Promising Therapeutic Impact of a Selective Estrogen Receptor Downregulator, Fulvestrant, as Demonstrated In Vitro upon Low-Grade Serous Ovarian Carcinoma Cell Lines
Bibliographic record
Abstract
Few studies have reported hormonal agent use in the treatment of low-grade serous ovarian carcinomas (LGSOCs), which are chemoresistant. Considering the need for novel effective therapies, we investigated the hormone receptor expression and hormonal inhibition efficacy in LGSOCs. Using immunohistochemistry, we assessed the estrogen receptor (ER) expression status in 33 cases of histologically confirmed serous ovarian tumors, including 10, 11, and 12 cases of LGSOCs, serous borderline tumors (SBTs), and serous cystadenomas (SCAs), respectively. The genetic background reported in our previous study was used in the current study. MPSC1 cells, which were established from LGSOCs, were used in cell proliferation assays. We observed a higher ER expression in LGSOCs and SBTs than in SCAs (70%, 81%, and 50%, respectively). Thus, LGSOCs and SBTs exhibit higher ER expression than SCAs. Moreover, the PIK3CA mutation positively correlated with ER expression in LGSOCs (p = 0.0113). MPSC1 cells showed low ER expression on Western blotting. MPSC1 cell proliferation was significantly inhibited by fulvestrant (a selective ER downregulator). The activation of ER and PI3K/AKT signaling pathways may play an important role in LGSOC carcinogenesis. ER downregulation with fulvestrant or combination therapy with PI3K inhibitors is a possible novel treatment for patients with LGSOCs.
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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".