Therapy of established B16-F10 melanoma tumors by a single vaccination of CTL/T helper peptides in VacciMax®
Bibliographic record
Abstract
BACKGROUND: Melanoma tumors are known to express antigens that usually induce weak immune responses of short duration. Expression of both tumor-associated antigens p53 and TRP2 by melanoma cells raises the possibility of simultaneously targeting more than one antigen in a therapeutic vaccine. In this report, we show that VacciMax (VM), a novel liposome-based vaccine delivery platform, can increase the immunogenicity of melanoma associated antigens, resulting in tumor elimination. METHODS: C57BL/6 mice bearing B16-F10 melanoma tumors were vaccinated subcutaneously 6 days post tumor implantation with a mixture of synthetic peptides (modified p53: 232-240, TRP-2: 181-188 and PADRE) and CpG. Tumor growth was monitored and antigen-specific splenocyte responses were assayed by ELISPOT. RESULTS: Vaccine formulated in VM increased the number of both TRP2- and p53-specific IFN-gamma producing splenocytes following a single vaccination. Vaccine formulated without VM resulted only in enhanced IFN-gamma producing splenocytes to one CTL epitopes (TRP2:180-188), suggesting that VM overcomes antigen dominance and enhances immunogenicity of multiple epitopes. Vaccination of mice bearing 6-day old B16-F10 tumors with both TRP2 and p53-peptides formulated in VM successfully eradicated tumors in all mice. A control vaccine which contained all ingredients except liposomes resulted in eradication of tumors in no more than 20% of mice. CONCLUSION: A single administration of VM is capable of inducing an effective CTL response to multiple tumor-associated antigens. The responses generated were able to reject 6-day old B16-F10 tumors.
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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".