Potentiation of a Dendritic Cell Vaccine for Murine Renal Cell Carcinoma by CpG Oligonucleotides
Bibliographic record
Abstract
PURPOSE: An ideal vaccine therapy for tumors should activate both effector and memory immune responses against tumor-specific antigens. Here we investigated the effect of CpG oligodeoxynucleotides (CpG-ODN) for their ability to potentiate the activity of tumor antigen-pulsed bone marrow-derived dendritic cells (DC) in a vaccine model for the treatment of murine renal cell carcinoma (RENCA). EXPERIMENTAL DESIGN: First we evaluated the effects of a murine renal cell carcinoma (RENCA) on immune cell activity in a mouse model using in vitro assays for T-cell proliferation and natural killer cell activation. To overcome the immune suppression of the tumor, we s.c. injected groups of 10 mice with dendritic cells and tumor cells. We compared the effect of different conditioning regimens of the DCs with RENCA antigen and/or CpG-ODNs before injection by measuring tumor size twice a week. RESULTS: Tumor growth was shown to negatively affect spleen cell and T-cell proliferation, IFN-gamma production, natural killer cell activity, and NF-kappaB activation in T cells. In this model, we have shown that RENCA-pulsed CpG-ODN-treated DCs were able not only to significantly reduce tumor growth but also to prevent tumor implantation in 60% of mice. Tumor-free mice were resistant to tumor challenge and the immunity conferred by the vaccine was transferable and tumor specific. CONCLUSIONS: This data show that RENCA down-modulates the immune response, and DC vaccine therapy, in conjunction with CpG-ODN, can restore tumor-specific immunity.
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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".