ABT-898 Induces Tumor Regression and Prolongs Survival in a Mouse Model of Epithelial Ovarian Cancer
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy and is often not diagnosed until late stages due to its asymptomatic nature. Women diagnosed with EOC typically undergo surgical debulking followed by chemotherapy; however, disease recurrence often occurs. In this study, we evaluated the ability of the thrombospondin-1 mimetic peptide, ABT-898, to regress established, late-stage tumors in a mouse model of human EOC. Ovarian tumors were induced and ABT-898 treatment was initiated at time points that were representative of late stages of the disease to study tumor regression. ABT-898 induced tumor regression and reduced the morbidity of treated animals compared with controls. Analysis of tumors from ABT-898-treated animals showed reduced abnormal tumor vasculature, decreased expression of the proangiogenic compound VEGF, and reduced tumor tissue hypoxia. ABT-898 treatment initiated at late-stage disease also significantly prolonged disease-free survival compared with control animals. Results from this study show that ABT-898 is capable of regressing established ovarian tumors in an animal model of the disease. As most women are detected at advanced stage EOC, ABT-898 may improve our treatment of ovarian cancer.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".