Comprehensive Functional Self-Antigen Screening to Assess Cross-Reactivity in a Promiscuous Engineered T-cell Receptor
Bibliographic record
Abstract
Abstract T cell receptor therapeutics are an emerging modality of biologic and cell-based medicines with the unique ability to target intracellular antigens and finely discriminate between healthy and infected or mutated cells. An obstacle to the development of new T cell receptor therapeutics is the difficulty in engineering these proteins for enhanced therapeutic efficacy while avoiding introduction of unexpected off-target autoreactivity. In this study, we apply a functional high-throughput screening assay, Tope-seq, to detecting cross-reactive epitopes in libraries of >5 x 10 5 unique peptide-coding sequences. We retrospectively analyze an affinity-enhanced engineered T cell receptor, which previously failed clinical trials due to severe off-target toxicity caused by epitope cross-reactivity, by comprehensive functional testing against all genome-coded self-antigens. Using the Tope-seq methodology, we were able to identify the epitope mediating off-target reactivity at a significance threshold of p < 0.01 in first-pass bulk screening. We also identified other potential cross-reactive epitopes of the engineered TCR of-interest, suggesting that the need for assessing promiscuity in TCR based therapeutics is larger than previously appreciated.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".