Efficacy of Cotargeting Angiopoietin-2 and the VEGF Pathway in the Adjuvant Postsurgical Setting for Early Breast, Colorectal, and Renal Cancers
Bibliographic record
Abstract
Antiangiogenic tyrosine kinase inhibitors (TKI) that target VEGF receptor-2 (VEGFR2) have not been effective as adjuvant treatments for micrometastatic disease in phase III clinical trials. Angiopoietin-2 (Ang2) is a proangiogenic and proinflammatory vascular destabilizer that cooperates with VEGF. The purpose of this study was to test whether CVX-060 (an Ang2-specific CovX-body) can be combined with VEGFR2-targeting TKIs (sunitinib or regorafenib) to successfully treat postsurgical metastatic disease in multiple orthotopically implanted human tumor xenograft and syngeneic murine tumor models. In the MDA-MB-231.LM2-4 human breast cancer model, adjuvant sunitinib was ineffective, whereas adjuvant CVX-060 delayed the progression of pulmonary or distant lymphatic metastases; however, overall survival was only improved with the adjuvant use of a VEGF-A/Ang2-bispecific CovX-body (CVX-241) but not when CVX-060 is combined with sunitinib. Adjuvant CVX-241 also showed promise in the EMT-6/CDDP murine breast cancer model, with or without an immune checkpoint inhibitor (anti-PD-L1). In the RENCA model of mouse renal cancer, however, combining CVX-060 with sunitinib in the adjuvant setting was superior to CVX-241 as treatment for postsurgical lung metastases. In the HCT116 and HT29 xenograft models of colorectal cancer, both CVX-060 and regorafenib inhibited liver metastases. Overall, our preclinical findings suggest differential strategies by which Ang2 blockers can be successfully combined with VEGF pathway targeting in the adjuvant setting to treat micrometastatic disease-particularly, in combination with VEGF-A blockers (but not VEGFR2 TKIs) in resected breast cancer; in combination with VEGFR2 TKIs in resected kidney cancer; and as single agents or with VEGFR2 TKIs in resected colorectal cancer. Cancer Res; 76(23); 6988-7000. ©2016 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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".