A T cell redirection platform for co-targeting dual antigens on solid tumors
Bibliographic record
Abstract
In order to direct T cells to specific features of solid cancer cells, we engineered a bispecific antibody format, named Dual Antigen T cell Engager (DATE), by fusing a single-chain variable fragment targeting CD3 to a tumor-targeting antigen-binding fragment. In this format, multiple novel paratopes against different tumor antigens were able to recruit T-cell cytotoxicity to tumor cells in vitro and in an in vivo pancreatic ductal adenocarcinoma xenograft model. Since unique surface antigens in solid tumors are limited, in order to enhance selectivity, we further engineered “double-DATEs” targeting two tumor antigens simultaneously. The double-DATE contains an additional autonomous variable heavy-chain domain, which binds a second tumor antigen without itself eliciting a cytotoxic response. This novel modality provides a strategy to enhance the selectivity of immune redirection through binary targeting of native tumor antigens. The modularity and use of a common, stable human framework for all components enables a pipeline approach to rapidly develop a broad repertoire of tailored DATEs and double-DATEs with favorable biophysical properties and high potencies and selectivities.
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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".