Modulation of Inflammatory Proteins in Serum May Reflect Cutaneous Immune Responses in Cancer Immunotherapy
Bibliographic record
Abstract
Diphencyprone (DPCP), a topical contact sensitizer, has shown efficacy in treating cutaneous melanoma metastases, including at times beyond the directly treated sites, but biomarkers indicative of treatment response have not been characterized. Thus, we performed a proteomic analysis of the skin and serum of five patients with cutaneous melanoma metastases treated with DPCP on days 0, 63, and 112 of the treatment course. In the serum, we found a significant upregulation ( P < 0.05) in 13 of 96 assessed immuno-oncology proteins after DPCP treatment. Upregulated proteins included those of the T helper 1 axis (CXCL9, CXCL10), immune checkpoint proteins (PD-1), and various proteins with roles in promoting tumor immunity such as CD80 and TNFRSF4/9. Given the positive clinical response to topical treatment noted in the five patients studied, these proteins may represent prognostic biomarkers in the serum for evaluating the efficacy of DPCP treatment of cutaneous melanoma metastases. Because DPCP does not lead to nonspecific immune-related adverse events seen with immune checkpoint inhibitors, our study provides evidence for potential tumor-specific systemic immune activation and systemic antitumor effectors elicited by topical DPCP.
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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 | 0.001 | 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) | 0.001 | 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".