RESUMEN

BACKGROUND: Current management of head and neck squamous cell carcinoma (HNSCC) depends on tumor staging. Despite refinements in clinical staging algorithms, outcomes remain unchanged for the last two decades. In this study, we set out to identify a small, clinically applicable molecular panel to aid prognostication of patients with HNSCC. MATERIALS AND METHODS: Data from The Cancer Genome Atlas (TCGA) was used to derive copy number aberrations and expression changes to identify putative prognostic genes. To account for cross entity relevance of the biomarkers, HNSCC (n = 276), breast (n = 808) and lung cancer (n = 282) datasets were used to identify robust and reproducible markers with prognostic potential. Validation was performed using immunohistochemistry (IHC) on tissue microarrays of an independent cohort of HNSCC (n = 333). FINDINGS: Using GISTIC algorithm together with gene expression analysis, we identified six putative prognostic genes in at least two out of three cancers analyzed, of which four were successfully optimized for automated IHC. Of these, three were successfully validated; each molecular target being significantly prognostic on univariate analysis. Patients were differentially segregated into four prognostic groups based on the number of genes dysregulated (p < 0.001). The IHC panel remained an independent predictor of survival after adjusting for known survival covariates including clinical staging criteria in a multivariate Cox regression model (p < 0.001). . INTERPRETATION: We have identified and validated a clinically applicable IHC biomarker panel that is independently associated with overall survival. This panel is readily applicable, serving as a useful adjunct to current staging systems and provides novel targets for future therapeutic strategies.