39 Lysosome disruptor’s siramesine and the kinase inhibitor lapatinib induce synergistic cell death through ferroptosis in glioma cells
Bibliographic record
Abstract
Glioblastoma is inherently resistant to radiation and drug treatments. This is mediated by the most common forms of cell death are often actively inhibited. Identifying and exploiting alternative cell death pathways are essential to overcoming or bypassing drug resistance. Ferroptosis, a newly described, morphologically and biochemically distinct, cell death mechanism is characterized by iron-dependent cellular accumulation of reactive oxygen species. The combination of siramesine, a lysosome disruptor, and lapatinib, a dual tyrosine kinase inhibitor (TKI), synergistically induced death in glioma cancer cells. This cell death had characteristics of ferroptosis: it was blocked by the ferroptosis inhibitor ferrostatin-1 and the iron chelator deferoxamine. In addition, the amount of ROS and lipid peroxidation were increased in glioma cells. Iron transport protein remained unchanged but reactive iron levels increased. One target for kinase inhibitors is protein bisulfate isomerase (PDI). Knockdown of PDI in combination with siramesine increased cell death that was blocked by ferrostatin-1. Taken together, drug combinations that alter reactive iron and ROS levels might induce ferroptosis and overcome drug resistance in glioma 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) | 0.000 | 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) | 0.002 | 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".