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

TY - JOUR

T1 - Transcriptomic analysis of cutaneous squamous cell carcinoma reveals a multi-gene prognostic signature associated with metastasis

AU - Wang, Jun

AU - Harwood, Catherine A.

AU - Bailey, Emma

AU - Bewicke-Copley, Findlay

AU - Anene, Chinedu Anthony

AU - Thomson, Jason

AU - Jabeen Qamar, Mah

AU - Laban, Rhiannon

AU - Nourse, Craig

AU - Schoenherr, Christina

AU - Treanor-Taylor, Mairi

AU - Healy, Eugene

AU - Lai, Chester

AU - Craig, Paul

AU - Moyes, Colin

AU - Rickaby, William

AU - Martin, Joanne

AU - Proby, Charlotte

AU - Inman, Gareth J.

AU - Leigh, Irene M.

N1 - Copyright: © 2023 Published by Elsevier Inc. on behalf of the American Academy of Dermatology, Inc.

PY - 2023/12

Y1 - 2023/12

N2 - 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.

AB - 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.

KW - cutaneous squamous cell carcinoma

KW - machine learning

KW - metastasis

KW - prognosis

KW - risk stratification

KW - transcriptomics

UR - https://www.scopus.com/pages/publications/85170411135

U2 - 10.1016/j.jaad.2023.08.012

DO - 10.1016/j.jaad.2023.08.012

M3 - Article

C2 - 37586461

SN - 0190-9622

VL - 89

SP - 1159

EP - 1166

JO - Journal of the American Academy of Dermatology

JF - Journal of the American Academy of Dermatology

IS - 6

ER -