14 (D14) achieved an impressive 3-month complete response rate of 77.8% vs 22.2% (p=0.015) in patients with detectable ctDNA positive (ctDNA+), similar results observed for D28. CtDNA- at D28 predicted significantly longer 1-year PFS (90.9% vs 27.3%; p=0.004) and OS (90.9% vs 49.1%; p=0.003) compared with patients who remained ctDNA+. Notably, it is the first time to report that shorter ctDNA fragments (<170 base pairs) were significantly associated with poorer PFS (p=0.031 for D14; p=0.002 for D28) and OS (p=0.013 for D14; p=0.008 for D28) in patients with LBCL receiving CAR T-cell therapy. Multiple mutated genes exhibited an elevated prevalence among patients with progressive disease, including

conclusionsOur study highlights that dynamic ctDNA monitoring during CAR T-cell therapy can be a promising non-invasive method for early predicting treatment response and survival outcomes. Additionally, the ctDNA mutational profile provides novel insights into the mechanisms of tumor-intrinsic resistance to CAR19 T-cell therapy.

Indexed as

Circulating Tumor DNALymphoma, Large B-Cell, DiffuseGenomicsHumansImmunotherapy, AdoptiveNeoplasm Recurrence, LocalCirculating Tumor DNAChimeric antigen receptor - CARImmunotherapyLymphoma

Identifiers

PMID38443094
PMCPMC11146396
OpenAlexW4392469028

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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.