A Novel Breast/Ovarian Cancer Peptide Vaccine Platform That Promotes Specific Type-1 but not Treg/Tr1-type Responses
Bibliographic record
Abstract
In light of lack of efficacy associated with current cancer vaccines, we aimed to develop a novel vaccine platform called DepoVax as a therapeutic vaccine for breast/ovarian cancer. This study was designed to examine the efficacy of this novel platform over conventional emulsion vaccine using human class I MHC transgenic mice. We have developed a water-free depot vaccine formulation (DPX-0907) with high immune activating potential. Naturally processed peptides bound to HLA-A2 molecules isolated from independent breast and ovarian tumor cell lines, but not normal cells, were isolated and used as antigens in DPX-0907 along with a proprietary adjuvant and a T helper peptide epitope. Efficacy of vaccine was tested in immunized HLA-A*0201/H2Dd transgenic mice by measuring the frequency of IFN-gamma secreting cells in the draining lymph nodes, and regulatory T-cell frequencies in the spleen. Compared with a water-in-oil emulsion vaccine, DPX-0907 enhanced IFN-gamma+CD8+ T cells in vaccine site-draining lymph nodes, as seen by immunofluorescence staining and increased the frequency of IFN-gamma+ lymph node cells as seen by enzyme-linked immunosorbent spot assay. Notably, while conventional vaccine formulations elicited elevated levels of splenic Foxp3+CD4+ and IL10-secreting T cells, this was not the case for DPX-0907-based vaccines, with treated animals exhibiting normal levels of regulatory T cells. These data support the unique capabilities of a vaccine formulation containing novel tumor peptides and DPX-0907 to elicit type-1 dominated, specific immunity that may represent a potent clinical therapeutic modality for patients with breast or ovarian carcinoma.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".