Evidence map›Paper›PMID 41121752›Full record

ReviewClinical and translational medicine2025

Current status and future perspectives of multi-modal bacteria-based cancer therapies.

Shuai Fan, Siyu Zhu, Wenyu Wang, Yuetong Liu, Yutong Zhou, Hao Li, Bofeng Liu, Qin Xia, Lili Huang, Lei Dong

Abstract readReview
In one paragraph

Review in Clinical and translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Shuai FanAdvanced Technology Research Institute, State Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), School of Life Science, Beijing Institute of Technology, Beijing, China.ORCID 0009-0004-0038-0530
Siyu ZhuSchool of Medical Technology, Beijing Institute of Technology, Beijing, China.
Wenyu WangAdvanced Technology Research Institute, State Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), School of Life Science, Beijing Institute of Technology, Beijing, China.
Yuetong LiuAdvanced Technology Research Institute, State Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), School of Life Science, Beijing Institute of Technology, Beijing, China.
Yutong ZhouAdvanced Technology Research Institute, State Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), School of Life Science, Beijing Institute of Technology, Beijing, China.
Hao LiChina Talent Group, Beijing, China.
Bofeng LiuAdvanced Technology Research Institute, State Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), School of Life Science, Beijing Institute of Technology, Beijing, China.
Qin XiaAdvanced Technology Research Institute, State Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), School of Life Science, Beijing Institute of Technology, Beijing, China.
Lili HuangSchool of Medical Technology, Beijing Institute of Technology, Beijing, China.
Lei DongAdvanced Technology Research Institute, State Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), School of Life Science, Beijing Institute of Technology, Beijing, China.ORCID 0000-0001-7530-7427

Funding

Beijing Municipal Natural Science Foundation QY24187Fundamental Research Funds for the Central Universities 2024CX06057Fundamental Research Funds for the Central Universities 2024CX06114Hebei Natural Science Foundation C2025105003National Natural Science Foundation of China 32570906National Natural Science Foundation of China U21A20200Shandong Provincial Natural Science Foundation ZR2024MC035Shandong Provincial Natural Science Foundation ZR2024QC039
6 · The paper itself

Abstract

backgroundTargeted drug delivery systems have garnered increasing research interest in cancer threapy. Bacteria have emerged as a promising vehicle due to their innate ability to the tumour microenvironment (TME) and their intrinsic immune-stimulating properties. This review explores the application of bacteria in oncology, emphasizing the tumour-targeting behaviour of specific strains, their immunomodulatory functions, and their potential as delivery platforms for the controlled release of therapeutic agents. MAIN TEXT: This review synthesizes recent advances in bacteria-mediated cancer therapy, focusing on the mechanisms underlying bacterial targeting of hypoxic and immunosuppressive regions within the tumor microenvironment (TME). We discuss how genetic modification has been employed to design recombinant bacterial strains with enhanced tumor specificity and amplified therapeutic effects. Furthermore, the integration of bacteria with nanotechnology has facilitated the development of hybrid systems capable of targeted drug delivery and triggered-release mechanisms. The combination of bacterial therapy with other treatment modalities-such as photodynamic (PDT) and sonodynamic therapies (SDT)-is also examined, emphasizing their synergistic potential in overcoming tumor heterogeneity and enhancing anti-tumor immunity. Finally, we survey the current clinical progress of bacteria-based therapeutics and offer perspectives on the future role of artificial intelligence (AI) in improving the design and application of these living medicines.

conclusionsBacteria-based delivery systems represent a multifunctional and innovative strategy in the evolution of targeted cancer therapies. Through genetic modification and nanobiotechnology approaches, bacteria can be customized to mediate multi-effect synergistic treatments for cancer, enhancing the precision, safety, and efficacy of cancer therapies. With the ongoing integration of advanced technologies, including AI, there is great potential to overcome existing limitations and accelerate the clinical translation of bacterial anticancer therapies. This interdisciplinary effort is poised to open new avenues for next-generation cancer treatments and lay the foundation for future directions in cancer research and therapeutic practice. KEY POINTS: Bacteria exhibit inherent tumour-targeting capabilities, particularly thriving in hypoxic tumour microenvironments (TMEs) and activating potent anti-tumour immune responses through pathogen-associated molecular patterns (PAMPs) and immunomodulation. Genetic engineering and nanobiotechnology enable advanced bacterial therapies, allowing for reduced toxicity, controlled proliferation, targeted drug delivery and the expression of therapeutic payloads (e.g., cytokines, enzymes, tumour antigens) within tumours. Bacteria serve as versatile platforms for multi-modal synergistic therapy, effectively combining with immunotherapy, photodynamic therapy (PDT), thermodynamic therapy (TDT), photothermal therapy (PTT) and sonodynamic therapy (SDT) to significantly enhance tumour eradication. Artificial intelligence (AI) is poised to revolutionise bacterial cancer therapy, offering powerful tools for optimising synthetic biology designs (e.g., promoters, gene circuits), nanocarrier engineering and predicting bacterial-host interactions for more effective and safer treatments.

Indexed as

BacteriaDrug Delivery SystemsNeoplasmsHumansTumor Microenvironmentbacteriacancergenetic modificationnanotechnologysynergistic therapyTME

Identifiers

PMID41121752
PMCPMC12541140

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.