Tumor Inhibition by DepoVax-Based Cancer Vaccine Is Accompanied by Reduced Regulatory/Suppressor Cell Proliferation and Tumor Infiltration
Bibliographic record
Abstract
A successful cancer vaccine needs to overcome the effects of immune-suppressor cells such as Treg lymphocytes, suppressive cytokine-secreting Tr1 cells, and myeloid-derived suppressor cells (MDSCs), while enhancing tumor-specific immune responses. Given the relative poor efficacy associated with current cancer vaccines, a novel vaccine platform called DepoVax(TM) (DPX) was developed. C3 tumor-challenged mice were immunized with HPV-E7 peptide in DPX- or conventional-emulsion- (CE-) based vaccine. While control mice showed marked increase in Treg/MDSCs in spleen and blood, in mice treated with DPX-E7 the levels remained similar to tumor-free naive mice. Such differences were also seen within the tumor. Antigen-specific IL10-secreting CD4/CD8 T cells and TGF- β (+)CD8(+) T cell frequencies were increased significantly in CE-treated and control mice in contrast to DPX-E7-immunized mice. Analysis of tumor-infiltrating cells revealed higher frequency of suppressor cells in untreated controls than in DPX-E7 group while the converse was true for tumor-infiltrating CD8 T cells. Immunization of tumor-bearing HLA-A2 transgenic mice with human vaccine DPX-0907, a peptide-based vaccine for breast/ovarian/prostate cancers, showed efficient induction of immune response to cancer peptides despite the presence of suppressor cells. Thus, this study provides the rationale for using DPX-based cancer vaccines in immune-suppressed cancer patients, to induce effective anticancer 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".