Advanced assessment of migration and invasion of cancer cells in response to mifepristone therapy using double fluorescence cytochemical labeling
Bibliographic record
Abstract
BACKGROUND: Previous work in our laboratory demonstrated that antiprogestin mifepristone impairs the growth and adhesion of highly metastatic cancer cells, and causes changes in their cellular morphology. In this study, we further assess the anti-metastatic properties of mifepristone, by studying whether cytostatic doses of the drug can inhibit the migration and invasion of various cancer cell lines using a double fluorescence cytochemical labeling approach. METHODS: Cell lines representing cancers of the ovary (SKOV-3), breast (MDA-MB-231), glia (U87MG), or prostate (LNCaP) were treated with cytostatic concentrations of mifepristone. Wound healing and Boyden chamber assays were utilized to study cellular migration. To study cellular invasion, the Boyden chamber assay was prepared by adding a layer of extracellular matrix over the polycarbonate membrane. We enhanced the assays with the addition of double fluorescence cytochemical staining for fibrillar actin (F-actin) and DNA to observe the patterns of cytoskeletal distribution and nuclear positioning while cells migrate and invade. RESULTS: When exposed to cytostatic concentrations of mifepristone, all cancer cells lines demonstrated a decrease in both migration and invasion capacities measured using standard approaches. Double fluorescence cytochemical labeling validated that mifepristone-treated cancer cells exhibit reduced migration and invasion, and allowed to unveil a distinct migration pattern among the different cell lines, different arrays of nuclear localization during migration, and apparent redistribution of F-actin to the nucleus. CONCLUSION: This study reports that antiprogestin mifepristone inhibits migration and invasion of highly metastatic cancer cell lines, and that double fluorescence cytochemical labeling increases the value of well-known approaches to study cell movement.
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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 | 0.000 | 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) | 0.000 | 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".