Dose-related immunomodulatory effects of recombinant TRAIL in the tumor immune microenvironment
Bibliographic record
Abstract
Abstract Background In addition to specifically inducing tumor cell apoptosis, recombinant tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) has also been reported to influence the cancer immune microenvironment; however, its underlying effects and mechanisms remain unclear. Investigating the immunomodulatory effects and mechanisms of recombinant TRAIL in the tumor microenvironment (TME) may provide an important perspective and facilitate the exploration of novel TRAIL strategies for tumor therapy. Methods Immunocompetent mice with different tumors were treated with three doses of recombinant TRAIL, and then the tumors were collected for immunological detection and mechanistic investigation. Methodological approaches include flow cytometry analysis and single-cell sequencing. Results In an immunocompetent mouse model, recombinant soluble mouse TRAIL (smTRAIL) had dose-related immunomodulatory effects. The optimal dose of smTRAIL (2 mg/kg) activated innate immune cells and CD8+ T cells, whereas higher doses of smTRAIL (8 mg/kg) promoted the formation of a tumor-promoting immune microenvironment to counteract the apoptotic effects on tumor cells. The higher doses of smTRAIL treatment promoted M2-like macrophage recruitment and polarization and increased the production of protumor inflammatory cytokines, such as IL-10, which deepened the suppression of natural killer (NK) cells and CD8+ T cells in the tumor microenvironment. By constructing an HU-HSC-NPG.GM3 humanized immune system mouse model, we further verified the immunomodulatory effects induced by recombinant soluble human TRAIL (shTRAIL) and found that combinational administration of shTRAIL and trabectedin, a macrophage-targeting drug, could remodel the tumor immune microenvironment, further enhance antitumor immunity, and strikingly improve antitumor effects. Conclusion Our results highlight the immunomodulatory role of recombinant TRAIL and suggest promising therapeutic strategies for clinical application. Graphical Abstract
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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) | 0.000 | 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".