LY75 Suppression in Mesenchymal Epithelial Ovarian Cancer Cells Generates a Stable Hybrid EOC Cellular Phenotype, Associated with Enhanced Tumor Initiation, Spreading and Resistance to Treatment in Orthotopic Xenograft Mouse Model
Bibliographic record
Abstract
The implications of the epithelial–mesenchymal transition (EMT) mechanisms in the initiation and progression of epithelial ovarian cancer (EOC) remain poorly understood. We have previously shown that suppression of the antigen receptor LY75 directs mesenchymal–epithelial transition (MET) in EOC cell lines with the mesenchymal phenotype, associated with the loss of Wnt/β-catenin signaling activity. In the present study, we used the LY75-mediated modulation of EMT in EOC cells as a model in order to investigate in vivo the specific role of EOC cells, with an epithelial (E), mesenchymal (M) or mixed epithelial plus mesenchymal (E+M) phenotype, in EOC initiation, dissemination and treatment response, following intra-bursal (IB) injections of SKOV3-M (control), SKOV3-E (Ly75KD) and a mixed population of SKOV3-E+M cells, into severe combined immunodeficiency (SCID) mice. We found that the IB-injected SKOV3-E cells displayed considerably higher metastatic potential and resistance to treatment as compared to the SKOV3-M cells, due to the acquisition of a Ly75KD-mediated hybrid phenotype and stemness characteristics. We also confirmed in vivo that the LY75 depletion directs suppression of the Wnt/β-catenin pathway in EOC cells, suggestive of a protective role of this pathway in EOC etiology. Moreover, our data raise concerns regarding the use of LY75-targeted vaccines for dendritic-cell EOC immunotherapy, due to the possible occurrence of undesirable side effects.
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 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 | 0.001 |
| 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".