Case Report: Combined Intra-Lesional IL-2 and Topical Imiquimod Safely and Effectively Clears Multi-Focal, High Grade Cutaneous Squamous Cell Cancer in a Combined Liver and Kidney Transplant Patient
Bibliographic record
Abstract
Cutaneous squamous cell carcinoma (cSCC) is the second most common non-melanoma skin cancer worldwide, with ever increasing incidence and mortality. While most patients can be treated successfully with surgical excision, cryotherapy, or radiation therapy, there exist a subset of patients with aggressive cSCC who lack adequate therapies. Among these patients are solid organ transplant recipients who due to their immunosuppression, develop cSCC at a dramatically increased rate compared to the normal population. The enhanced ability of the tumor to effectively undergo immune escape in these patients leads to more aggressive tumors with a propensity to recur and metastasize. Herein, we present a case of aggressive, multi-focal cSCC in a double organ transplant recipient to frame our discussion and current understanding of the immunobiology of cSCC. We consider factors that contribute to the significantly increased incidence of cSCC in the context of immunosuppression in this patient population. Finally, we briefly review current literature describing experience with localized therapies for cSCC and present a strong argument and rationale for consideration of an IL-2 based intra-lesional treatment strategy for cSCC, particularly in this immunosuppressed patient population.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".