STAT5 Is a Key Regulator in NK Cells and Acts as a Molecular Switch from Tumor Surveillance to Tumor Promotion
Bibliographic record
Abstract
UNLABELLED: Natural killer (NK) cells are tightly regulated by the JAK-STAT signaling pathway and cannot survive in the absence of STAT5. We now report that STAT5-deficient NK cells can be rescued by overexpression of BCL2. Our experiments define STAT5 as a master regulator of NK-cell proliferation and lytic functions. Although NK cells are generally responsible for killing tumor cells, the rescued STAT5-deficient NK cells promote tumor formation by producing enhanced levels of the angiogenic factor VEGFA. The importance of VEGFA produced by NK cells was verified by experiments with a conditional knockout of VEGFA in NK cells. We show that STAT5 normally represses the transcription of VEGFA in NK cells, in both mice and humans. These findings reveal that STAT5-directed therapies may have negative effects: In addition to impairing NK-cell-mediated tumor surveillance, they may even promote tumor growth by enhancing angiogenesis. SIGNIFICANCE: The importance of the immune system in effective cancer treatment is widely recognized. We show that the new signal interceptors targeting the JAK-STAT5 pathway may have dangerous side effects that must be taken into account in clinical trials: inhibiting JAK-STAT5 has the potential to promote tumor growth by enhancing NK-cell-mediated angiogenesis.
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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) | 0.003 | 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".