Abstract
Background: Metastasis of cutaneous squamous cell carcinoma (cSCC) is uncommon. Current staging methods are reported to have sub-optimal performances in metastasis prediction. Accurate identification of patients with tumours at high risk of metastasis would have a significant impact on management.
Objective: To develop a robust and validated gene expression profile (GEP) signature for predicting primary cSCC metastatic risk using an unbiased whole transcriptome discovery-driven approach.
Methods: Archival formalin-fixed paraffin-embedded primary cSCC with perilesional normal tissue from 237 immunocompetent patients (151 non-metastasising and 86 metastasising) were collected retrospectively from four centres. TempO-seq was used to probe the whole transcriptome and machine learning algorithms were applied to derive predictive signatures, with a 3:1 split for training and testing datasets.
Results: A 20-gene prognostic model was developed and validated, with an accuracy of 86.0%, sensitivity of 85.7%, specificity of 86.1%, and positive predictive value of 78.3% in the testing set, providing more stable, accurate prediction than pathological staging systems. A linear predictor was also developed, significantly correlating with metastatic risk.
Limitations: This was a retrospective 4-centre study and larger prospective multicentre studies are now required.
Conclusion: The 20-gene signature prediction is accurate, with the potential to be incorporated into clinical workflows for cSCC.
Objective: To develop a robust and validated gene expression profile (GEP) signature for predicting primary cSCC metastatic risk using an unbiased whole transcriptome discovery-driven approach.
Methods: Archival formalin-fixed paraffin-embedded primary cSCC with perilesional normal tissue from 237 immunocompetent patients (151 non-metastasising and 86 metastasising) were collected retrospectively from four centres. TempO-seq was used to probe the whole transcriptome and machine learning algorithms were applied to derive predictive signatures, with a 3:1 split for training and testing datasets.
Results: A 20-gene prognostic model was developed and validated, with an accuracy of 86.0%, sensitivity of 85.7%, specificity of 86.1%, and positive predictive value of 78.3% in the testing set, providing more stable, accurate prediction than pathological staging systems. A linear predictor was also developed, significantly correlating with metastatic risk.
Limitations: This was a retrospective 4-centre study and larger prospective multicentre studies are now required.
Conclusion: The 20-gene signature prediction is accurate, with the potential to be incorporated into clinical workflows for cSCC.
Original language | English |
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Pages (from-to) | 1159-1166 |
Number of pages | 8 |
Journal | Journal of the American Academy of Dermatology |
Volume | 89 |
Issue number | 6 |
Early online date | 14 Aug 2023 |
DOIs | |
Publication status | Published - Dec 2023 |
Keywords
- cutaneous squamous cell carcinoma
- machine learning
- metastasis
- prognosis
- risk stratification
- transcriptomics
ASJC Scopus subject areas
- Dermatology