ReviewNature reviews. Clinical oncology2023
Advancing CAR T cell therapy through the use of multidimensional omics data.
Review in Nature reviews. Clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 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
75 citing papers in PubMed, 107 citations in OpenAlex.
- Harnessing the NKG2D immune surveillance pathway using engineered T-cells for the treatment of human disease.Human vaccines & immunotherapeutics · 2026Review
- Thymic APC Networks Orchestrate T-Cell Selection: Mechanisms and Therapeutic Opportunities in Immune Disorders.Immunology · 2026Review
- Next-generation CAR-T cell therapy against cancer: precision engineering, programmable immunity, and emerging clinical frontiers.Journal of the Egyptian National Cancer Institute · 2026Review
- Recent advances in molecular mechanisms to improve the efficacy of CAR-T cell therapy for viral diseases, cancer, and autoimmune diseases.Stem cell research & therapy · 2026Review
- Harnessing immunity against breast cancer: from checkpoints to cell therapies.Journal of translational medicine · 2026Review
- A retrospective pharmacovigilance analysis based on the FAERS database reveals sex-associated differences in toxicities of CAR T-cell therapy.Nature communications · 2026Article
- Review
- Novel Insights into the Role of circRNAs in Cancer Immunotherapy Resistance and Clinical Implications.International journal of molecular sciences · 2026Review
- Beyond the infusion: nursing at the vanguard of cytokine release syndrome rescue in CAR-T cell therapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Combining Small-Molecular Compounds With CAR T-Cell Therapy: Novel Strategies for Enhanced Cancer Immunotherapy.Cancer innovation · 2026Review
- Review
- β-Catenin stabilization protects against alveolar hemorrhage through amphiregulin- and BATF-mediated Tregs.JCI insight · 2026Article
- Low-input deep learning platform for citrullinated peptide identification, autoantigen discovery and rheumatoid arthritis treatment stratification.Nature biomedical engineering · 2026Article
- Tumour-resident oncolytic bacteria trigger potent anticancer effects through selective intratumoural thrombosis and necrosis.Nature biomedical engineering · 2026Article
- Process Systems Engineering in Precision Medicine: Opportunities in Autologous CAR-T Therapy.Engineering in life sciences · 2026Review
- Antibiotic Use Before CAR-T Treatment Is Associated with Inferior Outcomes in DLBCL Lymphoma Patients.Biomedicines · 2026Article
- Functional genomics-guided design of CAR-T and CAR-NK therapies in hematological malignancies: aligning cellular engineering with immune escape and microenvironmental resistance.Frontiers in genome editing · 2026Review
- Rejuvenated Hematopoietic Stem and Progenitor Cell-Engineered CAR-Armored Natural Killer T Cells for Malignant Pleural Mesothelioma.Research (Washington, D.C.) · 2026Article
- The impact of the large B-cell lymphoma tumor microenvironment on efficacy of CD19-directed CAR-T immunotherapy.Frontiers in immunology · 2026Review
- CAR-T cells in solid tumors: engineering, biomarkers, translational pathways and the road ahead.Frontiers in immunology · 2026Review
15 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
5 authors at 3 institutions in 1 country.
Funding
Abstract
Despite the notable success of chimeric antigen receptor (CAR) T cell therapies in the treatment of certain haematological malignancies, challenges remain in optimizing CAR designs and cell products, improving response rates, extending the durability of remissions, reducing toxicity and broadening the utility of this therapeutic modality to other cancer types. Data from multidimensional omics analyses, including genomics, epigenomics, transcriptomics, T cell receptor-repertoire profiling, proteomics, metabolomics and/or microbiomics, provide unique opportunities to dissect the complex and dynamic multifactorial phenotypes, processes and responses of CAR T cells as well as to discover novel tumour targets and pathways of resistance. In this Review, we summarize the multidimensional cellular and molecular profiling technologies that have been used to advance our mechanistic understanding of CAR T cell therapies. In addition, we discuss current applications and potential strategies leveraging multi-omics data to identify optimal target antigens and other molecular features that could be exploited to enhance the antitumour activity and minimize the toxicity of CAR T cell therapy. Indeed, fully utilizing multi-omics data will provide new insights into the biology of CAR T cell therapy, further accelerate the development of products with improved efficacy and safety profiles, and enable clinicians to better predict and monitor patient responses.
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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.