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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 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".