Generating <em>De Novo</em> Antigen-specific Human T Cell Receptors by Retroviral Transduction of Centric Hemichain
Bibliographic record
Abstract
T cell receptors (TCRs) are used clinically to direct the specificity of T cells to target tumors as a promising modality of immunotherapy. Therefore, cloning TCRs specific for various tumor-associated antigens has been the goal of many studies. To elicit an effective T cell response, the TCR must recognize the target antigen with optimal affinity. However, cloning such TCRs has been a challenge and many available TCRs possess sub-optimal affinity for the cognate antigen. In this protocol, we describe a method of cloning de novo high affinity antigen-specific TCRs using existing TCRs by exploiting hemichain centricity. It is known that for some TCRs, each TCRα or TCRβ hemichain do not contribute equally to antigen recognition, and the dominant hemichain is referred to as the centric hemichain. We have shown that by pairing the centric hemichain with counter-chains differing from the original counter-chain, we are able to maintain the antigen specificity, while modulating its interaction strength for the cognate antigen. Thus, the therapeutic potential of a given TCR can be improved by optimizing the pairing between the centric and counter hemichains.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".