Immune checkpoint inhibitors in cutaneous squamous cell carcinoma: A systematic review of clinical trials.
Bibliographic record
Abstract
e21529 Background: Cutaneous squamous cell carcinoma (cSCC) with high-risk features is primarily treated with radiations or surgically. However, locally advanced (LA) or metastatic cSCC (mcSCC) may not be managed surgically and require systemic therapy. In this systematic review, we will assess the efficacy of immune checkpoint inhibitors (ICIs) in patients with resectable, LA, or mcSCC. Methods: A literature search was performed on PubMed, Embase, and clinicaltrials.gov with keywords, “cutaneous squamous cell carcinoma” and “immune checkpoint inhibitor” from the inception of data till 12/31/22. After screening of 883 articles, 8 clinical trials (N = 513) were included. Results: In 8 clinical trials (N = 513), 205 patients had LA cSCC, 99 patients had resectable cSCC, and 209 patients had mcSCC. 81 patients were newly diagnosed while 432 patients were relapsed/refractory (RR). Cemiplimab was used to treat 262 patients, pembrolizumab for 227 patients, and nivolumab for 24 patients. In 2 clinical trials (N = 142) on RR LA cSCC, complete response (CR), partial response (PR), overall response (OR), and progressive disease (PD) were 13-17%, 31-33%, 44-50%, and 12-17%, respectively, in patients treated with ICIs. In 3 clinical trials (N = 175) on RR mcSCC, CR, PR, OR, and PD were 7-18%, 25-41%, 36-64%, and 16-26%, respectively. In two clinical trials (N = 81) on ND cSCC patients, CR, PR, OR, and PD were 0-7%, 35-58%, 42-58%, and 32-20%, respectively. In 2 clinical trials (N = 99) on resectable cSCC, pathological CR, pathological major response, and PD were 51-75%, 13-20%, and 10%, respectively, in patients treated with ICIs. Table. Conclusions: ICIs including cemiplimab, pembrolizumab, and nivolumab were effective in the treatment of resectable, LA, and mcSCC. ICIs were effective in the treatment of both R/R and treatment naïve mcSCC. However, no results were available for randomized clinical trials and large-scale randomized studies are needed to confirm these results. [Table: see text]
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".