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 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 | 0.001 | 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".