Mifepristone inhibits non-small cell lung carcinoma cellular escape from DNA damaging cisplatin
Bibliographic record
Abstract
BACKGROUND: Lung cancer is the leading cause of cancer deaths in the world. The major histopathological subtype of lung cancer is non-small cell lung cancer (NSCLC). Platinum-based therapy is the standard of care for patients with advanced stage NSCLC. However, even with treatment, most patients will die of this disease within 5 years and most of these deaths are due to recurrence. One strategy to inhibit recurrence is to use cytostatic compounds following courses of lethal chemotherapy. We have shown in various cancer cell types that mifepristone (MF), an anti-progestin/anti-glucocorticoid, is a powerful cytostatic anti-cancer agent. Thus, in this work we tested the hypothesis that MF should be efficacious in inducing cytostasis and preventing repopulation of NSCLC following cisplatin (CDDP) therapy. METHODS: We established an in vitro approach wherein human NSCLC cells with different genetic backgrounds and sensitivities to CDDP (A549 and H23) were exposed to rounds of lethal concentrations of CDDP for 1 h followed or not by MF monotherapy. Every 2 days, cell number, cell viability, and colony-forming ability of viable cells were studied. RESULTS: CDDP killed the majority of cells, yet there were remnant cells escaping CDDP lethality and repopulating the culture, as evidenced by the improved clonogenic survival of viable cells. In contrast, when cells exposed to CDDP where further treated with MF following CDDP removal, their number and clonogenic capacity were reduced drastically. CONCLUSION: This study reports that there is repopulation of NSCLC cells following a lethal concentration of CDDP monotherapy, that NSCLC cells are sensitive to the growth inhibition properties of MF, and that MF abrogates the repopulation of NSCLC cells following CDDP therapy. Our study supports further evaluating MF as an adjuvant therapy for NSCLC.
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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".