Optimization of T-cell Reactivity by Exploiting TCR Chain Centricity for the Purpose of Safe and Effective Antitumor TCR Gene Therapy
Bibliographic record
Abstract
Adoptive transfer of T cells redirected by a high-affinity antitumor T-cell receptor (TCR) is a promising treatment modality for cancer patients. Safety and efficacy depend on the selection of a TCR that induces minimal toxicity and elicits sufficient antitumor reactivity. Many, if not all, TCRs possess cross-reactivity to unrelated MHC molecules in addition to reactivity to target self-MHC/peptide complexes. Some TCRs display chain centricity, in which recognition of MHC/peptide complexes is dominated by one of the TCR hemi-chains. In this study, we comprehensively studied how TCR chain centricity affects reactivity to target self-MHC/peptide complexes and alloreactivity using the TCR, clone TAK1, which is specific for human leukocyte antigen-A*24:02/Wilms tumor 1(235-243) (A24/WT1(235)) and cross-reactive with B*57:01 (B57). The TAK1β, but not the TAK1α, hemi-chain possessed chain centricity. When paired with multiple clonotypic TCRα counter-chains encoding TRAV12-2, 20, 36, or 38-2, the de novo TAK1β-containing TCRs showed enhanced, weakened, or absent reactivity to A24/WT1(235) and/or to B57. T cells reconstituted with these TCRα genes along with TAK1β possessed a very broad range (>3 log orders) of functional and structural avidities. These results suggest that TCR chain centricity can be exploited to enhance desired antitumor TCR reactivity and eliminate unwanted TCR cross-reactivity. TCR reactivity to target MHC/peptide complexes and cross-reactivity to unrelated MHC molecules are not inextricably linked and are separable at the TCR sequence level. However, it is still mandatory to carefully monitor for possible harmful toxicities caused by adoptive transfer of T cells redirected by thymically unselected TCRs.
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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".