Comprehensive gene expression meta-analysis of head and neck squamous cell carcinoma microarray data defines a robust survival predictor

Background: Head and neck squamous cell carcinoma refers to a heterogeneous disease frequently aggressive in its biologic behavior. Despite the improvements in the therapeutic modalities, the long-term survival rate remained unchanged over the past decade and patients with this type of cancer are at a high risk of developing recurrence. For this reason, there is a great need to find better ways to foresee outcome, to improve treatment choices, and to enable a more personalized approach. Patients and methods: Nine microarray gene expression datasets, reporting survival data of a total of 841 samples, were retrieved from publicly repositories. Three datasets, profiled on the same version of microarray chips, were selected and merged following a meta-analysis approach to build a training set. The remaining six studies were used as independent validation sets. Results: The training set led us to identify a 172-gene signature able to stratify patients in low or high risk of relapse [log-rank, P = 2.44e-05; hazard ratio (HR) = 2.44, 95% confidence interval (CI) 1.58-3.76]. The model based on the 172 genes was validated on the six independent datasets. The performance of the model was challenged against other proposed prognostic signatures (radiosensitivity index, 13-gene oral squamous cell carcinoma signature, hypoxia metagene, 42-gene high-risk signature) and was compared with a human papillomavirus (HPV) signature: our model resulted independent and even better in prediction. Conclusions: We have identified and validated a prognostic model based on the expression of 172 genes, independent from HPV status and able to improve assessment of patient's risk of relapse compared with other molecular signatures. In order to transpose our model into a useful clinical grade assay, additional work is needed following the framework established by the Institute of Medicine and REMARK guidelines