ArticleNature biotechnology2023
Deconvolution of clinical variance in CAR-T cell pharmacology and response.
Article in Nature biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 47 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
47 citing papers in PubMed.
- Single-Cell RNA Analysis Reveals Cell-Intrinsic Functions of CAR T Cells Correlating with Response in a Phase II Study of Lymphoma Patients.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Trial
- Programmable CAR-engaging particles rewire human CAR T-cell signaling for sustainable ex vivo expansion.Nature communications · 2026Article
- Single-cell genomics links targeted functional manipulations to efficacy-associated chromatin signatures in CAR T cells.Research square · 2026Article
- IL4I1+ macrophages drive hepatocellular carcinoma progression by responding to biomechanical cues.JHEP reports : innovation in hepatology · 2026Article
- Review
- In vivo CAR-T therapy: from molecular design to precision delivery.Journal of nanobiotechnology · 2026Review
- Modeling and Simulation in Development and Regulatory Approval of Chimeric Antigen Receptor T-Cell Therapies: An Analysis of European Medicines Agency Centralized Procedures From 2018 to 2024.CPT: pharmacometrics & systems pharmacology · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Agent-based modeling of cellular dynamics in adoptive cell therapy.Communications biology · 2026Article
- Multidimensional transcriptome dataset for systematic evaluation of Jakyakgamcho-tang-induced cell signatures.Scientific data · 2026Article
- A Pharmacologic Model Predicts that Tumor Debulking Improves CAR T-cell Efficacy in Large B-cell Lymphoma.Blood cancer discovery · 2026Article
- Mathematical modeling of combinatorial antigen targeting with multiple CAR T-cell products for glioblastoma treatment.NPJ systems biology and applications · 2026Article
- Beyond exhaustion: T cell fitness for next generation of immunotherapy for hematological cancer.Frontiers in immunology · 2026Review
- Data-driven computational modeling of CAR-T cell function.Frontiers in immunology · 2026Article
- Foundational Principles for the Quantitative Translation of T-Cell Therapeutics for Hematologic Malignancies and Immunology.Clinical pharmacology and therapeutics · 2026Review
- Anti-CD19 CAR T-cell therapy for autoimmune diseases: from exploratory clinical trials to quantitative pharmacodynamic modeling development.Frontiers in immunology · 2026Review
- Multi-parametric profiling of IL-7-augmented GD2.CART products in a phase 1 clinical trial.iScience · 2025Article
- CD28 signaling complexes are correlated with patient outcomes in anti-CD19 41BB-costimulation CAR T cell therapy.Journal for immunotherapy of cancer · 2025Article
- Enhancing the potency of CAR-T cells against solid tumors through transcription factor engineering.JCI insight · 2025Review
- Microfluidic technologies for enhancing the potency, predictability and affordability of adoptive cell therapies.Nature biomedical engineering · 2025Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chimeric antigen receptor T cell (CAR-T) expansion and persistence vary widely among patients and predict both efficacy and toxicity. However, the mechanisms underlying clinical outcomes and patient variability are poorly defined. In this study, we developed a mathematical description of T cell responses wherein transitions among memory, effector and exhausted T cell states are coordinately regulated by tumor antigen engagement. The model is trained using clinical data from CAR-T products in different hematological malignancies and identifies cell-intrinsic differences in the turnover rate of memory cells and cytotoxic potency of effectors as the primary determinants of clinical response. Using a machine learning workflow, we demonstrate that product-intrinsic differences can accurately predict patient outcomes based on pre-infusion transcriptomes, and additional pharmacological variance arises from cellular interactions with patient tumors. We found that transcriptional signatures outperform T cell immunophenotyping as predictive of clinical response for two CD19-targeted CAR-T products in three indications, enabling a new phase of predictive CAR-T product development.
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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.