Glycolipid stimulation of invariant NKT cells mobilizes precursors of mature NK cells and potentiates their participation in immune surveillance against metastatic cancer.
Bibliographic record
Abstract
Abstract Precursors of mature natural killer (pre-mNK) cells, initially called IFN-producing killer dendritic cells, have been recently characterized as a novel intermediate in NK cell differentiation. Typified by a unique B220+NK1.1+CD11c+MHCII+ phenotype, pre-mNK cells exhibit prolific anti-tumor cytotoxicity while retaining the ability to present antigens thereby activating neighbouring T cells. Invariant NKT (iNKT) cells are another population highlighted for their potential in anti-cancer immunity. They are activated very early and play an important role in transactivating downstream effectors, such as NK cells. The extent to which iNKT cells can activate pre-mNK cells is unknown. We hypothesized that iNKT cells are able to activate pre-mNK cells and potentiate their anti-tumor properties. Wildtype C57BL/6 mice were injected i.p. with the iNKT cell superagonist, α-galactosylceramide (αGC), and various lymphoid tissues were harvested after 5 days for phenotyping or cytotoxicity assays. We have shown, for the first time that iNKT cell activation via αGC results in robust expansion of pre-mNK cells in the spleen, lung and most notably, in the liver. These cells exhibited cytotoxic activity against YAC-1 thymoma and B16 melanoma cancer cells via the granule exocytosis pathway and significantly contributed to overall NK cytotoxic activity in vivo. The anti-cancer responses due to αGC activation have been well documented; however, the contribution of pre-mNK cells in these responses have never been shown. Our findings demonstrate that pre-mNK cells rapidly expand due to iNKT activation while retaining their capacity to kill tumor targets. This may suggest a novel mechanism of targeting pre-mNK cells in anti-cancer therapies.
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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 | 0.000 |
| 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".