Prostate Cancer Cells Are Sensitive to Lysosomotropic Agent Siramesine through Generation Reactive Oxygen Species and in Combination with Tyrosine Kinase Inhibitors
Bibliographic record
Abstract
BACKGROUND: Prostate cancer is the most common cancer affecting men often resulting in aggressive tumors with poor prognosis. Even with new treatment strategies, drug resistance often occurs in advanced prostate cancers. The use of lysosomotropic agents offers a new treatment possibility since they disrupt lysosomal membranes and can trigger a series of events leading to cell death. In addition, combining lysosomotropic agents with targeted inhibitors can induce increased cell death in different cancer types, but prostate cancer cells have not been investigated. METHODS: We treated prostate cancer cells with lysosomotropic agents and determine their cytotoxicity, lysosome membrane permeabilization (LMP), reactive oxygen species (ROS) levels, and mitochondrial dysfunction. In addition, we treated cells with lysosomotropic agent in combination with tyrosine kinase inhibitor, lapatinib, and determined cell death, and the role of ROS in this cell death. RESULTS: Herein, we found that siramesine was the most effective lysosomotropic agent at inducing LMP, increasing ROS, and inducing cell death in three different prostate cancer cell lines. Siramesine was also effective at increasing cell death in combination with the tyrosine kinase inhibitor, lapatinib. This increase in cell death was mediated by lysosome membrane permeabilization, an increased in ROS levels, loss of mitochondrial membrane potential and increase in mitochondrial ROS levels. The combination of siramesine and lapatinib induced apoptosis, cleavage of PARP and decreased expression of Bcl-2 family member Mcl-1. Furthermore, lipid peroxidation occurred with siramesine treatment alone or in combination with lapatinib. Treating cells with the lipid peroxidation inhibitor alpha-tocopherol resulted in reduced siramesine induced cell death alone or in combination with lapatinib. The combination of siramesine and lapatinib failed to increase cell death responses in normal prostate epithelial cells. CONCLUSIONS: This suggests that lysomotropic agents such as siramesine in combination with tyrosine kinase inhibitors induces cell death mediated by ROS and could be an effective treatment strategy in advanced prostate cancer.
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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 | 0.001 |
| 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".