Induction of Wilms' Tumor Protein (WT1)–Specific Antitumor Immunity Using a Truncated WT1-Expressing Adenovirus Vaccine
Bibliographic record
Abstract
PURPOSE: Wilms' tumor protein (WT1) is overexpressed in most leukemias and many solid tumors and is a promising target for tumor immunotherapy. WT1 peptide-based cancer vaccines have been reported but have limited application due to HLA restriction of the peptides. We sought to vaccinate using adenoviral (Ad) vectors encoding tumor-associated antigens such as WT1 that can stimulate tumor-associated antigen-specific immunity across a broad array of HLA types and multiple class I and class II epitopes. EXPERIMENTAL DESIGN: We developed a novel Ad vector encoding a truncated version of WT1 (Ad-tWT1) lacking the highly conserved COOH terminus zinc finger domains and tested its ability to stimulate WT1-specific immune responses and antitumor immunity in two murine models of WT1-expressing tumors. RESULTS: Despite encoding a transcription factor, we found that Ad-tWT1-transduced murine and human dendritic cells showed cytoplasmic expression of the truncated WT1 protein. In addition, vaccination of C57BL/6 mice with Ad-tWT1 generated WT1-specific cell-mediated and humoral immune responses and conferred protection against challenge with the leukemia cell line, mWT1-C1498. Moreover, in a tumor therapy model, Ad-tWT1 vaccination of TRAMP-C2 tumor-bearing mice significantly suppressed tumor growth. CONCLUSIONS: This is the first report of a WT1-encoding Ad vector that is capable of inducing effective immunity against WT1-expressing malignancies. Based on these findings, Ad-tWT1 warrants investigation in human clinical trials to evaluate its applications as a vaccine for patients with WT1-expressing cancers.
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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) | 0.001 | 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".