Synthetic dual co-stimulation increases the potency of HIT and TCR-targeted cell therapies
Bibliographic record
Abstract
Chimeric antigen receptor T cells have dramatically improved the treatment of hematologic malignancies. T cell antigen receptor (TCR)-based cell therapies are yet to achieve comparable outcomes. Importantly, chimeric antigen receptors not only target selected antigens but also reprogram T cell functions through the co-stimulatory pathways that they engage upon antigen recognition. We show here that a fusion receptor comprising the CD80 ectodomain and the 4-1BB cytoplasmic domain, termed 80BB, acts as both a ligand and a receptor to engage the CD28 and 4-1BB pathways, thereby increasing the antitumor potency of human leukocyte antigen-independent TCR (HIT) receptor- or TCR-engineered T cells and tumor-infiltrating lymphocytes. Furthermore, 80BB serves as a switch receptor that provides agonistic 4-1BB co-stimulation upon its ligation by the inhibitory CTLA4 molecule. By combining multiple co-stimulatory features in a single antigen-agnostic synthetic receptor, 80BB is a promising tool to sustain CD3-dependent T cell responses in a wide range of targeted immunotherapies. Dobrin et al. develop a fusion receptor comprising the CD80 ectodomain and the 4-1BB cytoplasmic domain, which engages the CD28 and 4-1BB pathways and increases the antitumor potency of HLA-independent (HIT) and TCR-engineered T cells.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".