Chimeric CD3ζ chains containing CD28 signalling motifs enhance antigen-specific IL-2 production and expansion of human TCR-engineered T cells <i>in vitro</i>
Bibliographic record
Abstract
Abstract Gene-engineered T-cell products have been developed for immunotherapy to treat cancers, with great success observed in haematological malignancies but limited efficacy in treating solid cancers. TCR-engineered T cells utilize transferred TCRs targeting tumour-associated and cancer-specific peptides presented by MHC molecules. The CD3ζ chains are part of the TCR-CD3 complex expressed by T cells and mediate signal transduction when the TCR binds to MHC-presented peptides. In this study, we explored whether co-stimulation domains, that were effective in improving the function of T cells engineered with chimeric antigen receptors (CARs), can be exploited to improve the functionality of TCR-engineered T cells. We inserted the signalling domains of CD28 or 4–1BB at the membrane proximal or the membrane distal position of the intracellular tail of CD3ζ and engineered human T cells to express a specific TCR in combination with either modified CD3ζ or unmodified control. Antigen-specific in vitro stimulation assays revealed that T cells expressing CD3ζ constructs with CD28 signal domains displayed enhanced peptide-specific IL-2 production and, following repeated antigen stimulation, expanded to substantially greater numbers than T cells expressing unmodified CD3ζ. Importantly, greater expansion seen with the CD28-containing ζ did not result in any reduction of effector function as assessed by peptide-specific cytotoxicity and cytokine production. The data indicate that modification of the CD3ζ chain with a CD28 signal motif provides an opportunity to improve antigen-specific expansion and effector function of TCR-engineered T cells by combining signal 1 and co-stimulatory signal 2 in one molecular TCR-CD3 complex.
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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 | 0.001 |
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".