Vitiligo-Like Depigmentation Induced by Anti–Programmed Death 1 Antibody
Bibliographic record
Abstract
To the Editor, A 50-year-old woman presented to the dermatology clinic with a 3-month history of asymptomatic depigmented macules and patches on the face, neck, and chest (Fig. 1). Ten months ago, she started treatment with an anti–programmed death (PD)-1 antibody (toripalimab) for metastatic melanoma. One year ago, she was diagnosed with acral lentiginous melanoma with lymph node metastasis and received surgical treatment and high-dose interferon. Both the patient and her family had no history of vitiligo. The patient refused additional treatment for vitiligo-like depigmentation (VLD), which only affected esthetic appearance.Figure 1: Multiple depigmented macules and patches on the face and neck.Vitiligo-like depigmentation is a common cutaneous adverse effect in patients with melanoma receiving checkpoint inhibitors, and the incidence is approximately 10% to 28%, which is much more frequent than spontaneously occurring vitiligo.1 The development of VLD during treatment with checkpoint inhibitors can represent a longer survival and a higher response rate.1 Vitiligo-like depigmentation is different from vitiligo in clinical characteristics and mechanisms. Vitiligo-like depigmentation is characterized by flecked depigmented macules appearing on photoexposed areas without the Koebner phenomenon.2 In contrast to vitiligo, the patients with VLD did not report any personal or family histories of vitiligo, thyroiditis, or other autoimmune disorders.2 The level of serum CXCL10, expression of CXCR3 in skin CD8 T cells, and the levels of lesional interferon γ and tumor necrosis factor α were significantly elevated in patients with VLD.2 Their specific clinical and histological patterns on previous sun-exposed areas with a possible link between microphthalmia-associated transcription factor and the immune response reinforced by anti–PD-1.3 We herein described the first case of VLD induced by toripalimab. Toripalimab, the first domestic anti–PD-1 antibody in China, has received approval to treat melanoma in 2018 as well as nasopharyngeal carcinoma and urothelial carcinoma in 2021 and has shown preliminary efficacy in other tumors with acceptable safety profiles.4 It has different crystal structures and interactions with immune checkpoint proteins compared with other anti–PD-1 antibodies.5 Toripalimab mainly binds to the FG loop of PD-1 with an unconventionally long complementarity-determining region 3 loop of the heavy chain; the light chain complementarity-determining regions of toripalimab participate mainly in recognizing the epitopes on PD-1.4,6 Comparatively, nivolumab mainly binds to the N-terminal loop of PD-1, whereas the binding of pembrolizumab primarily involves the C′ D loop.4 Although VLD occurring in patients receiving anti–PD-1 is not life-threatening, the management of VLD deserves concern because it could dramatically impact the quality of life and social interactions. At present, there is a lack of effective treatment for VLD. Cosmetic camouflage with educational sessions may be a viable choice. Li-Wen ZhangLi-Xin FuWen-Ju WangYong-Hong LuTao Chen Department of Dermatovenereology Chengdu Second People's Hospital China [email protected]
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".