Characterisation of gene-modified CD3-enhanced CD4+ T cells for cancer immunotherapy
Bibliographic record
Abstract
Abstract TCR gene transfer is used to redirect the antigen specificity of T lymphocytes towards known tumour antigens. TCR gene therapies in murine studies have shown promising results. However, in the clinic they often generate sub-optimal responses, when compared to treatments with tumour infiltrating lymphocytes. Previous work to improve TCR gene therapy has demonstrated that transferring additional CD3 genes increases TCR expression of both endogenous and introduced TCR in CD4+ and CD8+ T cells. In vivo experiments demonstrated that CD8+ T cells, transduced with TCR and additional CD3 were more effective in tumour protection than T cells transduced with the TCR alone. Whilst, CD4+ T cells transduced with TCR and additional CD3 initially showed improved tumour protection, lethal toxicity, unrelated to tumour burden, was later observed. To investigate the effects of CD3 overexpression, CD3 genes only (no TCR genes) were transferred into purified CD4+ and CD8+ T cells. Following adoptive transfer, CD3-enhanced CD4+ T cells survived for longer and were recovered in higher percentages in spleen, lymph nodes, bone marrow and liver, compared to CD3-enhanced CD8+ T cells and mock transduced CD4+ T cells. The same trend was also seen in competition experiments where mice received a 1:1 ratio of CD3-enhanced CD4+ T cells and mock-transduced CD4+ T cells. Interestingly, this was observed despite a twofold downregulation of TCR levels in the CD3-enhanced CD4+ T cells, compared to their pre-transfer TCR levels. Current experiments are aimed at dissecting the mechanisms responsible for, and the physiological implications of the observed TCR downregulation.
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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) | 0.002 | 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".