Paradoxical activation of chronic lymphocytic leukemia cells by ruxolitinib in vitro and in vivo
Bibliographic record
Abstract
Introduction Chronic lymphocytic leukemia (CLL) is characterized by an aberrant cytokine network that can support tumor growth by triggering janus kinase (JAK)/STAT pathways. Targeting cytokine-signaling should then be a rational therapeutic strategy but the JAK inhibitor ruxolitinib failed to control and seemingly accelerated the disease in clinical trials. Methods The effect of ruxolitinib on primary human CLL cells was studied in vitro and in vivo. Results Ruxolitinib increased phosphorylation of IRAK4, an important toll-like receptor (TLR)- signaling intermediate, in circulating CLL cells in vitro. It also enhanced p38 and NFKB1 phosphorylation while lowering STAT3 phosphorylation in CLL cells activated with TLR-7/8 agonists and IL-2. Among the cytokines made by activated CLL cells, high levels of IL-10 contributed strongly to STAT3 phosphorylation and inhibited TLR7 activity. Ruxolitinib limited TLR-mediated IL10 transcription and markedly reduced IL-10 production in vitro. It also decreased blood levels of IL-10 while increasing TNFα along with phospho-p38 expression and gene sets associated with TLR-activation in CLL cells in vivo. The bruton's tyrosine kinase inhibitor ibrutinib decreased IL-10 production in vitro but, in contrast to ruxolitinib, blocked initial IL10 transcription induced by TLR-signaling in vitro, decreased TNFα production, and deactivates CLL cells in vivo. Discussion These findings suggest the possible benefits of inhibiting growth factors with JAK inhibitors in CLL are outweighed by negative effects on potential tumor suppressors such as IL-10 that allow unrestrained activation of NFκB by drivers such as TLRs. Specific inhibition of growth-promoting cytokines with blocking antibodies or infusing suppressive cytokines like IL-10 might be better strategies to manipulate cytokines in CLL.
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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) | 0.004 | 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".