Epithelial-to-mesenchymal transition is a prognostic marker for patient outcome in advanced stage HNSCC patients treated with chemoradiotherapy. (June 2020)

• Record Type: Journal Article
• Authors: van der Heijden, Martijn; Essers, Paul B.M.; Verhagen, Caroline V.M.; Willems, Stefan M.; Sanders, Joyce; de Roest, Reinout H.; Vossen, David M.; Leemans, C. René; Verheij, Marcel; Brakenhoff, Ruud H.; van den Brekel, Michiel W.M.; Vens, Conchita
• Is Part Of: Radiotherapy and oncology. Volume 147(2020)
• Page Start: 186
• Page End: 194
• Publication Date: 2020-06
• Subjects: Epithelial to mesenchymal transition -- Head and neck cancer -- Prognostic biomarkers -- Chemoradiotherapy -- HNSCC -- RNA-Seq; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2020.05.013
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: Mesenchymal characteristics are associated with poor prognosis in HPV-negative HNSCC. Multiple EMT signatures confirm poor prognosis association. Novel HNSCC-specific EMT prediction models predict patient outcomes. Early metastatic disease prior to treatment linked to mesenchymal HNSCC. Abstract: Background: The prognosis of patients with HPV-negative advanced stage head and neck squamous cell carcinoma (HNSCC) remains poor. No prognostic markers other than TNM staging are routinely used in clinic. Epithelial-to-mesenchymal transition (EMT) has been shown to be a strong prognostic factor in other cancer types. The purpose of this study was to determine the role of EMT in HPV-negative HNSCC outcomes. Methods: Pretreatment tumor material from patients of two cohorts, totalling 174 cisplatin-based chemoradiotherapy treated HPV-negative HNSCC patients, was RNA-sequenced. Seven different EMT gene expression signatures were used for EMT status classification and generation of HNSCC-specific EMT models using Random Forest machine learning. Results: Mesenchymal classification by all EMT signatures consistently enriched for poor prognosis patients in both cohorts of 98 and 76 patients. Uni- and multivariate analyses show important HR of 1.6–5.8, thereby revealing EMT's role in HNSCC outcome. Discordant classification by these signatures prompted the generation of an HNSCC-specific EMT profile based on the concordantly classified samples in the first cohort (cross-validation AUC > 0.98). The independent validation cohort confirmed the association of mesenchymal classification by the HNSCC-EMT model with poor overall survival (HR = 3.39, p < 0.005) and progression free survival (HR = 3.01, p < 0.005) in multivariate analysis with TNM. Analysis of an additional HNSCC cohort from PET-positive patients with metastatic disease prior to treatment further supports this relationship and reveals a strong link of EMT to the propensity to metastasize. Conclusions: EMT in HPV-negative HNSCC co-defines patient outcome after chemoradiotherapy. The generated HNSCC-EMT prediction models can function as strong prognostic biomarkers.