T Cells Engineered With Chimeric Antigen Receptors Targeting NKG2D Ligands Display Lethal Toxicity in Mice
Bibliographic record
Abstract
Ligands for the NKG2D receptor are overexpressed on tumors, making them interesting immunotherapy targets. To assess the tumoricidal properties of T cells directed to attack NKG2D ligands, we engineered murine T cells with two distinct NKG2D-based chimeric antigen receptors (CARs): (i) a fusion between the NKG2D receptor and the CD3ζ chain and (ii) a conventional second-generation CAR, where the extracellular domain of NKG2D was fused to CD28 and CD3ζ. To enhance the CAR surface expression, we also engineered T cells to coexpress DAP10. In vitro functionality and surface expression levels of all three CARs was greater in BALB/c T cells than C57BL/6 T cells, indicating strain-specific differences. Upon adoptive transfer of NKG2D-CAR-T cells into syngeneic animals, we observed significant clinical toxicity resulting in morbidity and mortality. The severity of these toxicities varied between the CAR configurations and paralleled their in vitro NKG2D surface expression. BALB/c mice were more sensitive to these toxicities than C57BL/6 mice, consistent with the higher in vitro functionality of BALB/c T cells. Treatment with cyclophosphamide prior to adoptive transfer exacerbated the toxicity. We conclude that while NKG2D ligands may be useful targets for immunotherapy, the pursuit of NKG2D-based CAR-T cell therapies should be undertaken with caution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".