Ferroptosis is induced following siramesine and lapatinib treatment of breast cancer cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ferroptosis is an iron-dependent, oxidative cell death, and is distinct from apoptosis, necrosis and autophagy. In this study, we demonstrated that lysosome disrupting agent, siramesine and a tyrosine kinase inhibitor, lapatinib synergistically induced cell death and reactive oxygen species (ROS) in MDA MB 231, MCF-7, ZR-75 and SKBr3 breast cancer cells over a 24 h time course. Furthermore, the iron chelator deferoxamine (DFO) significantly reduced cytosolic ROS and cell death following treatment with siramesine and lapatinib. Furthermore, we determined that FeCl3 levels were elevated in cells treated with siramesine and lapatinib indicating an iron-dependent cell death, ferroptosis. To confirm this, we treated cells with a potent inhibitor of ferroptosis, ferrastatin-1 that effectively inhibited cell death following siramesine and lapatinib treatment. The increase levels of iron could be due to changes in iron transport. We found that 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. Knocking down of transferrin resulted in decreased cell death and ROS after treatment. In addition, ferroportin-1 (FPN) is an iron transport protein, responsible for removal of iron from cells. We found its expression is decreased after treatment with siramesine alone or in combination with lapatinib. Overexpression FPN resulted in decreased ROS and cell death whereas knockdown of FPN increased cell death after siramesine and lapatinib treatment. This indicates a novel induction of ferroptosis through altered iron regulation by treating breast cancer cells with a lysosome disruptor and a tyrosine kinase inhibitor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it