CD5 blockade, a novel immune checkpoint inhibitor, enhances T cell anti-tumour immunity and delays tumour growth in mice harbouring poorly immunogenic 4T1 breast tumour homografts
Bibliographic record
Abstract
CD5 is a member of the scavenger receptor cysteine-rich superfamily that is expressed on T cells and a subset of B cells (B1a) cell and can regulate the T cell receptor signaling pathway. Blocking CD5 function may have therapeutic potential in treatment of cancer by enhancing cytotoxic T lymphocyte recognition and ablation of tumour cells. The effect of administering an anti-CD5 antibody to block or reduce CD5 function as an immune checkpoint blockade to enhance T cell anti-tumour activation and function in vivo has not been explored. Here we challenged mice with poorly immunogenic 4T1 breast tumour cells and tested whether treatment with anti-CD5 monoclonal antibodies (MAb) in vivo could enhance non-malignant T cell anti-tumour immunity and reduce tumour growth. Treatment with anti-CD5 MAb resulted in an increased fraction of CD8+ T cells compared to CD4+ T cell in draining lymph nodes and the tumour microenvironment. In addition, it increased activation and effector function of T cells isolated from spleens, draining lymph nodes, and 4T1 tumours. Furthermore, tumour growth was delayed in mice treated with anti-CD5 MAb. These data suggest that use of anti-CD5 MAb as an immune checkpoint blockade can both enhance activation of T cells in response to poorly immunogenic antigens and reduce tumour growth in vivo. Exploration of anti-CD5 therapies in treatment of cancer, alone and in combination with other immune therapeutic drugs, is warranted.
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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".