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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".