An Engineered Soluble Single‐Chain TCR Engager for KRAS‐G12V Specific Tumor Immunotherapy
Bibliographic record
Abstract
T cell receptor (TCR) based immunotherapy is an attractive strategy to target a wide range of intra-tumoral antigens and elicit robust tumor cytotoxicity. However, engineering soluble TCR engagers that preserve physiological affinity is crucial for universal TCR drug development, yet remains challenging. In the present study, multiple TCR engagers featuring diverse architectures based on the KRAS-G12V specific 1-2C TCR in the context of HLA-A*11:01 is designed and evaluated. Notably, a soluble tandem double single-chain TCR (STanD-scTCR) engager, comprising two repeated single-chain variable fragment (scFv) TCRs, exhibit enhanced binding avidity and potent T-cell activation. Through site-directed mutagenesis, T96F mutation (T96F-TCR) within the TCR β chain is identified, which substantially augment T cell reactivity while maintaining physiological affinity and minimizing off-target cross-reactivity. The T96F-mutated STanD-scTCR engager demonstrates improved antigen sensitivity, promotes multi-functional T-cell responses, and facilitates immune synapse formation between T cells and target cells. In a xenograft tumor model harboring the KRAS-G12V mutation, the TCR engager displays substantial tumor suppression efficacy. These findings underscore the therapeutic potential of 1-2C STanD-scTCR engage in targeting KRAS-G12V mutations in the context of HLA-A*11:01. Furthermore, the engineering strategies employ in the development of STanD-scTCR engager provide an invaluable for future designs of TCR engager drugs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".