Functional effects of TrkA inhibition on system x <sub>C</sub> <sup>−</sup> -mediated glutamate release and cancer-induced bone pain
Bibliographic record
Abstract
Breast cancer cells release the signalling molecule glutamate via the system x C − antiporter, which is upregulated to exchange extracellular cystine for intracellular glutamate to protect against oxidative stress. Here, we demonstrate that this antiporter is functionally influenced by the actions of the neurotrophin nerve growth factor on its cognate receptor tyrosine kinase, TrkA, and that inhibiting this complex may reduce cancer-induced bone pain via its downstream actions on xCT, the functional subunit of system x C − . We have characterized the effects of the selective TrkA inhibitor AG879 on system x C − activity in murine 4T1 and human MDA-MB-231 mammary carcinoma cells, as well as its effects on nociception in our validated immunocompetent mouse model of cancer-induced bone pain, in which BALB/c mice are intrafemorally inoculated with 4T1 murine carcinoma cells. AG879 decreased functional system x C − activity, as measured by cystine uptake and glutamate release, and inhibited nociceptive and physiologically relevant responses in tumour-bearing animals. Cumulatively, these data suggest that the activation of TrkA by nerve growth factor may have functional implications on system x C − -mediated cancer pain. System x C − -mediated TrkA activation therefore presents a promising target for therapeutic intervention in cancer pain treatment.
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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) | 0.002 | 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".