Evidence map›Paper›PMID 41964040›Full record

ReviewJournal of hematology & oncology2026

Optimizing next-generation CAR-macrophages against solid tumors: challenges and potential strategies.

Yizhao Chen, Lucheng Zhou, Xinlei Chen, Shuai Wang, Weiwei Chen, Zixuan Li, Ji Qiu, Ruilin Li, Jiajie Tu, Ning Lin

Abstract readReview
In one paragraph

Review in Journal of hematology & oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Yizhao Chen *Department of Pharmacy, The Third Affiliated Hospital of Anhui Medical University, Hefei First People's Hospital, Hefei, Anhui, China.
Lucheng Zhou *Department of Neurosurgery, The Affiliated Chuzhou Hospital of Anhui Medical University, The First People's Hospital of Chuzhou, 369# Zuiwengxi Road, Nanqiao District, Anhui, Chuzhou, China.
Xinlei Chen *Key Laboratory of Anti-Inflammatory and Immune Medicine, Ministry of Education, Anhui Collaborative Innovation Center of Anti-Inflammatory and Immune Medicine, Institute of Clinical Pharmacology, School of Pharmacy, Anhui Medical University, 81# Meishan Road, Shushan District, Anhui, Hefei, China.
Shuai WangDepartment of Neurosurgery, The Affiliated Chuzhou Hospital of Anhui Medical University, The First People's Hospital of Chuzhou, 369# Zuiwengxi Road, Nanqiao District, Anhui, Chuzhou, China.
Weiwei ChenDepartment of Pharmacy, The Third Affiliated Hospital of Anhui Medical University, Hefei First People's Hospital, Hefei, Anhui, China.
Zixuan LiDepartment of Pharmacy, The Third Affiliated Hospital of Anhui Medical University, Hefei First People's Hospital, Hefei, Anhui, China.
Ji QiuDepartment of Pharmacy, The Third Affiliated Hospital of Anhui Medical University, Hefei First People's Hospital, Hefei, Anhui, China. ahqiuji@163.com.
Ruilin LiDepartment of Pharmacy, The Third Affiliated Hospital of Anhui Medical University, Hefei First People's Hospital, Hefei, Anhui, China. liruilin0986@hotmail.com.
Jiajie TuKey Laboratory of Anti-Inflammatory and Immune Medicine, Ministry of Education, Anhui Collaborative Innovation Center of Anti-Inflammatory and Immune Medicine, Institute of Clinical Pharmacology, School of Pharmacy, Anhui Medical University, 81# Meishan Road, Shushan District, Anhui, Hefei, China. tujiajie@ahmu.edu.cn.
Ning LinDepartment of Neurosurgery, The Affiliated Chuzhou Hospital of Anhui Medical University, The First People's Hospital of Chuzhou, 369# Zuiwengxi Road, Nanqiao District, Anhui, Chuzhou, China. linning@ahmu.edu.cn.

Funding

National Natural Science Foundation of China 82504836Scientific and Technological Project of Bengbu Medical University under the "Healthcare Alliance" in 2024 2024byzd361
6 · The paper itself

Abstract

Chimeric antigen receptor macrophage (CAR-M) therapy has emerged as a highly promising novel platform in solid tumor immunotherapy. Leveraging its inherent tumor-homing capacity, potent phagocytic function, and potential to remodel the tumor microenvironment (TME), CAR-M offers a new strategic approach to address the limitations faced by CAR-T therapy in solid tumors, such as poor infiltration and immunosuppression. Despite these mechanistic advantages, clinical outcomes with first-generation CAR-M constructs have been modest, largely due to their limited in vivo persistence and effector activity. In this review, we summarize the core challenges limiting the efficacy and clinical application of CAR-M, and provide an in-depth discussion of engineering strategies aimed at enhancing its anti-tumor activity through optimization of the CAR molecular structure. Beyond CAR-M engineering, we also outline recent advances in combining CAR-M with other therapeutic modalities and discussing their underlying synergistic mechanisms. Looking forward, we highlight next-generation CAR-M platforms, such as in vivo edited CAR-M and CAR-monocytes, which aim to simplify manufacturing, reduce costs, and enable more precise immune modulation. Although challenges remain in manufacturing, durability of response, and safety, continuous technological innovation and rational combination strategies are accelerating the translation of CAR-M therapy from proof-of-concept toward clinical application, holding promise for opening new avenues in solid tumor treatment.

Indexed as

Immunotherapy, AdoptiveMacrophagesNeoplasmsReceptors, Chimeric AntigenAnimalsHumansTumor MicroenvironmentReceptors, Chimeric AntigenCAR-MacrophageCombined therapyImmunotherapyIn vivo editingSolid tumor

Identifiers

PMID41964040
PMCPMC13130784

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.