Amplification of virus-induced antimelanoma T-cell reactivity by high-dose interferon-alpha2b: implications for cancer vaccines.
Bibliographic record
Abstract
PURPOSE: The therapeutic effectiveness of cancer vaccines, composed of tumor antigens that are also self-antigens, may be limited by the normal mechanisms that preserve immunological tolerance. Consistent with this notion, we found that vaccination of melanoma patients with recombinant viral vaccines expressing gp100 (a melanoma antigen also expressed by normal melanocytes) produced only transient increases in noncytotoxic T cells specific for immunodominant gp100 epitopes. To improve the therapeutic effects of these vaccines, IFN-alpha2b (IFN-alpha) was administered to some high-risk patients. EXPERIMENTAL DESIGN: 7 HLA-A*0201(+) patients were injected with high doses of IFN-alpha (20 MU/m(2) x 20 doses) at various times after completing the vaccination protocol. Clinical toxicity and responses were documented, and the effects on gp100-reactive T cells were measured by IFN-gamma enzyme-linked immunospot assays, tetramers of HLA-A*0201 and gp100 epitopes, and cellular cytotoxicity assays. RESULTS: In patients who had previously responded to vaccination, high doses of IFN-alpha recalled gp100-reactive T cells with the ability to kill gp100-expressing tumor targets in vitro. Concomitant with the reappearance of these cytotoxic T cells, tumor regression was observed in the two patients with clinically evident metastatic disease. CONCLUSIONS: The finding that high-dose IFN recalls previously activated tumor-reactive T cells with potent killing ability suggests a strategy to maintain antitumor responses initiated by cancer vaccines.
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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 | 0.000 | 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 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".