Inhibition of breast cancer-cell glutamate release with sulfasalazine limits cancer-induced bone pain
Bibliographic record
Abstract
Cancer in bone is frequently a result of metastases from distant sites, particularly from the breast, lung, and prostate. Pain is a common and often severe pathological feature of cancers in bone, and is a significant impediment to the maintenance of quality of life of patients living with bone metastases. Cancer cell lines have been demonstrated to release significant amounts of the neurotransmitter and cell-signalling molecule l-glutamate via the system xC(-) cystine/glutamate antiporter. We have developed a novel mouse model of breast cancer bone metastases to investigate the impact of inhibiting cancer cell glutamate transporters on nociceptive behaviour. Immunodeficient mice were inoculated intrafemorally with the human breast adenocarcinoma cell line MDA-MB-231, then treated 14days later via mini-osmotic pumps inserted intraperitoneally with sulfasalazine, (S)-4-carboxyphenylglycine, or vehicle. Both sulfasalazine and (S)-4-carboxyphenylglycine attenuated in vitro cancer cell glutamate release in a dose-dependent manner via the system xC(-) transporter. Animals treated with sulfasalazine displayed reduced nociceptive behaviours and an extended time until the onset of behavioural evidence of pain. Animals treated with a lower dose of (S)-4-carboxyphenylglycine did not display this reduction in nociceptive behaviour. These results suggest that a reduction in glutamate secretion from cancers in bone with the system xC(-) inhibitor sulfasalazine may provide some benefit for treating the often severe and intractable pain associated with bone metastases.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".