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Models for predicting stage in head and neck squamous cell carcinoma using proteomic data

Head and neck squamous cell carcinoma (HNSCC) that is detected at an advanced stage is associated with much worse patient outcomes than if detected at early stages. This study uses reverse phase protein array (RPPA) data to build predictive models that discriminate between early and advanced stage HNSCC. Individual and ensemble binary classifiers, using filter-based and wrapper-based feature selection, are used to build several models which achieve moderate MCC and AUC values. This study identifies informative protein feature sets which may contribute to an increased understanding of the molecular basis of HNSCC.