Ectopic Expression of Interferon Regulatory Factor-1 Promotes Human Breast Cancer Cell Death and Results in Reduced Expression of Survivin
Bibliographic record
Abstract
The overexpression of the inhibitor of apoptosis protein, survivin, may provide tumor cells with a distinct survival advantage in situ; hence, therapeutic strategies have been designed to inhibit its expression. In this study, we ectopically expressed the interferon regulatory factor (IRF)-1 protein in the breast carcinoma cell lines MDA-MB-468 and SK-BR-3 using a recombinant adenovirus (Ad-IRF-1). By screening microarray analysis of cDNA from the human breast cancer cell line MDA-MB-468 infected with Ad-IRF-1, we observed a 15-fold down-regulation of the survivin gene when compared with uninfected cells. Consequently, we tested survivin expression in Ad-IRF-1-infected MDA-MB-468 and SK-BR-3 breast cancer cell lines. Immunoblotting analyses supported the contention that ectopic expression of the IRF-1 protein results in down-regulation of survivin protein expression that is independent of p53. In addition, Ad-IRF-1 infection of these human breast cancer cell lines induces the expression of p21. We also report that increased apoptosis is observed in tumor cells infected with Ad-IRF-1 compared with Ad-Psi5 mock-infected cells and that cell death is further augmented when the IRF-1-infected cells are cultured with Adriamycin. Moreover, in a xenogeneic mouse model of breast carcinoma, in vivo treatment of tumor-bearing mice with intratumoral Ad-IRF-1 injections results in tumor growth inhibition. In resected tumors from mice that had been treated with Ad-IRF-1, tumor cells that express the IRF-1 transgene have a predominant IRF-1-positive, survivin-negative phenotype. Collectively, these observations suggest that therapies designed to enhance IRF-1 expression within tumor cells may represent novel treatment strategies for breast cancer.
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) | 0.000 | 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) | 0.000 | 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".