Transcriptomic analysis of cutaneous squamous cell carcinoma reveals a multi-gene prognostic signature associated with metastasis

Jun Wang (Lead / Corresponding author), Catherine A. Harwood, Emma Bailey, Findlay Bewicke-Copley, Chinedu Anthony Anene, Jason Thomson, Mah Jabeen Qamar, Rhiannon Laban, Craig Nourse, Christina Schoenherr, Mairi Treanor-Taylor, Eugene Healy, Chester Lai, Paul Craig, Colin Moyes, William Rickaby, Joanne Martin, Charlotte Proby, Gareth J. Inman, Irene M. Leigh

    Research output: Contribution to journalArticlepeer-review

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    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.
    Original languageEnglish
    Pages (from-to)1159-1166
    Number of pages8
    JournalJournal of the American Academy of Dermatology
    Volume89
    Issue number6
    Early online date14 Aug 2023
    DOIs
    Publication statusPublished - Dec 2023

    Keywords

    • cutaneous squamous cell carcinoma
    • machine learning
    • metastasis
    • prognosis
    • risk stratification
    • transcriptomics

    ASJC Scopus subject areas

    • Dermatology

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