Chemosensitization of Cancer <i>In vitro</i> and <i>In vivo</i> by Nitric Oxide Signaling
Bibliographic record
Abstract
PURPOSE: Hypoxia contributes to drug resistance in solid cancers, and studies have revealed that low concentrations of nitric oxide (NO) mimetics attenuate hypoxia-induced drug resistance in tumor cells in vitro. Classic NO signaling involves activation of soluble guanylyl cyclase, generation of cyclic GMP (cGMP), and activation of cGMP-dependent protein kinase. Here, we determined whether chemosensitization by NO mimetics requires cGMP-dependent signaling and whether low concentrations of NO mimetics can chemosensitize tumors in vivo. EXPERIMENTAL DESIGN: Survival of human prostate and breast cancer cells was assessed by clonogenic assays following exposure to chemotherapeutic agents. The effect of NO mimetics on tumor chemosensitivity in vivo was determined using a mouse xenograft model of human prostate cancer. Drug efflux in vitro was assessed by measuring intracellular doxorubicin-associated fluorescence. RESULTS: Low concentrations of the NO mimetics glyceryl trinitrate (GTN) and isosorbide dinitrate attenuated hypoxia-induced resistance to doxorubicin and paclitaxel. Similar to hypoxia-induced drug resistance, inhibition of various components of the NO signaling pathway increased resistance to doxorubicin, whereas activation of the pathway with 8-bromo-cGMP attenuated hypoxia-induced resistance. Drug efflux was unaffected by hypoxia and inhibitors of drug efflux did not significantly attenuate hypoxia-induced chemoresistance. Compared with mice treated with doxorubicin alone, tumor growth was decreased in mice treated with doxorubicin and a transdermal GTN patch. The presence of GTN and GTN metabolites in plasma samples was confirmed by gas chromatography. CONCLUSION: Tumor hypoxia induces resistance to anticancer drugs by interfering with endogenous NO signaling and reactivation of NO signaling represents a novel approach to enhance chemotherapy.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".