Engineering a Dual Specificity γδ T-Cell Receptor for Cancer Immunotherapy
Bibliographic record
Abstract
γδ T-cells provide immune surveillance against cancer, straddling both innate and adaptive immunity. G115 is a clonal γδ T-cell receptor (TCR) of the Vγ9Vδ2 subtype which can confer responsiveness to phosphoantigens (PAgs) when genetically introduced into conventional αβ T-cells. Cancer immunotherapy using γδ TCR-engineered T-cells is currently under clinical evaluation. In this study, we sought to broaden the cancer specificity of the G115 γδ TCR by insertion of a tumour-binding peptide into the complementarity-determining region (CDR) three regions of the TCR δ2 chain. Peptides were selected from the foot and mouth disease virus A20 peptide which binds with high affinity and selectivity to αvβ6, an epithelial-selective integrin that is expressed by a range of solid tumours. Insertion of an A20-derived 12mer peptide achieved the best results, enabling the resulting G115 + A12 T-cells to kill both PAg and αvβ6-expressing tumour cells. Cytolytic activity of G115 + A12 T-cells against PAg-presenting K562 target cells was enhanced compared to G115 control cells, in keeping with the critical role of CDR3 δ2 length for optimal PAg recognition. Activation was accompanied by interferon (IFN)-γ release in the presence of either target antigen, providing a novel dual-specificity approach for 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".