Targeting HER-2/ <i>neu</i> in Early Breast Cancer Development Using Dendritic Cells with Staged Interleukin-12 Burst Secretion
Bibliographic record
Abstract
Overexpression of HER-2/neu (c-erbB2) is associated with increased risk of recurrent disease in ductal carcinoma in situ (DCIS) and a poorer prognosis in node-positive breast cancer. We therefore examined the early immunotherapeutic targeting of HER-2/neu in DCIS. Before surgical resection, HER-2/neu(pos) DCIS patients (n = 13) received 4 weekly vaccinations of dendritic cells pulsed with HER-2/neu HLA class I and II peptides. The vaccine dendritic cells were activated in vitro with IFN-gamma and bacterial lipopolysaccharide to become highly polarized DC1-type dendritic cells that secrete high levels of interleukin-12p70 (IL-12p70). Intranodal delivery of dendritic cells supplied both antigenic stimulation and a synchronized preconditioned burst of IL-12p70 production directly to the anatomic site of T-cell sensitization. Before vaccination, many subjects possessed HER-2/neu-HLA-A2 tetramer-staining CD8(pos) T cells that expressed low levels of CD28 and high levels of the inhibitory B7 ligand CTLA-4, but this ratio inverted after vaccination. The vaccinated subjects also showed high rates of peptide-specific sensitization for both IFN-gamma-secreting CD4(pos) (85%) and CD8(pos) (80%) T cells, with recognition of antigenically relevant breast cancer lines, accumulation of T and B lymphocytes in the breast, and induction of complement-dependent, tumor-lytic antibodies. Seven of 11 evaluable patients also showed markedly decreased HER-2/neu expression in surgical tumor specimens, often with measurable decreases in residual DCIS, suggesting an active process of "immunoediting" for HER-2/neu-expressing tumor cells following vaccination. DC1 vaccination strategies may therefore have potential for both the prevention and the treatment of early breast cancer.
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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) | 0.000 | 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 | 0.000 |
| 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".