A Vasculature-Targeting Regimen of Preoperative Docetaxel with or without Bevacizumab for Locally Advanced Breast Cancer: Impact on Angiogenic Biomarkers
Bibliographic record
Abstract
PURPOSE: Taxanes have effects on angiogenesis causing difficulties in separating biological effects of chemotherapy from those due to angiogenesis inhibitors. This randomized phase II trial was designed to evaluate the additional biomarker effect on angiogenesis when bevacizumab is added to docetaxel. EXPERIMENTAL DESIGN: Patients with inoperable breast cancer were randomized to either 2 cycles of preoperative docetaxel (D) 35 mg/m(2) i.v. weekly for 6 weeks, followed by a 2-week break; or docetaxel with bevacizumab 10 mg/kg i.v. every other week for a total of 16 weeks (DB). Plasma and serum markers of endothelial damage, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and tumor microvessel density were assessed before treatment and at the end of each preoperative cycle. RESULTS: Forty-nine patients were randomized (DB, 24; D, 25). There was no difference in overall clinical response, progression-free survival, or overall survival. Vascular endothelial growth factor increased during treatment; more so with DB (P < 0.0001). Vascular cell adhesion molecule-1 (VCAM-1) also increased (P < 0.0001); more so with DB (P = 0.069). Intercellular adhesion molecule increased (P = 0.018) and E-selectin decreased (P = 0.006) overall. Baseline levels of VCAM-1 and E-selectin correlated with clinical response by univariate analysis. DCE-MRI showed a greater decrease in tumor perfusion calculated by initial area under the curve for the first 90 seconds in DB (P = 0.024). DCE-MRI also showed an overall decrease in tumor volume (P = 0.012). CONCLUSION: Bevacizumab plus docetaxel caused a greater increase in vascular endothelial growth factor and VCAM-1, and a greater reduction in tumor perfusion by DCE-MRI compared with docetaxel. Clinical outcomes of inoperable breast cancer were predicted by changes in VCAM-1 and E-selectin.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".