Dual VEGF/VEGFR inhibition in advanced solid malignancies
Bibliographic record
Abstract
Our prior phase I study of the combination of vascular endothelial growth factor (VEGF) antibody, bevacizumab, and VEGF receptor (VEGFR) inhibitor, sunitinib, in advanced solid tumors identified an encouraging response evaluation. An expansion phase of this study was thus undertaken to obtain further safety data, response assessment and characterization of pharmacodynamic biomarkers in melanoma, renal, and adrenal carcinoma patients. Patients with metastatic solid tumors received sunitinib (37.5 mg/d, 4 wk on/2 wk off) and bevacizumab (5 mg/kg intravenously every 2 wk). Responses were assessed every 2 cycles. Serum levels of angiogenic molecules were measured using ELISA assays. Twenty-two patients were enrolled, including 11 melanoma, 5 renal cell carcinoma (RCC), 5 adrenal cancer, and 1 angiosarcoma. Grade 3 or higher adverse events were observed in 15 patients, including hypertension (41%), thrombocytopenia (23%), and fatigue (14%). Three RCC patients, and 1 melanoma patient developed thrombotic microangiopathy (TMA). Partial response (PR) occurred in 21% patients, including melanoma (2), adrenal (1), and renal (1) carcinomas. Overall, 6 patients demonstrated some reduction in their tumor burden. Serum VEGF and several other proangiogenic proteins declined over the first 4 wk of treatment whereas the putative VEGF-resistant protein, prokineticin-2, increased over 10-fold. Occurrence of TMA related to dual VEGF/VEGFR inhibition can result from systemic or nephron specific injury even in non-renal malignancies. While the combination of sunitinib and bevacizumab was clinically efficacious in renal cell carcinoma and melanoma, the observance of microangiopathy, even in non-RCC patients, is a significant toxicity that precludes further clinical development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".