Ferroptosis and autophagy induced cell death occur independently after siramesine and lapatinib treatment in breast cancer cells
Bibliographic record
Abstract
Ferroptosis is a cell death pathway characterized by iron-dependent accumulation of reactive oxygen species (ROS) within the cell. The combination of siramesine, a lysosome disruptor, and lapatinib, a dual tyrosine kinase inhibitor, has been shown to synergistically induce cell death in breast cancer cells mediated by ferroptosis. These treatments also induce autophagy but its role in this synergistic cell death is unclear. In this study, we determined that siramesine and lapatinib initially induced ferroptosis but changes to an autophagy induced cell death after 24 hours. Furthermore, we found that intracellular iron level increased in a time dependent manner following treatment accompanied by an increase in ROS. Using the iron chelator deferoxamine (DFO) or the ROS scavenger alpha-tocopherol decreased both autophagy flux and cell death. We further discovered that decreased expression of the iron storage protein, ferritin was partially dependent upon autophagy degradation. In contrast, the expression of transferrin, which is responsible for the transport of iron into cells, is increased following treatment with lapatinib alone or in combination with siramesine. This indicates that ferroptosis and autophagy induced cell death occur independently but both are mediated by iron dependent ROS generation in breast cancer 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) | 0.001 | 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".