Doxycycline-Induced Changes in Circulating MMP or TIMP2 Levels Are Not Associated with Skeletal-Related Event-Free or Overall Survival in Patients with Bone Metastases from Breast Cancer
Bibliographic record
Abstract
Doxycycline is often used as a promoter of inducible gene expression in preclinical models; however, it can also have direct effects on tumor growth and survival. This is due in part to its ability to inhibit cell invasion and regulate matrix metalloproteinase (MMP) expression. Given that doxycycline is also osteotropic, a clinical study to assess its effects on modulation of tumor progression or prevention of skeletal-related events (SRE) in patients with bone metastases from breast cancer (the Achilles trial) was undertaken. Patients received 100 mg of oral doxycycline twice daily for 12 weeks, with serum obtained at baseline and 4, 8 and 12 weeks post-initiation of doxycycline treatment. Exploratory analysis of the effects of doxycycline on circulating levels of MMP or tissue inhibitor of matrix metalloproteinase 2 (TIMP2) was performed in enrolled patients. Statistically significant associations were observed between MMP2, MMP9 and TIMP2 at baseline with significant associations maintained between absolute levels and changes in levels of MMP2 and TIMP2 at weeks 4-12 post initiation of doxycycline. Treatment with doxycycline generally resulted in decreases in MMP2 and MMP9 levels with concurrent upregulation of TIMP2 at 12 weeks post-initiation of doxycycline treatment. Despite this, we observed no association with the levels of any of these factors with either SRE-free or overall survival in this patient cohort. In summary, despite observing hypothesized effects of doxycycline administration on surrogate markers of its anti-tumor activity, measures of circulating levels of these biomarkers were not prognostic in this patient population.
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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 | 0.001 |
| 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 | 0.000 |
| 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 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".