Antagonism of EG-VEGF Receptors as Targeted Therapy for Choriocarcinoma Progression <i>In Vitro</i> and <i>In Vivo</i>
Bibliographic record
Abstract
Abstract Purpose: Choriocarcinoma (CC) is the most malignant gestational trophoblastic disease that often develops from complete hydatidiform moles (CHM). Neither the mechanism of CC development nor its progression is yet characterized. We recently identified endocrine gland–derived vascular endothelial growth factor (EG-VEGF) as a novel key placental growth factor that controls trophoblast proliferation and invasion. EG-VEGF acts via two receptors, PROKR1 and PROKR2. Here, we demonstrate that EG-VEGF receptors can be targeted for CC therapy. Experimental Design: Three approaches were used: (i) a clinical investigation comparing circulating EG-VEGF in control (n = 20) and in distinctive CHM (n = 38) and CC (n = 9) cohorts, (ii) an in vitro study investigating EG-VEGF effects on the CC cell line JEG3, and (iii) an in vivo study including the development of a novel CC mouse model, through a direct injection of JEG3-luciferase into the placenta of gravid SCID-mice. Results: Both placental and circulating EG-VEGF levels were increased in CHM and CC (×5) patients. EG-VEGF increased JEG3 proliferation, migration, and invasion in two-dimensional (2D) and three-dimensional (3D) culture systems. JEG3 injection in the placenta caused CC development with large metastases compared with their injection into the uterine horn. Treatment of the animal model with EG-VEGF receptor's antagonists significantly reduced tumor development and progression and preserved pregnancy. Antibody-array and immunohistological analyses further deciphered the mechanism of the antagonist's actions. Conclusions: Our work describes a novel preclinical animal model of CC and presents evidence that EG-VEGF receptors can be targeted for CC therapy. This may provide safe and less toxic therapeutic options compared with the currently used multi-agent chemotherapies. Clin Cancer Res; 23(22); 7130–40. ©2017 AACR.
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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 | 0.001 |
| 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".