Evidence map›Paper›PMID 41159033›Full record

ReviewFrontiers in immunology2025

Next-generation T cell immunotherapy: overcoming exhaustion, senescence, and suppression.

Guangmei Li, Dengju Li, Xiaojian Zhu

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. Review
  8. Review
  9. Review
  10. Review
  11. Article
  12. 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

3 authors.

Guangmei LiDepartment of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Dengju LiDepartment of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Xiaojian ZhuDepartment of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

T-cells are a core component of tumor immunotherapy because of their potent ability to identify and kill cancer cells. Yet efficacy is limited by exhaustion, senescence, metabolic dysregulation, an immunosuppressive tumor microenvironment (TME), and limited persistence. This review analyzed these key issues and proposed targeted improvement strategies. Emerging approaches encompass pharmacological modulation of T cell activation and survival pathways, epigenetic reprogramming to reverse exhaustion and senescence, metabolic engineering, combinatorial targeting of immunosuppressive TME components and advanced genetic tools, notably CRISPR-Cas9-based CAR-T optimization, which exemplifies how precise genome editing can enhance therapeutic efficacy. We review the progress and prospects of T-cell improvement strategies in tumor immunotherapy, emphasizing the need for further exploration to enhance the broader application and long-term efficacy of T-cell therapies. This review highlights recent advances and future directions in T-cell engineering, metabolic modulation, and microenvironment targeting, aiming to translate innovations into effective cancer immunotherapies.

Indexed as

ImmunotherapyImmunotherapy, AdoptiveNeoplasmsT-LymphocytesAnimalsCellular SenescenceHumansLymphocyte ActivationTumor Microenvironmentmetabolic regulationmicroenvironment optimizationT-cell enhancementT-cell exhaustion and agingtumor immunotherapy

Identifiers

PMID41159033
PMCPMC12554686

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.