STAT3 Inhibition in a Murine Model of Human Breast Cancer-Induced Bone Pain Delays Onset of Nociception
Bibliographic record
Abstract
Introduction/Aim: Alterations in extracellular glutamate levels have been previously found to contribute to cancer-induced bone pain (CIBP). Increased activity of system xc-, a cystine-glutamate membrane antiporter, has been previously implicated in our lab in these nociceptive behaviours. System xc- subunit xCT, is further positively regulated by signal transducer and activator of transcription 3 (STAT3). In the current investigation, we hypothesized that DR-1–55-mediated inhibition of pSTAT3 will lead to decreased nociceptive behaviours in a validated xCT overexpression model of CIBP.Methods: Using a murine xenograft CIBP model, a high glutamate-releasing xCT/pSTAT3 overexpressing human breast cancer cell line (T47D clone) was injected into the distal epiphysis of the femur of female nude mice. Nociceptive behaviours were monitored through automated von Frey, dynamic weight bearing, and open field testing for the study duration. Three weeks after cell inoculation, a 14-day schedule of intraperitoneal injections of DR-1–55 or vehicle were administered.Results: In vitro, there were elevated levels of glutamate, IL-6, and IL- 1β exhibited in the T47D clone. These cells also showed significant nociceptive behaviours earlier than the T47D wild type (WT). Treatment with DR-1–55 significantly delayed the onset and severity of spontaneous and induced nociceptive behaviours, as seen with behavioural testing.Discussion/Conclusions: This study shows that targeting pSTAT3 may be a viable treatment when managing CIBP, and can be a molecule of interest in future studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".