A Novel Immune-Based Cancer Therapy Using Gene-Silenced Dendritic Cells (48.8)
Bibliographic record
Abstract
Abstract Hyporesponsiveness is a major hallmark in dendritic cells (DC)-mediated anticancer immunotherapy. Indoleamine 2, 3-dioxygenase (IDO), an immunosuppressive molecule expressed by DC, is a critical factor mediating hyporesponsiveness to cancer immune therapy. We hypothesized that silencing of IDO in DC using siRNA would enhance anticancer therapy. In this study, DC were cultured in vitro, exposed to melanoma B16 lysate, silenced with IDO siRNA, and injected into C57/BL6 mice. Mice were then challenged with B16 tumor cells. The anticancer effects of IDO-silenced DC therapy, compared with non-silenced, conventional control DC vaccine, were evidenced by postponed melanoma tumor onset time, and decreased tumor size. In addition, after immunization with IDO-silenced DC, the number of CD8+ T cells was significantly increased while CD4+ and CD8+ T cell apoptosis in draining lymph nodes was remarkably reduced. Furthermore, immunization with IDO-silenced DC enhanced tumor antigen-specific T cell proliferation and CTL activity, and decreased numbers of CD4+CD25+FoxP3+ regulatory T cells (Treg). In conclusion, this study is the first to demonstrate a novel anti-tumor vaccine by silencing an immunosuppressive gene (IDO) in DC, which enhanced anti-tumor immunity, reduced T cell apoptosis and Treg cell formation, and prevented tumor growth. IDO-silenced DC have clinical potential as an immune-based therapy.
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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".