Transcriptomic analysis of cutaneous squamous cell carcinoma reveals a multigene prognostic signature associated with metastasis

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 tumors at high risk of metastasis would have a significant impact on management. To develop a r...

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Bibliographic Details
Published in:Journal of the American Academy of Dermatology 2023-12, Vol.89 (6), p.1159-1166
Main Authors: Wang, Jun, Harwood, Catherine A., Bailey, Emma, Bewicke-Copley, Findlay, Anene, Chinedu Anthony, Thomson, Jason, Qamar, Mah Jabeen, Laban, Rhiannon, Nourse, Craig, Schoenherr, Christina, Treanor-Taylor, Mairi, Healy, Eugene, Lai, Chester, Craig, Paul, Moyes, Colin, Rickaby, William, Martin, Joanne, Proby, Charlotte, Inman, Gareth J., Leigh, Irene M.
Format: Article
Language:English
Subjects:
Carcinoma, Squamous Cell - pathology
cutaneous squamous cell carcinoma
Humans
machine learning
metastasis
Neoplasm Staging
Prognosis
Prospective Studies
Retrospective Studies
risk stratification
Skin Neoplasms - genetics
Skin Neoplasms - pathology
Transcriptome
transcriptomics
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Summary: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 tumors at high risk of metastasis would have a significant impact on management. To develop a robust and validated gene expression profile signature for predicting primary cSCC metastatic risk using an unbiased whole transcriptome discovery-driven approach. Archival formalin-fixed paraffin-embedded primary cSCC with perilesional normal tissue from 237 immunocompetent patients (151 nonmetastasizing and 86 metastasizing) were collected retrospectively from four centers. 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. 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. This was a retrospective 4-center study and larger prospective multicenter studies are now required. The 20-gene signature prediction is accurate, with the potential to be incorporated into clinical workflows for cSCC.
ISSN:0190-9622
1097-6787
DOI:10.1016/j.jaad.2023.08.012