IFN-β activates cytotoxic function of human natural killer cells toward IL-27 and poly(I:C) stimulated PC3 and DU145 cells
Bibliographic record
Abstract
Natural killer (NK) cell phenotype and function are altered in patients with prostate cancer, and increased NK cell activity is associated with a better prognosis in patients with disease. For patients with advanced stage prostate cancer, immunotherapies are a promising approach when standard treatment options have been exhausted. With the rapid emergence of NK cell-based therapies, it is important to understand the mechanisms by which NK cells can be triggered to kill cancer cells that have developed immune-evasive strategies. Altering the cytokine profiles of advanced prostate cancer cells may be an area to explore when considering ways in which NK cell activation can be modulated. We have previously demonstrated that combining the cytokine, IL-27, with TLR3 agonist, poly(I:C), changes cytokine secretion in the advanced prostate cancer models, PC3 and DU145 cells. Herein, we extend our previous work to study the effect of primary human NK cells on prostate cancer cell death in an in vitro co-culture model. Stimulating PC3 and DU145 cells with IL-27 and poly(I:C) induced IFN-β secretion, which was required for activation of primary human NK cells to kill these stimulated prostate cancer cells. PC3 cells were more sensitized to NK cell-mediated killing when compared to DU145 cells, which was attributed to differential levels of IFN-β produced in response to stimulation with IL-27 and poly(I:C). IFN-β increased granzyme B secretion and membrane-bound TRAIL expression by co-cultured NK cells. We further demonstrated that these NK cells killed PC3 cells in a partially TRAIL-dependent manner. This work provides mechanistic insight into how the cytotoxic function of NK cells can be improved to target cancer cells.
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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 | 0.001 |
| 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".