Blockage of immune checkpoint molecules increases T‐cell priming potential of dendritic cell vaccine
Bibliographic record
Abstract
Dendritic cell (DC) -based cancer immunotherapy is one of the most important anti-cancer immunotherapies, and has been associated with variable efficiencies in different cancer types. It is well-known that tumor microenvironment plays a key role in the efficacy of various immunotherapies such as DC vaccine. Accordingly, the expression of programmed death ligand 1 (PD-L1) on DCs, which interacts with PD-1 on T cells, leads to inhibition of anti-tumor responses following presentation of tumor antigens by DCs to T cells. Therefore, we hypothesized that down-regulation of PD-L1 in DCs in association with silencing of PD-1 on T cells may lead to the enhancement of T-cell priming by DCs to have efficient anti-tumor T-cell responses. In this study, we silenced the expression of PD-L1 in DCs and programmed cell death protein 1 (PD-1) in T cells by small interfering RNA (siRNA) -loaded chitosan-dextran sulfate nanoparticles (NPs) and evaluated the DC phenotypic and functional characteristics and T-cell functions following tumor antigen recognition on DCs, ex vivo. Our results showed that synthesized NPs had good physicochemical characteristics (size 77·5 nm and zeta potential of 14·3) that were associated with efficient cellular uptake and target gene silencing. Moreover, PD-L1 silencing was associated with stimulatory characteristics of DCs. On the other hand, presentation of tumor antigens by PD-L1-negative DCs to PD-1-silenced T cells led to induction of potent T-cell responses. Our findings imply that PD-L1-silenced DCs can be considered as a potent immunotherapeutic approach in combination with PD-1-siRNA loaded NPs, however; further in vivo investigation is required in animal models.
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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".