Inhibition of breast cancer-cell glutamate release with sulfasalazine limits cancer-induced bone pain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 it