Type 2 innate lymphoid cells impede IL-33-mediated tumor suppression
Bibliographic record
Abstract
Abstract Although a number of studies have recently explored the contribution of the adaptive immunity in interleukin 33 (IL-33)-mediated antitumor effects, innate immune involvement has been poorly characterized. Utilizing Rag1−/− mice (lacking T and B lymphocytes), we show here that either systemic administration of recombinant IL-33 or ectopic expression of IL-33 in melanoma cells is sufficient to inhibit tumor growth independent of adaptive antitumor immunity. We have demonstrated that IL-33-mediated antitumor effects relate to expansion and activation of NK cells. Interestingly, IL-33 also promoted the expansion of active type 2 innate lymphoid cells (ILC2s) via its receptor, ST2, which in turn inhibited NK activation and cytotoxicity. This IL-33-induced ILC2 activity coincided with greater expression of the immunosuppressive ecto-enzyme CD73. Removal of CD73 from ILC2s in culture with NK cells resulted in markedly increased activation levels in NK cells, offering a potential mechanism by which ILC2s might suppress NK cell-mediated tumor killing. Thus, our data reveal an important contribution of IL-33-induced ILC2 to tumor growth by weakening NK cell activation and tumor killing, regardless of adaptive immunity.
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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 | 0.000 |
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".