Aflibercept and Ang1 supplementation improve neoadjuvant or adjuvant chemotherapy in a preclinical model of resectable breast cancer
Bibliographic record
Abstract
Phase III clinical trials evaluating bevacizumab (an antibody to the angiogenic ligand, VEGF-A) in breast cancer have found improved responses in the presurgical neoadjuvant setting but no benefits in the postsurgical adjuvant setting. The objective of this study was to evaluate alternative antiangiogenic therapies, which target multiple VEGF family members or differentially modulate the Angiopoietin/Tie2 pathway, in a mouse model of resectable triple-negative breast cancer (TNBC). Neoadjuvant therapy experiments involved treating established orthotopic xenografts of an aggressive metastatic variant of the MDA-MB-231 human TNBC cell line, LM2-4. Adjuvant therapies were given after primary tumor resections to treat postsurgical regrowths and distant metastases. Aflibercept ('VEGF Trap', which neutralizes VEGF-A, VEGF-B and PlGF) showed greater efficacy than nesvacumab (an anti-Ang2 antibody) as an add-on to neoadjuvant/adjuvant chemotherapy. Concurrent inhibition of Ang1 and Ang2 signaling (through an antagonistic anti-Tie2 antibody) was not more efficacious than selective Ang2 inhibition. In contrast, short-term perioperative BowAng1 (a recombinant Ang1 variant) improved the efficacy of adjuvant chemotherapy. In conclusion, concurrent VEGF pathway inhibition is more likely than Ang/Tie2 pathway inhibition (e.g., anti-Ang2, anti-Ang2/Ang1, anti-Tie2) to improve neoadjuvant/adjuvant chemotherapies for TNBC. Short-term perioperative Ang1 supplementation may also have therapeutic potential in conjunction with adjuvant chemotherapy for TNBC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".