Abstract
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.
CONCLUSIONS
EMT in HPV-negative HNSCC co-defines patient outcome after chemoradiotherapy. The generated HNSCC-EMT prediction models can function as strong prognostic biomarkers.
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.
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.