Modulating extracellular TCR-CD3 interaction to identify new immunotherapy targets against cancer
Bibliographic record
Abstract
Abstract T cell recognition of antigen and resulting proximal signaling are key steps in the initiation of the adaptive immune response. Previous studies targeting antigen binding site for enhancing T-cell responses to tumor antigens often lead to off-target effects and toxicity. Recently, we used nuclear magnetic resonance (NMR) spectroscopy, mutational analysis and computational docking to derive a 3D structure of the extracellular TCR-CD3 assembly. Further, biomolecular force probe (BFP) measurements allowed us to determine how 2D affinity and force-modulated TCR-pMHC kinetics depend on TCR-CD3 interaction sites and affect transduction of extracellular pMHC-TCR ligation into T cell function. Based on our TCR-CD3 structural model and binding data, we generated TCR libraries for a melanoma-specific TCR (DMF5) using site-specific mutagenesis in the Cbhelix 3 and helix 4-F strand regions of the TCR to optimize the TCR-CD3 interaction and to select for mutants with enhanced T-cell effector function. One Cb helix 4-F strand mutant, NP202203AA showed increased T cell response to antigen and showed enhanced TCR-pMHC bond lifetime (catch-bonds) in BFP assays leading to prolonged T cell signaling. In the future, DMF5 TCR with reengineered CD3 binding regions will be used in tumor rejection in pre-clinical mouse melanoma models for efficacy and toxicity to develop more effective T cell therapies for human targets.
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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) | 0.002 | 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".