ArticleScience (New York, N.Y.)2022
Decoding CAR T cell phenotype using combinatorial signaling motif libraries and machine learning.
Article in Science (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers.
What it found
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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
77 citing papers in PubMed, 116 citations in OpenAlex.
- Harnessing macrophage signaling pathways and scalable engineering for next-generation immunotherapies.Signal transduction and targeted therapy · 2026Review
- Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems.Advanced drug delivery reviews · 2026Review
- Design and optimization of a kinase-controlled allosteric switch.Nature methods · 2026Article
- Harnessing immunity against breast cancer: from checkpoints to cell therapies.Journal of translational medicine · 2026Review
- A guide to CAR T cell therapies: development, current status and future prospects.Nature reviews. Immunology · 2026Review
- Single-cell genomics links targeted functional manipulations to efficacy-associated chromatin signatures in CAR T cells.Research square · 2026Article
- Probiotics and Postbiotics in Life-Style Disease Management: A Comprehensive Review on the Technologies in the Era of Omics and Artificial Intelligence.Probiotics and antimicrobial proteins · 2026Review
- Programmable synthetic cytokine receptors polarize macrophages to user-defined functional states.bioRxiv : the preprint server for biology · 2026Article
- Nanotechnology-enhanced CAR-T therapy strategies in cancer, aging, and autoimmune diseases.Journal of hematology & oncology · 2026Review
- Review
- A motif-vocabulary model of CAR T-cell intracellular domains identifies determinants of immunophenotype differentiation.bioRxiv : the preprint server for biology · 2026Article
- Unleashing CAR-T potential in solid tumors: overcoming intrinsic and extrinsic hurdles to improve therapy.Cancer immunology, immunotherapy : CII · 2026Review
- AI-guided CAR designs and targeted pathway modulation to enhance multi-antigen CAR T cell durability and overcome antigen escape.Nature communications · 2026Article
- Dual-module aCAR-iCAR NK cells for solid tumors: cascade resistance mechanisms, AI-driven engineering, and precision stratification.Frontiers in immunology · 2026Review
- Advancements and expanding applications of CAR-T cell therapy.Frontiers in immunology · 2026Review
- CAR-T cell signaling dynamics, rational design principles and artificial intelligence for next-generation chimeric antigen receptors.Frontiers in immunology · 2026Review
- Predictive markers for the efficacy of CAR T-cell therapy: the interplay between CAR T-cell fitness and systemic immunity.Blood advances · 2025Review
- A unified genetic perturbation language for human cellular programming.bioRxiv : the preprint server for biology · 2025Article
- Nanoparticle-based strategy in CAR-T cell immunotherapy: challenges, implications, and perspectives.Molecular cancer · 2025Review
- Harnessing chimeric antigen receptor macrophages against solid tumors.Cancer communications (London, England) · 2025Review
17 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
9 authors at 2 institutions in 1 country.
Funding
Abstract
Chimeric antigen receptor (CAR) costimulatory domains derived from native immune receptors steer the phenotypic output of therapeutic T cells. We constructed a library of CARs containing ~2300 synthetic costimulatory domains, built from combinations of 13 signaling motifs. These CARs promoted diverse human T cell fates, which were sensitive to motif combinations and configurations. Neural networks trained to decode the combinatorial grammar of CAR signaling motifs allowed extraction of key design rules. For example, non-native combinations of motifs that bind tumor necrosis factor receptor-associated factors (TRAFs) and phospholipase C gamma 1 (PLCγ1) enhanced cytotoxicity and stemness associated with effective tumor killing. Thus, libraries built from minimal building blocks of signaling, combined with machine learning, can efficiently guide engineering of receptors with desired phenotypes.
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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.