Treatment of Lung Cancer Using Clinically Relevant Oral Doses of the Cyclooxygenase-2 Inhibitor Rofecoxib
Bibliographic record
Abstract
OBJECTIVE: To investigate the uses and limitations of cyclooxygenase- (COX) 2 inhibition using clinically relevant doses of oral rofecoxib in the treatment of murine models of non-small-cell lung cancer (NSCLC). SUMMARY BACKGROUND DATA: Overexpression of COX-2 has been reported in lung cancer. Several studies have demonstrated that high doses of COX-2 inhibitors could inhibit the growth of rodent and human lung cancer cell lines. The potential uses and limitations of COX-2 inhibition at doses equivalent to those currently approved for use in humans have not been well studied. METHODS: Three murine NSCLC cell lines were injected into the flanks of mice to establish tumor xenografts. Mice were treated orally with low doses of a COX-2 inhibitor (rofecoxib chow, 0.0075%). Mechanisms were evaluated by analysis of tumor-infiltrating lymphocytes. To study rofecoxib as adjuvant therapy, large established tumors (14-18 days after tumor inoculation) were surgically debulked and animals were treated with rofecoxib starting 3 days before surgery. Recurrence of the tumor after debulking was monitored. RESULTS: Rofecoxib significantly slowed the growth of small (0-120 mm) tumors (P < 0.01-0.05) in all 3 cell lines, with higher efficacy in the more immunogenic tumors. Minimal responses were noted in larger tumors. Rofecoxib appeared to augment CD8 T cell infiltration in immunogenic tumors. Rofecoxib significantly reduced the recurrence rate after debulking (P < 0.01). CONCLUSIONS: Clinically relevant doses of the COX-2 inhibitor rofecoxib given orally were effective in inhibiting the growth of small (but not large) tumors in 3 murine NSCLC cell lines tested and in preventing recurrences after surgical debulking. Depending on the immunogenicity of human tumors, COX-2 inhibition might be useful as adjuvant therapy for surgically resectable NSCLC.
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.
How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".