Silencing IDO in dendritic cells: A novel approach to enhance cancer immunotherapy in a murine breast cancer model
Bibliographic record
Abstract
Cancer immunotherapeutic agents (vaccines) in the form of antigen-loaded dendritic cells (DCs) reached an important milestone with the recent approval of Provenge, the first DC vaccine for treatment of prostate cancer. Although this heralds a new era of tumor immunotherapy, it also highlights the compelling need to optimize such DC-based therapies as they are increasingly tested and used to treat human patients. In this study we sought to augment and enhance the antitumor activity of a DC-based vaccine using siRNA to silence expression of immunosuppressive enzyme indoleamine 2,3-dioxygenase (IDO) in DCs. We report here that DCs loaded with tumor antigens, but with siRNA-silenced IDO expression, were introduced into 4T1 breast tumor-bearing mice, the treatment: (i) lengthened the time required for tumor onset, (ii) decreased tumor size compared to tumors grown for equal lengths of time in mice treated with antigen-loaded DCs without IDO silencing and (iii) reduced CD4(+) and CD8(+) T cell apoptosis. Furthermore, immunization with IDO-silenced DCs enhanced tumor antigen-specific T cell proliferation and CTL activity, and decreased numbers of CD4(+) CD25(+) Foxp3(+) T(reg). This study provides evidence to support silencing of immunosuppressive genes (IDO) as an effective strategy to enhance the efficacy of DC-based cancer immunotherapeutic.
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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".