Lysosomal Destabilizing Drug Siramesine and the Dual Tyrosine Kinase Inhibitor Lapatinib Induce a Synergistic Ferroptosis through Reduced Heme Oxygenase-1 (HO-1) Levels
Bibliographic record
Abstract
Ferroptosis is an iron-dependent type of cell death distinct from apoptosis or necrosis characterized by accumulation of reactive oxygen species. The combination of siramesine, a lysosomotropic agent, and lapatinib, a dual tyrosine kinase inhibitor (TKI), synergistically induced cell death in breast cancer cells mediated by ferroptosis. In this study, we showed that this combination of siramesine and lapatinib induces synergistic cell death in glioma cell line U87 and lung adenocarcinoma cell line A549. This cell death was characterized by the increase in iron content, reactive oxygen species (ROS) production, and lipid peroxidation accumulation after 24 hours of treatment. Moreover, iron chelator DFO and ferrostatin-1, a ferroptosis inhibitor, significantly reduced cell death. The mechanism underlying the activation of the ferroptotic pathway involves lysosomal permeabilization and increase in reactive iron levels in these cells. In addition, the downregulation of heme oxygenase-1 (HO-1) protein occurred. Overexpression of HO-1 resulted in reduction of ROS and lipid peroxidation production and cell death. Furthermore, knocking down of HO-1 combined with siramesine treatment resulted in increased cell death. Finally, we found that the inhibition of the proteasome system rescued HO-1 expression levels. Our results suggest that the induction of ferroptosis by combining a lysosomotropic agent and a tyrosine kinase inhibitor is mediated by iron release from lysosomes and HO-1 degradation by the proteasome system.
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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".