Combined therapy with thrombospondin‐1 type I repeats (3TSR) and chemotherapy induces regression and significandy improves survival in a preclinical model of advanced stage epithelial ovarian cancer
Bibliographic record
Abstract
Most women are diagnosed with epithelial ovarian cancer (EOC) at advanced stage, where therapies have limited effectiveness and the long-term survival rate is low. We evaluated the effects of combined antiangiogenic and chemotherapy treatments on advanced stage EOC. Treatment of EOC cells with a recombinant version of the thrombospondin-1 type I repeats (3TSR) induced more apoptotic cell death (36.5 ± 9.6%) in vitro compared to untreated controls (4.1 ± 1.4). In vivo, tumors were induced in an orthotopic, syngeneic mouse model of advanced stage EOC. Mice were treated with 3TSR (4 mg/kg per day) alone or in combination with chemotherapy drugs delivered with maximum tolerated dose or metronomic scheduling. Pretreatment with 3TSR induced tumor regression, normalized tumor vasculature, and improved uptake of chemotherapy drugs. Combination 3TSR and metronomic chemotherapy induced the greatest tumor regression (6.2-fold reduction in size compared to PBS-treated controls) and highest survival when treatment was initiated at advanced stage. 3TSR binding to its receptor, CD36 (cluster of differentiation 36), increased binding of CD36 and SHP-1, which significantly inhibited phosphorylation of the VEGF receptor. In this study, we describe a novel treatment approach and mechanism of action with 3TSR and chemotherapy that induces regression of advanced stage EOC and significantly improves survival.-Russell, S., Duquette, M., Liu, J., Drapkin, R., Lawler, J., Petrik, J. Combined therapy with thrombospondin-1 type I repeats (3TSR) and chemotherapy induces regression and significantly improves survival in a preclinical model of advanced stage epithelial ovarian cancer.
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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".