A new cancer immunotherapy via simultaneous DC‐mobilization and DC‐targeted IDO gene silencing using an immune‐stimulatory nanosystem
Bibliographic record
Abstract
The activity of negative immune regulatory molecules, such as indoleamine 2,3-oxygenase (IDO), significantly attenuates DC (Dendritic cells)-mediated immunotherapy. We have previously reported that knockdown of IDO using siRNA can reinstall anti-tumor immunity. However, a DC-targeted siRNA delivery system for in vivo mobilized DCs remains to be developed, while gene silencing in mobilized DCs for cancer immunotherapy has never been explored. In our study, we developed a novel DC-targeted siRNA delivery system, man-GNR-siIDO, using as a nanocarrier of siRNA specific for IDO (siIDO) and mannose (man) as a guide molecule for targeting DCs. We explored the immunostimulatory man-GNR-siIDO nano-construct in DCs mobilized by Flt3-L, a receptor-type tyrosine kinase ligand, for lung cancer immunotherapy. In vivo DC-targeted gene silencing of IDO resulted in robust anti-tumor immunity as evidenced by promoting DC maturation, up-regulating tumor antigen-specific T-cell proliferation and enhancing tumor-specific cytotoxicity. A combinatorial treatment for Lewis Lung Carcinoma (LLC)-bearing mice, with man-GNR-siIDO and Flt3-L, significantly attenuated tumor growth and delayed tumor formation, suggesting the treatment feasibility of the man-GNR-siIDO system in Flt3-L mobilized DCs in the immunotherapy of lung cancer. Therefore, our study highlights a clinical potential for a first-in-class anti-cancer immunotherapy through simultaneous DC-mobilization and DC-targeted gene silencing of IDO with man-GNR-siIDO and Flt3-L treatments.
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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) | 0.000 | 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".