Tuning TCR complex recruitment to the T cell antigen coupler (TAC) enhances TAC-T cell function
Bibliographic record
Abstract
The T cell antigen coupler (TAC) receptor is a novel synthetic receptor designed to maximize the therapeutic potential of T cells in the absence of tonic signaling or receptor-related toxicities. Prior studies indicated that TACs provide safe and long-lasting anti-tumor immunity in multiple preclinical models of solid tumors supported by mounting clinical evidence. TAC receptors function by targeting a cancer associated surface antigen while recapitulating natural T cell receptor (TCR) signaling, which involves both TCR/CD3 recruitment and intracellular CD4 co-receptor activity. While other receptor designs exist that redirect TCR signaling towards cancer associated antigens, the TAC technology is unique in that antigen binding is distinctly separated from TCR/CD3 complex recruitment. In the present study, we show that single amino-acid changes in the TAC domain responsible for TCR recruitment of a Claudin 18.2-directed TAC receptor led to enhanced in vivo functionality. Analyzing biophysical properties of the receptor suggests that TAC receptors with high TCR affinities are suboptimal compared to receptor constructs that show lower TCR affinities with notably fast off-rates. This work demonstrates that balancing TCR recruitment is critical when designing effective TAC T cell receptors, a concept that may apply more broadly to other therapeutic approaches relying on TCR signaling.
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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 | 0.000 |
| 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".