Abstract 4293: Siramesine and lapatinib induce ferroptosis in glioblastoma and lung adenocarcinoma cells
Bibliographic record
Abstract
Abstract Ferroptosis, a morphologically and biochemically distinct cell death pathway, is 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. This cell death was blocked by the ferroptosis inhibitor ferrostatin-1 (Fer-1) and the iron chelator deferoxamine (DFO). The objective of the present study was to determine whether lysosome disruptors and tyrosine kinase inhibitors, in combination, induced synergic cell death via the ferroptotic pathway in additional types of cancer. U87 (glioblastoma) and A549 (lung adenocarcinoma) cells were treated with various lysosome disruptors (siramesine or desipramine) in combination with tyrosine kinase inhibitors (lapatinib or sorafenib), and the amount of cell death was measured by trypan blue exclusion. We found that these combinations synergistically induced cell death in U87 and A549 cells. To determine whether ferroptosis was the mechanism of cell death, cells were pretreated with either Fer-1 or DFO (inhibitors of ferroptosis), or with exogenous iron chloride (an inducer of ferroptosis) before treatment with the combination of siramesine and lapatinib. Pretreatment with Fer-1 or DFO decreased cell death by approximately 35%. Pretreatment with iron chloride increased the effect of the drug combination by approximately 45%. Prussian Blue staining demonstrated that there was an increase in intracellular iron accumulation following treatment with the combination of siramesine and lapatinib. Collectively, these data show that in U87 and A549 cells, the combination of lysosome disruptors and tyrosine kinase inhibitors, specifically siramesine and lapatinib, induces ferroptotic cell death. Therefore, inducing ferroptosis in tumor cells is a potential strategy for therapy in these cancers with limited treatment options. Citation Format: Anna R. Blankstein, Shumei Ma, Spencer B. Gibson. Siramesine and lapatinib induce ferroptosis in glioblastoma and lung adenocarcinoma cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4293. doi:10.1158/1538-7445.AM2017-4293
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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 | 0.001 |
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".