Transcriptomic analysis of cutaneous squamous cell carcinoma reveals a multigene prognostic signature associated with metastasis
Jun Wang, Catherine Harwood, Emma Bailey, Findlay Bewicke‐Copley, Chinedu A. 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 M. Proby, Gareth J. Inman, Irene M. Leigh
Abstract
BackgroundMetastasis 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.ObjectiveTo develop a robust and validated gene expression profile signature for predicting primary cSCC metastatic risk using an unbiased whole transcriptome discovery-driven approach.MethodsArchival 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.ResultsA 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.LimitationsThis was a retrospective 4-center study and larger prospective multicenter studies are now required.ConclusionThe 20-gene signature prediction is accurate, with the potential to be incorporated into clinical workflows for cSCC. 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.