Clinical parameters associated with immunotherapy outcomes in metastatic cutaneous squamous cell carcinoma
Bibliographic record
Abstract
Lay abstract: We want to understand which subgroups of patients we treat respond well to immunotherapies. The use of anti-programmed death 1 (PD1) therapy cemiplimab has shown significant clinical benefit in patients with recurrent/ metastatic cutaneous squamous cell carcinoma (R/M-cSCC), with durable responses seen in most patients. However, around 30% of patients do not benefit from the treatment, and there is a need to develop predictors of response and resistance to therapy. In this study we aimed to identify clinical parameters that predict response/resistance to therapy in a cohort of 88 R/M-cSCC patients treated with cemiplimab at three UK cancer centers. The results showed that patients with R/M-cSCC with a head and neck primary site had a significantly improved overall survival (OS) and progression-free survival (PFS) compared with other primary sites when treated with cemiplimab. The median OS and PFS were not reached for the entire cohort, and the 2-year OS rate was 60%. The overall response rate (ORR) was 60%, and 40% of patients developed immune-related toxicities of any CTCAE grade, while 13% developed grade 3 or above. The study concludes that cemiplimab treatment demonstrated significant clinical benefit with a manageable side effect profile, and future work should investigate the observed difference in survival outcomes based on primary site further.
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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 | 0.000 | 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) | 0.003 | 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".