Activation of lysosomal iron triggers ferroptosis in cancer
Bibliographic record
Abstract
Iron catalyses the oxidation of lipids in biological membranes and promotes a form of cell death called ferroptosis1. Defining where this chemistry occurs in the cell can inform the design of drugs capable of inducing or inhibiting ferroptosis in various disease-relevant settings. Genetic approaches have revealed suppressors of ferroptosis2–4; by contrast, small molecules can provide spatiotemporal control of the chemistry at work5. Here we show that the ferroptosis inhibitor liproxstatin-1 exerts cytoprotective effects by inactivating iron in lysosomes. We also show that the ferroptosis inducer RSL3 initiates membrane lipid oxidation in lysosomes. We designed a small-molecule activator of lysosomal iron—fentomycin-1—to induce the oxidative degradation of phospholipids and ultimately ferroptosis. Fentomycin-1 is able to kill iron-rich CD44high primary sarcoma and pancreatic ductal adenocarcinoma cells, which can promote metastasis and fuel drug tolerance. In such cells, iron regulates cell adaptation6,7 while conferring vulnerability to ferroptosis8,9. Sarcoma cells exposed to sublethal doses of fentomycin-1 acquire a ferroptosis-resistant cell state characterized by the downregulation of mesenchymal markers and the activation of a membrane-damage response. This phospholipid degrader can eradicate drug-tolerant persister cancer cells in vitro and reduces intranodal tumour growth in a mouse model of breast cancer metastasis. Together, these results show that control of iron reactivity confers therapeutic benefits, establish lysosomal iron as a druggable target and highlight the value of targeting cell states10. Some cancer cells exhibit high loads of reactive iron in lysosomes, and this feature is exploited by using fentomycin-1, a newly developed small molecule, to induce ferroptosis.
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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".