Mevalonate Pathway Inhibition Slows Breast Cancer Metastasis via Reduced <i>N</i>-glycosylation Abundance and Branching
Bibliographic record
Abstract
Abstract Aberrant N-glycan Golgi remodeling and metabolism are associated with epithelial–mesenchymal transition (EMT) and metastasis in patients with breast cancer. Despite this association, the N-glycosylation pathway has not been successfully targeted in cancer. Here, we show that inhibition of the mevalonate pathway with fluvastatin, a clinically approved drug, reduces both N-glycosylation and N-glycan-branching, essential components of the EMT program and tumor metastasis. This indicates novel cross-talk between N-glycosylation at the endoplasmic reticulum (ER) and N-glycan remodeling at the Golgi. Consistent with this cooperative model between the two spatially separated levels of protein N-glycosylation, fluvastatin-induced tumor cell death was enhanced by loss of Golgi-associated N-acetylglucosaminyltransferases MGAT1 or MGAT5. In a mouse model of postsurgical metastatic breast cancer, adjuvant fluvastatin treatment reduced metastatic burden and improved overall survival. Collectively, these data support the immediate repurposing of fluvastatin as an adjuvant therapeutic to combat metastatic recurrence in breast cancer by targeting protein N-glycosylation at both the ER and Golgi. Significance: These findings show that metastatic breast cancer cells depend on the fluvastatin-sensitive mevalonate pathway to support protein N-glycosylation, warranting immediate clinical testing of fluvastatin as an adjuvant therapy for breast cancer.
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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".