<scp>NKG</scp>2<scp>D</scp> regulates production of soluble <scp>TRAIL</scp> by ex vivo expanded human γδ <scp>T</scp> cells
Bibliographic record
Abstract
Soluble TRAIL (sTRAIL) can be produced by myeloid-derived cells to kill cancer cells. Whether this mechanism is used by T cells, and if so, how sTRAIL production is regulated, remains unclear. Our previous studies showed that ex vivo expanded human γδ T cells express TRAIL and NK receptor group 2 (R2), member D (NKG2D), and possess potent anticancer activities both in vitro and in vivo. Here, we investigated in greater detail the mechanisms by which γδ T cells utilize TRAIL and NKG2D to kill lung cancer cells. We demonstrate that human lung cancer cells express TRAIL R2 and NKG2D ligands. Blocking TRAIL or NKG2D during γδ T-cell-lung cancer cell co-cultures significantly reduced γδ T-cell-mediated cytotoxicity. Cross-linking NKG2D with anti-NKG2D antibody to mimic ligand binding promoted γδ T cells to produce sTRAIL, which induced apoptosis in lung cancer cells through TRAIL R2. Either neutralizing sTRAIL or blocking lung cancer cell TRAIL R2 significantly reduced γδ T-cell-mediated cytotoxicity to lung cancer cells. This study demonstrates that γδ T cells can mediate anticancer immunity via NKG2D-regulated production of sTRAIL.
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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".