ArticleNature biomedical engineering2026
Predictive biomarkers of response to chimeric antigen receptor (CAR) T-cell therapy for pan-haematologic cancer.
Article in Nature biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed.
- Remodeling memory T cells with chemotherapy and immune checkpoint inhibitors as host pre-conditioning to empower in vivo CAR-T therapy.Synthetic and systems biotechnology · 2026Review
- AI and authorship: Norms and uses to preserve human-led science.PNAS nexus · 2026Article
- A modular γδ TCR-T platform combining KRAS pMHC targeting with re-dosable mRNA engager redirection.Journal of hematology & oncology · 2026Article
- Development of tandem canine CAR T cells to enable comparative studies of aggressive B-cell lymphoma.Molecular cancer therapeutics · 2026Article
- Determinants of Chimeric Antigen Receptor (CAR) T Cell Success: In Vitro and In Vivo Preclinical Assessment.Cancers · 2026Review
- Early Immune Signature Features, Including TLR2 and TLR4 Expression, Are Associated with Complete Remission After CD19 CAR-T Cell Therapy.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Predictive biomarkers of response to chimeric antigen receptor (CAR) T-cell therapy for pan-haematologic cancer.Nature biomedical engineering · 2026Article
- CAR T cell cancer immunotherapy: who does the job?Frontiers in immunology · 2026Review
Corrections and comments
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Authors and funding
37 authors.
Funding
Abstract
Chimeric antigen receptor (CAR) T-cell therapy holds great promise for patients with cancer, and the identification of predictive biomarkers is crucial in finding new ways to guide therapy. Major challenges to the application of informatics and machine learning in CAR T-cell therapy include limited sample sizes and non-uniformity in data generation across cancer indications and trials. Here we took a global, pan-haematologic cancer approach, analysing 256 patients across 5 cancer types and 13 clinical trials. We generated data using a framework that included pre-infusion clinical features, over 2 million apheresis T cells analysed by flow cytometry using 17 unique markers, ex vivo T-cell expansion during CAR T-cell manufacture, more than 90,000 measurements of 30 serum markers and serial tracking of circulating CAR T cells using qPCR. From this data resource, we demonstrate the potential of pan-cancer predictive biomarkers that capture generalizable characteristics of treatment response and non-response in CAR T-cell therapy.
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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.