Vaccination-Induced Autoimmune Vitiligo Is a Consequence of Secondary Trauma to the Skin
Bibliographic record
Abstract
A major concern for cancer vaccines targeting self-tumor antigens is the risk of autoimmune sequelae. Although antitumor immunity correlates with autoimmune disease in some preclinical models, the mechanism(s) linking antitumor immunity and subsequent autoimmune pathology remain(s) to be determined. In the current study, we demonstrated that intradermal (i.d.) immunization with a recombinant adenovirus (Ad) expressing the murine melanoma antigen tyrosinase-related protein 2 (AdmTrp-2) results in a moderate level of tumor protection against the B16F10 murine melanoma without any vitiligo. Similar immunization with an Ad encoding human Trp-2 (AdhTrp-2) resulted in 50-fold greater protective immunity and produced vitiligo in all of the mice, suggesting that the development of autoimmunity may reflect the potency of the vaccine. Interestingly, delivery of AdhTrp-2 by i.m. injection generated protective immunity comparable with that seen in mice that received the vaccine by the i.d. route, but none of the recipients in the i.m. group developed vitiligo. The cellular and humoral responses in the i.m. immunized mice were greater than in the i.d. group; therefore, the lack of vitiligo was not caused by reduced efficacy of the vaccine. These results led us to hypothesize that vaccine-induced vitiligo was associated with local inflammatory responses. Mice immunized i.m. with AdhTrp-2 did develop vitiligo when they subsequently were injected i.d. with either a control Ad vector or complete Freund's adjuvant, suggesting that vitiligo is initiated by some form of trauma within the skin. Our data demonstrated that autoimmune pathology is not an unavoidable outcome of effective cancer vaccines directed against self-tumor antigens.
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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 | 0.001 |
| 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".