Cyclosporine A immunosuppression drives catastrophic squamous cell carcinoma through IL-22
Bibliographic record
Abstract
Immune-suppressed organ transplant recipients (OTRs) can develop catastrophic squamous cell carcinoma (SCC), characterized by multiple primary tumors, extensive body surface area involvement, or metastases. There are currently no curative systemic therapies available. We previously showed that IL-22 enhances SCC proliferation. Herein, we examined links between cyclosporine (CSA), IL-22, and SCC in patients, cell lines, and mice with UV light-induced SCC. Eighteen of 114 OTRs developed catastrophic SCC, which was strongly associated with CSA treatment. We found that CSA drives T cell polarization toward IL-22-producing T22 cells, and CSA treatment increased IL-22 receptor in SCC cells. SCC tissue from OTRs showed increased expression of IL-22RA1. CSA potentiated rescue by IL-22 of serum-starved SCC cells; treatment of SCC cells with IL-22 and CSA increased both their migratory and invasive capacity. In a UV-induced model of SCC in SKH-1 immunocompetent mice, treatment with anti-IL-22 antibody reduced tumor number and tumor burden. We found that catastrophic SCC in OTRs is associated with CSA use, which may be acting by favoring T22 polarization. Since anti-IL-22 antibody administration decreased tumor number and tumor burden in vivo, blockade of the IL-22 axis may be developed as a viable therapeutic option for catastrophic SCC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".