Antibody-targeted T cells and natural killer cells for cancer immunotherapy
Bibliographic record
Abstract
BACKGROUND: Adoptive cell cancer therapies aim to re-engineer a patient's immune cells to mount an anti-cancer response. Chimeric antigen receptor T and natural killer cells have been engineered and proved successful in treating some cancers; however, the genetic methods for engineering are laborious, expensive, and inefficient and can cause severe toxicities when they over-proliferate. RESULTS: We examined whether the cell-killing capacity of activated T and NK cells could be targeted to cancer cells by anchoring antibodies to their cell surface. Using metabolic glycoengineering to introduce azide moieties to the cellular surface, we covalently attached a dibenzocyclooctyne-modified antibody using the strain-promoted alkyne azide cycloaddition reaction, creating antibody-conjugated T and NK cells. We targeted the immune cells to tumors possessing the xenoantigen, N-glycolyl neuraminic acid GM3 ganglioside, using the 14F7hT antibody. These activated T and NK cells are "armed" with tumour-homing capabilities that specifically lyses antigen-positive cancer cells without off-target toxicities. Moreover, when exposed to target cells, 14F7hT-conjugated T cells that are not preactivated exhibit increased perforin, granzyme, CD69, and CD25 expression and specific cell killing. CONCLUSIONS: This research shows the potential for a non-genetic method for redirecting cytotoxic immune cells as a feasible and effective approach for tumor-targeted cell immunotherapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".