ERα-Targeted Therapy in Ovarian Cancer Cells by a Novel Estradiol-Platinum(II) Hybrid
Bibliographic record
Abstract
As we previously showed, we have synthesized a new family of 17β-estradiol-platinum(II) hybrids. Earlier studies revealed the VP-128 hybrid to show high efficiency compared with cisplatin toward hormone-dependent breast cancer cells. In the present research, we have studied the antitumor activity of VP-128 in vitro and in vivo against ovarian cancer. In nude mice with ovarian xenografts, VP-128 displayed selective activity toward hormone-dependent tumors and showed higher efficiency than cisplatin to inhibit tumor growth. Similarly, in vitro, transient transfection of estrogen receptor (ER)-α in ERα-negative A2780 cells increased their sensitivity to VP-128-induced apoptosis, confirming the selectivity of VP-128 toward hormone-dependent tumor cells. In agreement, Western blot analysis revealed that VP-128 induced higher caspase-9, caspase-3, and poly (ADP-ribose) polymerase cleavage compared with cisplatin. The activation of caspase-independent apoptosis was also observed in ERα-negative A2780 cells, in which VP-128 rapidly induced the translocation of apoptosis-inducing factor to the nucleus. Conversely, subcellular localization of apoptosis-inducing factor was not modified in ERα-positive Ovcar-3 cells. We also discovered that VP-128 induces autophagy in ovarian cancer cells because of the formation of acidic vesicular organelles (AVOs) and increase of Light Chain 3B-II protein responsible for the formation of autophagosomes; pathways related to autophagy (AKT and mammalian target of rapamycin) were also down-regulated, supporting this mechanism. Finally, the inhibition of autophagy using chloroquine increased VP-128 efficiency, indicating a possible combination therapy. Altogether these results highlight the beneficial value of VP-128 for the treatment of hormone-dependent ovarian cancers and provide preliminary proof of concept for the efficient targeting of ERα- by 17β-estradiol-Pt(II)-linked chemotherapeutic hybrids in these tumors.
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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".