Safe Administration of Cemiplimab to a Kidney Transplant Patient with Locally Advanced Squamous Cell Carcinoma of the Scalp
Bibliographic record
Abstract
Immunotherapies directed at T-cell activation through antibodies targeting checkpoint proteins, such as programmed cell death 1 (PD1), are rapidly becoming the new standard of care in the treatment of several malignancies. Cemiplimab is a monoclonal antibody targeting PD1 that has recently emerged as a highly active treatment for locally advanced and metastatic cutaneous squamous cell carcinoma (CSCC). Patients who have received an organ transplant (OTRs) have been traditionally excluded from clinical trials with checkpoint inhibitors (CIs), given concerns for organ rejection. Renal transplant recipients (RTRs) are more likely to develop cancers than the general population, and skin cancers are among the most frequent malignancies. We report the case of a 72-year-old man with a history of a kidney transplant who presented with a rapidly growing, locally advanced squamous cell carcinoma (SCC) of the scalp that recurred within four weeks from surgical resection. The patient was able to safely receive ten cycles of cemiplimab so far with significant clinical benefit, and no issues with his kidney function, while continuing immunosuppression with low dose prednisone alone. An ongoing clinical trial (NCT04339062) is further exploring the safety of CIs in patients with metastatic CSCC who have previously received allogeneic hematopoietic stem cell transplant or a kidney transplant.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".