Distinct Roles for IFN Regulatory Factor (IRF)-3 and IRF-7 in the Activation of Antitumor Properties of Human Macrophages
Bibliographic record
Abstract
When properly activated, macrophages can be tumoricidal, thus making them attractive additions to standard cancer therapies. To this end, tolerance and activity of human autologous IFN-gamma-activated macrophages, produced in large scale for clinical use (MAK cells), have been assessed in pilot trials in cancer patients. In the present study, we tested the hypothesis that activation of IFN regulatory factor (IRF)-3 and IRF-7, with subsequent type I IFN production, may be involved in the acquisition of new antitumor functions by macrophages. Adenoviral vectors were generated for the delivery of constitutively active forms of IRF-3 (Ad-IRF-3) or IRF-7 (Ad-IRF-7) into primary human macrophages. Cell death was observed in Ad-IRF-3-transduced macrophages, whereas Ad-IRF-7-transduced macrophages produced type I IFNs and displayed increased expression of genes encoding tumor necrosis factor (TNF)-related apoptosis-inducing ligand, interleukin (IL)-12, IL-15, and CD80, persisting for at least 96 hours. Expression of iNOS, TNF-alpha, FasL, IL-1, and IL-6 genes was unaltered by Ad-IRF-7 transduction. Interestingly, Ad-IRF-3 or Ad-IRF-7 transduction negatively regulated the transcription of protumorigenic genes encoding vascular endothelial growth factor and matrix metalloproteinase-2. Furthermore, Ad-IRF-7-transduced macrophages exerted a cytostatic activity on different cancer cell lines, including SK-BR-3, MCF-7, and COLO-205; the latter cells were shown previously to be insensitive to MAK cells. In conclusion, transduction of active forms of IRF-3 or IRF-7 differentially modulate the apoptotic and antitumor properties of primary macrophages, with active IRF-7 leading to the acquisition of novel antitumor effector functions.
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 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 | 0.000 |
| 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".