Pitfalls of vaccinations with WT1-, Proteinase3- and MUC1-derived peptides in combination with MontanideISA51 and CpG7909
Bibliographic record
Abstract
T cells with specificity for antigens derived from Wilms Tumor gene (WT1), Proteinase3 (Pr3), and mucin1 (MUC1) have been demonstrated to lyse acute myeloid leukemia (AML) blasts and multiple-myeloma (MM) cells, and strategies to enhance or induce such tumor-specific T cells by vaccination are currently being explored in multiple clinical trials. To test safety and immunogenicity of a vaccine composed of WT1-, Pr3-, and MUC1-derived Class I-restricted peptides and the pan HLA-DR T helper cell epitope (PADRE) or MUC1-helper epitopes in combination with CpG7909 and MontanideISA51, four patients with AML and five with MM were repetitively vaccinated. No clinical responses were observed. Neither pre-existing nor naive WT1-/Pr3-/MUC1-specific CD8+ T cells expanded in vivo by vaccination. In contrast, a significant decline in vaccine-specific CD8+ T cells was observed. An increase in PADRE-specific CD4+ T helper cells was observed after vaccination but these appeared unable to produce IL2, and CD4+ T cells with a regulatory phenotype increased. Taken into considerations that multiple clinical trials with identical antigens but different adjuvants induced vaccine-specific T cell responses, our data caution that a vaccination with leukemia-associated antigens can be detrimental when combined with MontanideISA51 and CpG7909. Reflecting the time-consuming efforts of clinical trials and the fact that 1/3 of ongoing peptide vaccination trails use CpG and/or Montanide, our data need to be taken into consideration.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".