ReviewCancer research2026
Optimizing In Vivo CAR T-cell Engineering for Cancer Immunotherapy.
Review in Cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Pharmacokinetic and pharmacodynamic characterization of CD8-targeted lentiviral vector forMolecular therapy. Advances · 2026Article
- In Vivo CAR-Based Immune Cell Engineering: Future Applications and Challenges in Malignant Glioma.Cancers · 2026Review
- In Vivo mRNA-Lipid Nanoparticle CAR-T Cell Engineering: Advances, Challenges, and Clinical Translation.Biomedicines · 2026Review
- In vivo CAR-cell therapy: current challenges and emerging therapeutic advances.Molecular biomedicine · 2026Review
- Optimizing next-generation CAR-macrophages against solid tumors: challenges and potential strategies.Journal of hematology & oncology · 2026Review
- Beyond CAR-T and oncology: broadening chimeric antigen receptor technologies across cell types and diseases.Precision clinical medicine · 2026Review
- Engineering CAR-T cells for solid tumors: overcoming antigenic, trafficking, and microenvironmental barriers.Frontiers in immunology · 2026Review
- Activation of endogenous retroviruses in tumor cells and their immunomodulatory mechanisms: from molecular basis to clinical translation.Frontiers in oncology · 2026Review
- CA9-Targeted Liposomal Delivery of siETS1 Inhibits Clear Cell Renal Cell Carcinoma Progression by Disrupting the ETS1/MYC Regulatory Axis.International journal of nanomedicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Chimeric antigen receptor (CAR) T-cell therapy enables potent, antigen-specific immune responses and has demonstrated success in treating hematologic malignancies. However, conventional ex vivo CAR T manufacturing remains costly, individualized, and logistically complex, posing significant barriers to accessibility and scalability. In vivo CAR T-cell engineering offers a transformative alternative by reprogramming endogenous T cells within the patient, bypassing the need for cell harvesting and expansion. This review focuses on current in vivo CAR T delivery strategies, including viral vectors (such as lentiviruses, γ-retroviruses, adeno-associated viruses, and viral-like particles) and nonviral systems (such as lipid nanoparticles and polymer-based carriers), with a focus on how these platforms are engineered to achieve efficient, specific, and safe CAR transgene transfer. We also discuss the design principles of vector tropism, membrane modifications, and targeting ligands, as well as translational studies in both preclinical and clinical settings. Finally, the review explores delivery-related challenges and future perspectives for optimizing vector stability, enhancing T-cell targeting, and reducing immunogenicity to advance in vivo CAR T therapy toward broader clinical applications.
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.