Gemcitabine intercellular diffusion mediated by gap junctions: new implications for cancer therapy
Bibliographic record
Abstract
BACKGROUND: Solid tumors are often poorly vascularized, with cells that can be 100 microm away from blood vessels. These distant cells get less oxygen and nutrients and are exposed to lower doses of chemotherapeutic agents. As gap junctions allow the passage of small molecules between cells, we tested the possibility that the chemotherapeutic agent gemcitabine can diffuse through gap junctions in solid tumors. RESULTS: We first showed with a dye transfer assay that the glioblastoma and the osteosarcoma cells used in this study have functional gap junctions. These cells were genetically engineered to express the herpes simplex virus thymidine kinase (TK), and induced a "bystander effect" as demonstrated by the killing of TK-negative cells in presence of the nucleoside analogue ganciclovir (GCV). The ability of gemcitabine to induce a similar bystander effect was then tested by mixing cells treated with 3 microM gemcitabine for 24 hours with untreated cells at different ratios. In all cell lines tested, bystander cells were killed with ratios containing as low as 5% treated cells, and this toxic effect was reduced in presence of alpha-glycyrrhetinic acid (AGA), a specific gap junction inhibitor. We also showed that a 2- or a 24-hour gemcitabine treatment was more efficient to inhibit the growth of spheroids with functional gap junctions as compared to the same treatment made in presence of AGA. Finally, after a 24-hour gemcitabine treatment, the cell viability in spheroids was reduced by 92% as opposed to 51% in presence of AGA. CONCLUSION: These results indicate that gemcitabine-mediated toxicity can diffuse through gap junctions, and they suggest that gemcitabine treatment could be more efficient for treating solid tumors that display gap junctions. The presence of these cellular channels could be used to predict the responsiveness to this nucleoside analogue 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".