Where do we stand with immunotherapy for nonmelanoma skin cancers in the curative setting?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Nonmelanoma skin cancers (NMSC) represent a heterogeneous group of diseases that encompasses among the principal histologies basal cell carcinoma (BCC), cutaneous squamous cell carcinoma (cSCC), and Merkel cell carcinoma (MCC). Given the fact that high tumor mutational burden due to ultraviolet mutagenesis represents a common hallmark of NMSCs, immunotherapy has proved to be a promising therapeutic approach in recent years. The aim of this review is to shed light on immunotherapy applications in NMSCs in the curative setting. RECENT FINDINGS: Immune checkpoint inhibitors represent the first-line treatment of choice for advanced cSCC and MCC, while in second line for BCC. Given this success, more and more trials are evaluating the use of immune checkpoint blockade in neoadjuvant setting for NMSCs. Clinical trials are still ongoing, with the most mature data being found in cSCC. Also, translational studies have identified promising biomarkers of response. SUMMARY: Locoregional treatments of NMSCs can have non negligible functional and cosmetic impacts on patients, affecting their quality of life. As immunogenic diseases, neoadjuvant immunotherapy represents a promising treatment that could change the therapeutic path of these patients. Upcoming results from clinical trials will address these crucial issues.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".