Targeting the Tumour Vasculature: Exploitation of Low Oxygenation and Sensitivity to NOS Inhibition by Treatment with a Hypoxic Cytotoxin
Bibliographic record
Abstract
Many cancer research efforts focus on exploiting genetic-level features that may be targeted for therapy. Tissue-level features of the tumour microenvironment also represent useful therapeutic targets. Here we investigate the presence of low oxygen tension and sensitivity to NOS inhibition of tumour vasculature as potential tumour-specific features that may be targeted by hypoxic cytotoxins, a class of therapeutics currently under investigation. We have previously demonstrated that tirapazamine (TPZ) mediates central vascular dysfunction in tumours. TPZ is a hypoxic cytotoxin that is also a competitive inhibitor of NOS. Here we further investigated the vascular-targeting activity of TPZ by combining it with NOS inhibitor L-NNA, or with low oxygen content gas breathing. Tumours were analyzed via multiplex immunohistochemical staining that revealed irreversible loss of perfusion and enhanced tumour cell death when TPZ was combined with either low oxygen or a NOS inhibitor. Tumour growth rate was reduced by TPZ + NOS inhibition, and tumours previously resistant to TPZ-mediated vascular dysfunction were sensitized by low oxygen breathing. Additional mapping analysis suggests that tumours with reduced vascular-associated stroma may have greater sensitivity to these effects. These results indicate that poorly oxygenated tumour vessels, also being abnormally organized and with inadequate smooth muscle, may be successfully targeted for significant anti-cancer effects by inhibition of NOS and hypoxia-activated prodrug toxicity. This strategy illustrates a novel use of hypoxia-activated cytotoxic prodrugs as vascular targeting agents, and also represents a novel mechanism for targeting tumour vessels.
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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".