Validation of Bone Marrow Derived Dendritic Cells as an Appropriate Model to Study Tumor-Mediated Suppression of DC Maturation through STAT3 Hyperactivation
Bibliographic record
Abstract
PURPOSE: Tumors can escape immune eradication by harnessing dendritic cell (DC) maturation. However, DC types used as in vitro models to study tumor-mediated immunosuppression possess fundamental variability that could influence research outcomes. Therefore, we assessed the behavior of two distinct murine DC models upon exposure to tumor-conditioned medium of B16.F10 melanoma (B16-CM). METHODS: Using primary bone-marrow derived dendritic cells (BMDCs) or immortalized DC2.4 cell line, we evaluated the level of signal transducer and activator of transcription 3 (STAT3) phosphorylation by Western blot as a molecular parameter. We also examined the surface expression of co-stimulatory molecules on DCs by flow cytometry as a phenotypic parameter. RESULTS: Our results revealed critical discrepancies between the two models in response to tumor-conditioned medium. While conditioned medium was able to induce STAT3 phosphorylation in BMDCs, it did not significantly induce STAT3 phosphorylation in DC2.4 cell line. Moreover, only in BMDCs, the expression of CD86 and CD40 was remarkably downregulated by B16-CM and was not totally recovered after LPS stimulation. In contrast, DC2.4 cells did not show any signs of harnessed maturation upon exposure to B16-CM. CONCLUSIONS: In order to study the effect of tumor-mediated immunosuppression on DC maturation in vitro via tumor-induction of STAT3 activation, primary BMDCs are more reliable as a model than DC2.4.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
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".