ReviewBiomarker research2022
Nanobody-based CAR-T cells for cancer immunotherapy.
Review in Biomarker research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 3 of them syntheses that pooled 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.
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
110 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- An Assessment of the Effectiveness and Safety of Chimeric Antigen Receptor T-Cell Therapy in Multiple Myeloma Patients with Relapsed or Refractory Disease: A Systematic Review and Meta-Analysis.International journal of molecular sciences · 2024Pooled it
- The efficacy and safety of CD7 chimeric antigen receptor T-cell therapy for hematologic malignancies: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Effectiveness and safety of anti-BCMA chimeric antigen receptor T-cell treatment in relapsed/refractory multiple myeloma: a comprehensive review and meta-analysis of prospective clinical trials.Frontiers in pharmacology · 2023Pooled it
- Efficacy and safety of BCMA nanobody CAR T-cell therapy in relapsed or refractory plasma cell myeloma.Blood advances · 2025Trial
- Advances, challenges, and innovative strategies of CAR-T cell therapy in pancreatic cancer.Biomarker research · 2026Review
- Nanobody-based targeted cancer therapy and immunotherapy: fear not the future.Acta pharmacologica Sinica · 2026Review
- EF-1α-driven nanobody-based CD19-redirected CAR-T cells retain the antitumor efficacy, antigen-driven expansion, and cytokine secretion of CMV-driven counterparts.Molecular and cellular biochemistry · 2026Article
- GPCRs in CAR-T Cell Immunotherapy: Expanding the Target Landscape and Enhancing Therapeutic Efficacy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Yeast surface display-based screening and preliminary characterization of nectin-4-targeting nanobodies for bladder cancer.BMC biotechnology · 2026Article
- Application of CAR-T therapy in solid tumors: current opportunities and challenges.Cancer gene therapy · 2026Review
- Article
- A2AR as a key target for immune microenvironment remodeling in prostate cancer.Translational oncology · 2026Article
- Bridging Inflammation and Oncology: The Role and Therapeutic Potential of Macrophage Migration Inhibitory Factor in Lung Cancer.International journal of molecular sciences · 2026Review
- Nanobody-Engineered CLL-1 CAR T Cells: Optimizing Tumor-Specific Cytotoxicity and Minimizing Off-Tumor Toxicity.Cancer research communications · 2026Article
- Camelid-Derived Nanobodies Targeting Human Epidermal Growth Factor Receptor: Screening, Expression, and Functional Validation.Antibodies (Basel, Switzerland) · 2026Article
- Integrating CAR-T therapy with PD-1/PD-L1 blockade: Mechanisms, synergy, and optimized strategies in NSCLC.iScience · 2026Review
- Disulphide and sequence-encoded conformational priors guide nanobody structure prediction.bioRxiv : the preprint server for biology · 2026Article
- Next-generation CAR-T therapy for acute myeloid leukemia: bridging innovation with clinical translation.Annals of hematology · 2026Review
- Revisiting the Ofatumumab Epitope on CD20 through Integrative Molecular Dynamics and Flow Cytometry Analyses.Computational and structural biotechnology journal · 2026Article
- Advances in Nanotechnology for Lymphoma Treatment: Targeted Delivery, Immunomodulation, and TME-Responsive Therapy Strategies.International journal of nanomedicine · 2026Review
50 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chimeric antigen receptor T-cell (CAR-T) therapy is the result of combining genetic engineering-based cancer immunotherapy with adoptive cell therapy (ACT). CAR-T therapy has been successful in treating various types of hematological cancers. CARs are receptors made of an extracellular domain, a membrane-spanning domain, and an intracellular domain. The extracellular domain of CARs harbors an antigen-targeting domain responsible for recognizing and binding cell surface-expressed target antigens. Conventionally, the single-chain fragment variable (scFv) of a monoclonal antibody (mAb) is used as the antigen-targeting domain of CARs. However, of late, researchers have exploited nanobodies for this aim based on numerous rationales including the small size of nanobodies, their stability, specificity, and high affinity, and their easy and feasible development process. Many findings have confirmed that nanobody-based CAR-Ts can be as functional as scFv-based CAR-Ts in preclinical and clinical settings. In this review, we discuss the advantages and disadvantages of scFvs and nanobodies in regards to their application as the targeting domain of CARs. Ultimately, we discuss various CAR target antigens which have been targeted using nanobody-based CAR-T cells for the treatment of different types of malignancies.
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.