Potential Proinvasive or Metastatic Effects of Preclinical Antiangiogenic Therapy Are Prevented by Concurrent Chemotherapy
Bibliographic record
Abstract
PURPOSE: To resolve a controversy involving the therapeutic impact of antiangiogenic drugs and particularly antibodies targeting the VEGF pathway, namely, a body of preclinical mouse therapy studies showing such drugs can promote invasion and/or distant metastasis when used as monotherapies. In contrast, clinical studies have not shown such promalignancy effects. However, most such clinical studies have involved patients also treated with concurrent chemotherapy highlighting the possibility that chemotherapy may prevent any potential promalignancy effect caused by an antiangiogenic drug treatment. EXPERIMENTAL DESIGN: The impact of antiangiogenic therapy using DC101, an antibody targeting mouse VEGFR-2 with or without concurrent chemotherapy was assessed in multiple human breast cancer xenograft models, where impact on orthotopic primary tumors was evaluated. Metastasis was also assessed during adjuvant and neoadjuvant plus adjuvant therapy, after surgical resection of primary tumors, with the same combination therapies. RESULTS: Antiangiogenic therapy, while blunting tumor volume growth, was found to increase local invasion in multiple primary tumor models, including a patient-derived xenograft, but this effect was blocked by concurrent chemotherapy. Similarly, the combination of paclitaxel with DC101 caused a marked reduction of micro- or macrometastatic disease in contrast to DC101 monotherapy, which was associated with small increases in metastatic disease. CONCLUSIONS: Conventional wisdom is that targeted biologic antiangiogenic agents such as bevacizumab when used with chemotherapy increase the efficacy of the chemotherapy treatment. Our results suggest the reverse may be true as well-chemotherapy may improve the impact of antiangiogenic drug treatment and, as a result, overall efficacy. Clin Cancer Res; 21(24); 5488-98. ©2015 AACR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".