Effect of statins on IL-10 signaling and production by chronic lymphocytic leukemia cells
Bibliographic record
Abstract
The cytokine-signaling inhibitor ruxolitinib causes disease flares of chronic lymphocytic leukemia (CLL). This tumor-promoting activity correlates with its ability to inhibit interleukin (IL)-10 production by CLL B cells that have been activated with IL-2 and the Toll-like receptor 7 (TLR7) agonist resiquimod (called 2S cells) in vitro. In TLR-activated normal human B cells, IL-10 production is regulated by cholesterol biosynthesis and can be inhibited by statins. The goal of this study was to determine if statins affect IL-10 production by 2S cells. Lipophilic statins decreased IL-10 production from 2S CLL cells by inhibiting activation of MYC along with secondary signaling events that amplify and maintain IL10 transcription. IL-10 production was restored when the prenylation defect imposed by statins was corrected by adding geranylgeranyl pyrophosphate. CLL cells activated by ruxolitinib in vivo were enriched with genes associated with prenylation inhibition in TLR-activated human B cells in vitro. These findings suggest IL-10 production by 2S-activated CLL cells is regulated by cholesterol biosynthesis in part through geranylgeranyl pyrophosphate production and substrate prenylation. If IL-10 production in the 2S model constitutes a surrogate test for drug responses in vivo, realization of the potential clinical benefits of statins in CLL may require coadministration of other agents.
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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".