Cancer immunotherapy using virally transduced dendritic cells: animal studies and human clinical trials
Bibliographic record
Abstract
The immune system uses a process known as 'immunosurveillance' to help prevent the outgrowth of tumors. In cancer immunotherapy, a major goal is for immunity against tumor-associated antigens to be generated or strengthened in patients. To achieve this goal, several approaches have been tested, including the use of highly potent antigen-presenting cells called dendritic cells (DCs), which can activate T cells efficiently. Presentation of peptides derived from tumor antigens on the surface of DCs can stimulate strong antitumor immunity. Using recombinant viral vectors encoding tumor-associated antigens, DCs can be engineered efficiently to express sustained levels of tumor-antigen peptides. This review discusses the effectiveness of virally transduced DCs in treating tumors and generating antigen-specific T-cell responses. It covers mouse and nonhuman primate studies, preclinical in vitro human cell experiments and clinical trials.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".