A role for survivin in chemoresistance of endothelial cells mediated by VEGF
Bibliographic record
Abstract
Although standard anticancer chemotherapeutic drugs have been designed to inhibit the survival or growth of rapidly dividing tumor cells, it is possible to enhance the efficacy of such drugs by targeting the proliferating host endothelial cells (ECs) of the tumor vasculature. A theoretical advantage of this strategy lies in the possibility of circumventing, or significantly delaying, acquired drug resistance driven by the genetic instability of tumor cells. Here, we show that both vascular endothelial growth factor (VEGF) and basic fibroblast growth factor significantly reduce the pro-apoptotic potency of chemotherapy on both micro- and macrovascular ECs. This cytoprotection to drug toxicity was found to be phosphatidylinositol 3-kinase-dependent and could be recapitulated in the absence of VEGF by overexpressing the dominant-active form of the serine/threonine kinase protein kinase B/Akt. Downstream of phosphatidylinositol 3-kinase, we also show that survivin plays a pivotal role in VEGF-mediated EC protection by preserving the microtubule network. In this respect, its induction effectively protects ECs against chemotherapeutic damage, whereas overexpression of its dominant-interfering mutant (C84A) abrogates the protective effects of VEGF. Accordingly, the potency of VEGF as a chemoprotectant was more pronounced with drugs that interfere with microtubule dynamics than those that damage DNA. These studies implicate a role for survivin up-regulation as a novel mechanism of EC drug "resistance" and support the notion that angiogenic factors that induce the expression of survivin may act to shield tumor ECs from the apoptotic effects of chemotherapy. Thus, exploiting chemotherapeutic drugs as antiangiogenics is likely to be compromised by the high concentrations of proangiogenic survival/growth factors present in the tumor microenvironment; targeting EC survival pathways should improve the antiangiogenic efficacy of antineoplastic agents, particularly microtubule-inhibitor drugs.
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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) | 0.000 | 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 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".