Flt3 ligand as a vaccine adjuvant in association with HER-2/neu peptide-based vaccines in patients with HER-2/neu–overexpressing cancers
Bibliographic record
Abstract
Dendritic cells (DCs) are potent antigen-presenting cells and have shown promise to function as "natural" vaccine adjuvants. Currently, most cancer vaccine trials using DCs generate autologous DCs ex vivo for each patient. Systemic treatment with Flt3 ligand (FL) results in a marked increase of DCs in tissues such as spleen and lymph nodes in mice and in the peripheral blood and skin of humans. In light of these observations, we questioned whether FL could be used systemically as a vaccine adjuvant to stimulate DC mobilization in vivo, circumventing the need to generate DCs ex vivo. Ten patients with HER-2/neu-overexpressing cancer were enrolled in a phase 1 study to receive a HER-2/neu peptide-based vaccine targeting the intracellular domain of the HER-2/neu protein. All patients received 20 microg/kg FL per day subcutaneously for 14 days. Five patients received the HER-2/neu peptide-based vaccine alone on day 7 of the 14-day cycle, and 5 patients received the vaccine admixed with 150 microg granulocyte macrophage-colony-stimulating factor (GM-CSF) on day 7 of the FL cycle. T-cell proliferative responses to HER-2/neu peptides and intracellular domain protein suggest that vaccine regimens including FL as an adjuvant were not effective in eliciting a significant HER-2/neu protein-specific T-cell proliferative response. However, including FL as a vaccine adjuvant was effective in boosting the precursor frequency of interferon-gamma-secreting HER-2/neu-specific T cells. The small sample size of each group, however, did not allow a statistically significant comparison of immune responses between the FL alone and FL with GM-CSF arms. Finally, vaccine regimens including FL as a vaccine adjuvant were associated with the development of apparent autoimmune phenomena in some patients.
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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 | 0.001 | 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 | 0.000 | 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".