ReviewBiomarker research2025
Nanobody-enhanced chimeric antigen receptor T-cell therapy: overcoming barriers in solid tumors with VHH and VNAR-based constructs.
Review in Biomarker research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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.
Who cites it
11 citing papers in PubMed.
- AI-enabled discovery and biochemical optimization of minibinders targeting cancer cell-surface proteins.Nature communications · 2026Article
- Humanized biparatopic nanobody-based CAR-T cells overcome antigen-heterogeneity in multiple myeloma.Journal of translational medicine · 2026Article
- Novel approaches to modulate CAR-T cell function by targeting the tumor microenvironment in ovarian cancer.Journal of ovarian research · 2026Review
- Disulphide and sequence-encoded conformational priors guide nanobody structure prediction.bioRxiv : the preprint server for biology · 2026Article
- Engineering Immunity: Current Progress and Future Directions of CAR-T Cell Therapy.International journal of molecular sciences · 2026Review
- Advancements and expanding applications of CAR-T cell therapy.Frontiers in immunology · 2026Review
- Revisiting the Ofatumumab Epitope on CD20 through Integrative Molecular Dynamics and Flow Cytometry Analyses.Computational and structural biotechnology journal · 2026Article
- Review
- Application of nanobody‑based CAR‑T in tumor immunotherapy (Review).International journal of molecular medicine · 2025Review
- Nattokinase-driven remodeling of tumor microenvironment enhances the efficacy of MSLN-targeted CAR-T cell therapy in solid tumors.Cancer immunology, immunotherapy : CII · 2025Article
- VNAR: shark single-domain antibodies for the new era of medical biotechnology.Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
CAR-T cells are genetically modified T lymphocytes that express chimeric antigen receptors (CAR) on their surfaces. These receptors enable T lymphocytes to recognize specific antigens on target cells, triggering a response that leads to targeted cytotoxicity. While CAR-T therapy has effectively treated various blood cancers, it faces significant challenges in addressing solid tumors. These challenges include identifying precise tumor antigens, overcoming antigen evasion, and enhancing the function of CAR-T cells within the tumor microenvironment. Single domain antibody, versatile tools with low immunogenicity, high stability, and strong affinity, show promise for improving the efficacy of CAR-T cells against solid tumors. By addressing these challenges, single domain antibody has the potential to overcome the limitations associated with ScFv antibody-based CAR-T therapies. This review highlights the benefits of utilizing single domain antibody in CAR-T therapy, particularly in targeting tumor antigens, and explores development strategies that could advance the field.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.