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Record W4311285490 · doi:10.1186/s12935-022-02822-5

The metastatic capacity of high-grade serous ovarian cancer cells changes along disease progression: inhibition by mifepristone

2022· article· en· W4311285490 on OpenAlexafffund
Sabrina J. Ritch, Abu Shadat Mohammod Noman, Alicia A. Goyeneche, Carlos M. Telleria

Bibliographic record

VenueCancer Cell International · 2022
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsMcGill University Health CentreMcGill University
FundersFaculty of Medicine, McGill UniversityMcGill University
KeywordsMifepristoneSerous fluidOvarian cancerGentamicin protection assayCancer researchExtracellular matrixMedicineCancer cellIn vitroCancerPathologyMetastasisChemistryInternal medicineBiologyCell biology

Abstract

fetched live from OpenAlex

BACKGROUND: Simplistic two-dimensional (2D) in vitro assays have long been the standard for studying the metastatic abilities of cancer cells. However, tri-dimensional (3D) organotypic models provide a more complex environment, closer to that seen in patients, and thereby provide a more accurate representation of their true capabilities. Our laboratory has previously shown that the antiprogestin and antiglucocorticoid mifepristone can reduce the growth, adhesion, migration, and invasion of various aggressive cancer cells assessed using 2D assays. In this study, we characterize the metastatic capabilities of high-grade serous ovarian cancer cells generated along disease progression, in both 2D and 3D assays, and the ability of cytostatic doses of mifepristone to inhibit them. METHODS: High-grade serous ovarian cancer cells collected from two separate patients at different stages of their disease were used throughout the study. The 2D wound healing and Boyden chamber assays were used to study migration, while a layer of extracellular matrix was added to the Boyden chamber to study invasion. A 3D organotypic model, composed of fibroblasts embedded in collagen I and topped with a monolayer of mesothelial cells was used to further study cancer cell adhesion and mesothelial displacement. All assays were studied in cells, which were originally harvested from two patients at different stages of disease progression, in the absence or presence of cytostatic doses of mifepristone. RESULTS: 2D in vitro assays demonstrated that the migration and invasive rates of the cells isolated from both patients decreased along disease progression. Conversely, in both patients, cells representing late-stage disease demonstrated a higher adhesion capacity to the 3D organotypic model than those representing an early-stage disease. This adhesive behavior is associated with the in vivo tumor capacity of the cells. Regardless of these differences in adhesive, migratory, and invasive behavior among the experimental protocols used, cytostatic doses of mifepristone were able to inhibit the adhesion, migration, and invasion rates of all cells studied, regardless of their basal capabilities over simplistic or organotypic metastatic in vitro model systems. Finally, we demonstrate that when cells acquire the capacity to grow spontaneously as spheroids, they do attach to a 3D organotypic model system when pre-incubated with conditioned media. Of relevance, mifepristone was able to cause dissociation of these multicellular structures. CONCLUSION: Differences in cellular behaviours were observed between 2 and 3D assays when studying the metastatic capabilities of high-grade serous ovarian cancer cells representing disease progression. Mifepristone inhibited these metastatic capabilities in all assays studied.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.030
GPT teacher head0.327
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2022
Admission routes2
Has abstractyes

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