Novel glycolipid agents for killing cisplatin-resistant human epithelial ovarian cancer cells
Bibliographic record
Abstract
BACKGROUND: Chemotherapy resistance is one of the major factors contributing to mortality from human epithelial ovarian cancer (EOC). Identifying drugs that can effectively kill chemotherapy-resistant EOC cells would be a major advance in reducing mortality. Glycosylated antitumour ether lipids (GAELs) are synthetic glycolipids that are cytotoxic to a wide range of cancer cells. They appear to induce cancer cell death in an apoptosis-independent manner. METHODS: Herein, the effectiveness of two GAELs, GLN and MO-101, in killing chemotherapy-sensitive and -resistant EOC cells lines and primary cell samples was tested using monolayer, non-adherent aggregate, and non-adherent spheroid cultures. RESULTS: Our results show that EOC cells exhibit a differential sensitivity to the GAELs. Strikingly, both GAELs are capable of inducing EOC cell death in chemotherapy-sensitive and -resistant cells grown as monolayer or non-adherent cultures. Mechanistic studies provide evidence that apoptotic-cell death (caspase activation) contributes to, but is not completely responsible for, GAEL-induced cell killing in the A2780-cp EOC cell line, but not primary EOC cell samples. CONCLUSIONS: Studies using primary EOC cell samples supports previously published work showing a GAEL-induced caspase-independent mechanism of death. GAELs hold promise for development as novel compounds to combat EOC mortality due to chemotherapy resistance.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".