Evidence map›Paper›PMID 38918817›Full record

ReviewMolecular cancer2024

Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond.

Zhaokai Zhou, Jiahui Wang, Jiaojiao Wang, Shuai Yang, Ruizhi Wang, Ge Zhang, Zhengrui Li, Run Shi, Zhan Wang, Qiong Lu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Molecular cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing 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

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

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Review
  6. Review
  7. Review
  8. Review
  9. Review
  10. Review
  11. Review
  12. Review
  13. Review
  14. Article
  15. Review
  16. Article
  17. Article
  18. Review
  19. Review
  20. Spotlight onGenes · 2025
    Article
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

10 authors.

Zhaokai Zhou *Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.
Jiahui Wang *Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.
Jiaojiao Wang *Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.
Shuai Yang *Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China.
Ruizhi WangDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China.
Ge ZhangDepartment of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China.
Zhengrui LiDepartment of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Run ShiDepartment of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Zhan WangDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China.
Qiong LuDepartment of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, China. christy_luq@csu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, and response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T cell immunotherapy has emerged as a successful treatment for hematologic malignancies, it remains a conundrum for solid tumors. The heterogeneity of TIME is responsible for poor outcomes in CAR-T cell immunotherapy against solid tumors. The advancement of highly sophisticated technology enhances our exploration in TIME from a multi-omics perspective. In the era of machine learning, multi-omics studies could reveal the characteristics of TIME and its immune resistance mechanism. Therefore, the clinical efficacy of CAR-T cell immunotherapy in solid tumors could be further improved with strategies that target unfavorable conditions in TIME. Herein, this review seeks to investigate the factors influencing TIME formation and propose strategies for improving the effectiveness of CAR-T cell immunotherapy through a multi-omics perspective, with the ultimate goal of developing personalized therapeutic approaches.

Indexed as

Immunotherapy, AdoptiveNeoplasmsReceptors, Chimeric AntigenTumor MicroenvironmentAnimalsGenomicsHumansT-LymphocytesReceptors, Chimeric AntigenChimeric antigen receptor T cellImmunotherapyMachine learningMulti-omicsSolid tumorsTumor immune microenvironment

Identifiers

PMID38918817
PMCPMC11201788

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.