IL-33 increases ST2<sup>+</sup>Tregs and promotes metastatic tumour growth in the lungs in an amphiregulin-dependent manner
Bibliographic record
Abstract
Regulatory T cells (Tregs) facilitate primary and metastatic tumour growth through the suppression of anti-tumour immunity. Emerging evidence suggests a distinct role for Tregs in mediating tissue repair and barrier integrity in the lungs by IL-33 mediated production of the growth factor amphiregulin (AREG). Dependent on the type of cancer and local microenvironment, AREG may induce tumour cell proliferation, invasion, migration or resistance to apoptosis by signaling through the epidermal growth factor receptor (EGFR). We have found that IL-33 is dramatically increased in and around metastatic tumour foci in the lungs of mice bearing orthotopic murine mammary tumours. We observed that Tregs express significantly more of the IL-33 receptor, ST2, relative to conventional T cells, that ST2+ Tregs accumulate in the lungs of metastatic tumour-bearing mice, and that ST2+ Tregs produce significantly more AREG than ST2− Tregs. The intranasal administration of recombinant IL-33 increased the proportion of AREG producing ST2+ Tregs and enhanced the level of phosphorylated EGFR in the metastatic lungs. While recombinant AREG did not impact mammary tumour cell proliferation in vitro despite inducing a dose-dependent increase in phosphorylated EGFR, intranasal administration of AREG resulted in a ten-fold increase in pulmonary metastatic tumour burden in vivo. Further, the intranasal administration of recombinant IL-33 significantly increased metastatic tumour burden in the lungs in an amphiregulin-dependent manner. These data identify ST2+ Tregs as a microenvironmental source of AREG in the lungs of mice with orthotopic metastatic mammary tumours and highlight an important role for AREG in promoting metastatic tumour growth in the lungs.
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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) | 0.001 | 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".