ReviewThe Journal of clinical and aesthetic dermatology2024
Integrating 40-GEP Testing to Improve Clinical Recommendations for Adjuvant Radiation for Cutaneous Squamous Cell Carcinoma: Multidisciplinary Consensus Guidelines.
Review in The Journal of clinical and aesthetic dermatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Novel Immunotherapeutic Approaches for Patients with Head and Neck Cutaneous Squamous Cell Carcinoma.Oncology research · 2026Review
- The Role of Gene Expression Profiling in the Management of Cutaneous Squamous Cell Cancer: A Review.Cancers · 2024Review
- Predicting adjuvant radiation therapy benefit in cutaneous squamous cell carcinoma with the 40-gene expression profile.Future oncology (London, England) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early identification and intervention in patients with cutaneous squamous cell carcinoma (cSCC) who are at high risk for metastasis is important for optimal outcomes. Prognostic tools (e.g., American Joint Committee on Cancer, 8th edition [AJCC-8]) and management guidelines (National Comprehensive Cancer Network® [NCCN]) are useful in helping to identify high-risk patients with cSCC who might benefit from adjuvant therapies, such as radiation and/or immunotherapies; however, traditional staging and management guidelines rely on clinicopathologic risk factors to predict risk, which limits their prognostic accuracy. Gene expression profiling (GEP) is a clinically available, objective metric that can be used in conjunction with traditional clinicopathological staging to help clinicians stratify risk in patients with cSCC. The validated 40-GEP test can accurately classify patients with at least one high-risk feature as being at low (Class 1), higher (Class 2A), or highest (Class 2B) biological risk of nodal or distant metastasis within three years of diagnosis. A multidisciplinary panel comprising radiation oncologists and dermatologists/Mohs micrographic surgeons with expertise in cSCC management convened in June 2023 to discuss the utility of 40-GEP testing in cSCC clinical decision-making in regard to adjuvant radiation therapy (ART). The panel identified gaps in clinical practice in which 40-GEP testing has particular utility: in escalation of care for lower-stage patients with high-risk tumors; in de-escalation of care for patients for whom the risks of ART may outweigh the benefits; and in decision-making regarding elective radiation to the nodal basin. The expert panel developed a risk-based clinical workflow for ART in patients with cSCC, utilizing 40-GEP testing within NCCN management guidelines and AJCC-8 staging.
Indexed as
Identifiers
38495846PMC10939503W4394760116What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.