Methylseleninic Acid Sensitizes Notch3-Activated OVCA429 Ovarian Cancer Cells to Carboplatin
Bibliographic record
Abstract
Ovarian cancer, the deadliest of gynecologic cancers, is usually not diagnosed until advanced stages. Although carboplatin has been popular for treating ovarian cancer for decades, patients eventually develop resistance to this platinum-containing drug. Expression of neurogenic locus notch homolog 3 (Notch3) is associated with chemoresistance and poor overall survival in ovarian cancer patients. Overexpression of NICD3 (the constitutively active form of Notch3) in OVCA429 ovarian cancer cells (OVCA429/NICD3) renders them resistance to carboplatin treatment compared to OVCA429/pCEG cells expressing an empty vector. We have previously shown that methylseleninic acid (MSeA) induces oxidative stress and activates ataxia-telangiectasia mutated and DNA-dependent protein kinase in cancer cells. Here we tested the hypothesis that MSeA and carboplatin exerted a synthetic lethal effect on OVCA429/NICD3 cells. Co-treatment with MSeA synergistically sensitized OVCA429/NICD3 but not OVCA429/pCEG cells to the killing by carboplatin. This synergism was associated with a cell cycle exit at the G2/M phase and the induction of NICD3 target gene HES1. Treatment of N-acetyl cysteine or inhibitors of the above two kinases did not directly impact on the synergism in OVCA429/NICD3 cells. Taken together, these results suggest that the efficacy of carboplatin in the treatment of high grade ovarian carcinoma can be enhanced by a combinational therapy with MSeA.
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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) | 0.001 | 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".