Statins Enhance the Molecular Response in Chronic Myeloid Leukemia when Combined with Tyrosine Kinase Inhibitors
Bibliographic record
Abstract
Previous studies have suggested that statins can be repurposed for cancer treatment. However, the therapeutic efficacy of statins in chronic myeloid leukemia (CML) has not yet been demonstrated. In this study, we retrospectively evaluated the outcomes of 408 CML patients who underwent imatinib therapy. The deep molecular response rates in patients treated with the statin/TKI combination were significantly higher than those in patients treated with TKI alone (p = 0.0016). The statin/TKI combination exerted potent cytotoxic effects against wild-type and ABL1 mutant CML, BaF3, and K562/T315I mutant cells. Furthermore, the statin/TKI combination additively inhibited the colony-forming capacity of murine CML-KLS+ cells in vitro. In addition, we examined the additive growth-inhibitory effects of the statin/tyrosine kinase inhibitor (TKI) combination against CML patient-derived CD34+ cells. The growth-inhibitory effects of the statin/imatinib combination against CD34+/CML primary cells were higher than those against CD34+/Norm cells (p = 0.005), suggesting that the combination of rosuvastatin and imatinib exerted growth-inhibitory effects against CML CD34+ cells, but not against normal CD34+ cells. Furthermore, results from RNA sequencing of control and statin-treated cells suggested that statins inhibited c-Myc-mediated and hematopoietic cell differentiation pathways. Thus, statins can be potentially repurposed to improve treatment outcomes in CML patients when combined with TKI therapy.
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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 | 0.001 |
| 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".