A novel serous ovarian carcinoma model induced by DMBA: Results from OncoTherad® (MRB-CFI-1) immunotherapy preclinical testing
Bibliographic record
Abstract
AIMS: The term ovarian carcinoma (OC) refers to a heterogeneous collection of five distinct diseases known as histotypes. While histotype-specific treatment is still a clinical challenge in OC, well-characterized models are required for testing new therapeutic strategies. We employed OncoTherad® (MRB-CFI-1), an interferon (IFN-γ)-stimulating nano-immunotherapy mediated by Toll-like receptors (TLR) 2/4, in association or not with Erythropoietin (EPO) in a chemically-induced ovarian cancer model. Besides characterization of the therapies effects, we also assessed whether the animal model was representative of human OC by providing histotype classification. MAIN METHODS: Thirty-five Fischer rats were distributed into five groups: Control (Sham surgery); Cancer (7,12-dimethylbenzoanthracene - DMBA injection in the ovarian bursa, 1.25 mg/kg); OncoTherad® (20 mg/kg intraperitoneal); EPO (8.4 µg/kg intraperitoneal); and OncoTherad+EPO (same doses). Ovaries were formalin-fixed into paraffin-embedded blocks. TLR pathway and the inflammatory response profile were evaluated by immunohistochemistry (IHC). After DNA extraction and tissue microarray construction, we assessed typical gene mutations directly (Sanger sequencing) or indirectly (IHC surrogates) and examined biomarkers of different OC histotypes. KEY FINDINGS: OC induction decreased TLR2, TLR4, and proinflammatory cytokines. OncoTherad® alone or associated with EPO modulated the OC microenvironment to a cytotoxic immune profile through stimulation of the TLR4-mediated non-canonical pathway. EPO stimulated TLR2-mediated canonical pathway and notably increased Tregs. SIGNIFICANCE: The features analyzed favored interpretation of our DMBA-induced tumor model as predominantly low-grade, serous carcinoma-like, in which treatments with OncoTherad® and EPO showed immunomodulatory properties related to the reduction of ovarian lesions.
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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".