Harnessing CD3 diversity to optimize CAR T cells
Bibliographic record
Abstract
Current US Food and Drug Administration-approved chimeric antigen receptor (CAR) T cells harbor the T cell receptor (TCR)-derived ζ chain as an intracellular activation domain in addition to costimulatory domains. The functionality in a CAR format of the other chains of the TCR complex, namely CD3δ, CD3ε and CD3γ, instead of ζ, remains unknown. In the present study, we have systematically engineered new CD3 CARs, each containing only one of the CD3 intracellular domains. We found that CARs containing CD3δ, CD3ε or CD3γ cytoplasmic tails outperformed the conventional ζ CAR T cells in vivo. Transcriptomic and proteomic analysis revealed differences in activation potential, metabolism and stimulation-induced T cell dysfunctionality that mechanistically explain the enhanced anti-tumor performance. Furthermore, dimerization of the CARs improved their overall functionality. Using these CARs as minimalistic and synthetic surrogate TCRs, we have identified the phosphatase SHP-1 as a new interaction partner of CD3δ that binds the CD3δ-ITAM on phosphorylation of its C-terminal tyrosine. SHP-1 attenuates and restrains activation signals and might thus prevent exhaustion and dysfunction. These new insights into T cell activation could promote the rational redesign of synthetic antigen receptors to improve cancer immunotherapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".