Exploiting the mevalonate pathway to distinguish statin-sensitive multiple myeloma
Bibliographic record
Abstract
Statin inhibitors, used to control hypercholesterolemia, trigger apoptosis of hematologic tumor cells and therefore have immediate potential as anticancer agents. Evaluations of statins in acute myelogenous leukemia and multiple myeloma have shown that statin efficacy is mixed, with only a subset of tumor cells being highly responsive. Our goal was to distinguish molecular features of statin-sensitive and -insensitive myeloma cells and gain insight into potential predictive markers. We show that dysregulation of the mevalonate pathway is a key determinant of sensitivity to statin-induced apoptosis in multiple myeloma. In sensitive cells, the classic feedback response to statin exposure is lost. This results in deficient up-regulation of 2 isoforms of hydroxymethylglutaryl coenzyme A reductase: the rate-limiting enzyme of the mevalonate pathway and hydroxymethylglutaryl coenzyme A synthase 1. To ascertain the clinical utility of these findings, we demonstrate that a subset of primary myeloma cells is sensitive to statins and that monitoring dysregulation of the mevalonate pathway may distinguish these cancers. We also show statins are highly effective and well tolerated in an orthotopic model of myeloma using cells harboring this dysregulation. This determinant of sensitivity further provides molecular rationale for the significant therapeutic index of statins on these tumor cells.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".