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Record W7132949018

Sensitizing tumor vascular endothelial cells to anti-angiogenic therapy

2004· dissertation· W7132949018 on OpenAlexaff
Jennifer Tran

Bibliographic record

VenueTSpace · 2004
Typedissertation
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsCanadian Patient Safety InstituteBibliographical Society of Canada
Fundersnot available
KeywordsDrugAngiogenesisCytotoxic T cellVinblastineDrug resistanceCancerTumor microenvironmentEndothelial stem cell
DOInot available

Abstract

fetched live from OpenAlex

The expansion of a tumor mass beyond microscopic sizes is largely contingent on the recruitment of a vascular network. Interference of this angiogenic process impedes the delivery of nutrients and oxygen to the tumor such that tumors often stop growing, and in certain cases even start regressing. Unlike conventional anti-cancer strategies that directly target the tumor compartment, various anti-angiogenic strategies are instead directed towards the vascular compartment. Because the endothelial cells (ECs) that comprise the tumor vessels are genetically stable, such therapies would presumably circumvent or significantly delay acquired drug resistance, a major limitation of current therapies. Together, these studies show that the abundance of angiogenic growth factors within the tumor mass may provide robust survival signals for vascular ECs, which in turn appear to be drug resistant. Because this drug resistance mechanism is not driven by EC genetic mutations, it is expected that specifically abrogating EC survival mechanisms would sensitize ECs to a number of anti-angiogenic treatment strategies. Hence, these studies provide a rationale for the judicious combination of anti-angiogenic strategies with agents that interfere with EC survival. Such strategies could include the use of conventional chemotherapeutic drugs, particularly microtubule inhibitors such as paclitaxel, taxotere, vinblastine and epothilone B, as de facto antioangiogenic EC targeting agents. We further show that VEGF, particularly when combined with bFGF, allows vascular ECs to resist the cytotoxic effects of various chemotherapeutic drugs including tubulin-interfering and DNA damaging agents. Moreover, in the absence of any vascular growth factors, the overexpression of survivin could recapitulate this phenomenon while the overexpression of a dominant-interfering mutant of survivin could abrogate VEGF-mediated survival. Thus, it is possible that VEGF promotes EC chemoresistance primarily, or at least in part, though the induction of survivin. Here, we show that the high concentration of survival factors present within the tumor microenvironment may permit ECs to survive in otherwise cytotoxic conditions. Indeed, we and others show that various angiogenic growth factors, including Vascular Endothelial Growth Factor (VEGF), can induce a robust survival response in ECs secondary to the activation of downstream survival pathways. In particular, the activation of the survivin pathway is shown to play a pivotal role in the survival of ECs as a result of activation by various angiogenic factors including VEGF and basic fibroblast growth factor (bFGF) and more recently angiopoietin 1 (Ang1) and placental-like growth factor (PlGF).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.299
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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