PIM Kinase Inhibitors Downregulate STAT3Tyr705 Phosphorylation
Bibliographic record
Abstract
Using a cell-based high-throughput screen designed to detect small chemical compounds that inhibit cell growth and survival, we identified three structurally related compounds, 21A8, 21H7, and 65D4, with differential activity on cancer versus normal cells. Introduction of structural modifications yielded compound M-110, which inhibits the proliferation of prostate cancer cell lines with IC(50)s of 0.6 to 0.9 μmol/L, with no activity on normal human peripheral blood mononuclear cells up to 40 μmol/L. Screening of 261 recombinant kinases and subsequent analysis revealed that M-110 is a selective inhibitor of the PIM kinase family, with preference for PIM-3. The prostate cancer cell line DU-145 and the pancreatic cancer cell line MiaPaCa2 constitutively express activated STAT3 (pSTAT3(Tyr705)). Treatment of DU-145 cells with M-110 or with a structurally unrelated PIM inhibitor, SGI-1776, significantly reduces pSTAT3(Tyr705) expression without affecting the expression of STAT3. Furthermore, treatment of DU-145 cells with M-110 attenuates the interleukin-6-induced increase in pSTAT3(Tyr705). To determine which of the three PIM kinases is most likely to inhibit expression of pSTAT3(Tyr705), we used PIM-1-, PIM-2-, or PIM-3-specific siRNA and showed that knockdown of PIM-3, but not of PIM-1 or PIM-2, in DU-145 cells results in a significant downregulation of pSTAT3(Tyr705). The phosphorylation of STAT5 on Tyr694 in 22Rv1 cells is not affected by M-110 or SGI-1776, suggesting specificity for pSTAT3(Tyr705). These results identify a novel role for PIM-3 kinase as a positive regulator of STAT3 signaling and suggest that PIM-3 inhibitors cause growth inhibition of cancer cells by downregulating the expression of pSTAT3(Tyr705).
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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) | 0.001 | 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 | 0.001 |
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".