Multiple circulating proangiogenic factors induced by sunitinib malate are tumor-independent and correlate with antitumor efficacy
Bibliographic record
Abstract
Cancer patients treated with antiangiogenic multitargeted receptor tyrosine kinase (RTK) inhibitors show increased levels of plasma VEGF and placental growth factor and decreased levels of soluble VEGF receptor-2, thus implicating these overall changes as a possible class effect of such drugs and raising the possibility of their exploitation as surrogate biomarkers for pharmacodynamic drug activity/exposure and patient benefit. A postulated mechanism for these changes is that they are tumor-dependent, resulting from drug-induced decreases in vascular function, increases in tumor hypoxia, and changes in hypoxia-regulated genes. However, here we report that an identical pattern of change is observed in normal nontumor-bearing mice treated with SU11248/sunitinib, a small-molecule inhibitor of VEGF and PDGF RTKs. The changes were dose-dependent, plateaued after 4 days of consecutive treatment, reversed after discontinuation of therapy, and correlated with antitumor activity. Altered protein expression was found in a broad variety of tissues, and dose-dependent elevations were observed of several plasma proteins previously unassociated with this class of inhibitor, including G-CSF, SDF-1alpha, SCF, and osteopontin. Our results suggest that observed sunitinib-induced molecular plasma changes, including those both directly and indirectly targeted by drug, represent a systemic tumor-independent response to therapy and may correlate with the most efficacious antitumor doses, potentially having utility for defining the optimal biologic dose range for this drug class but not as predictive markers of tumor response or clinical benefit. They may also be relevant to drug-associated toxicities, drug resistance, and observed rapid tumor (re)growth seen after cessation of therapy.
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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) | 0.000 | 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) | 0.001 | 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".