Purpose: Distant metastasis after treatment is observed in about 20% of squamous cell carcinoma of the head and neck (HNSCC). In the absence of any validated robust biomarker, patients at higher risk for metastasis cannot be provided with tailored therapy. To identify prognostic HNSCC molecular subgroups and potential biomarkers, we have conducted genome-wide integrated analysis of four omic sets of data.
Experimental design: Using state-of-the-art technologies, a core set of 45 metastasizing and 55 nonmetastasizing human papillomavirus (HPV)-unrelated HNSCC patient samples were analyzed at four different levels: gene expression (transcriptome), DNA methylation (methylome), DNA copy number (genome), and microRNA (miRNA) expression (miRNome). Molecular subgroups were identified by a model-based clustering analysis. Their clinical relevance was evaluated by survival analysis, and functional significance by pathway enrichment analysis.
Results: Patient subgroups selected by transcriptome, methylome, or miRNome integrated analysis are associated with shorter metastasis-free survival (MFS). A common subgroup, R1, selected by all three omic approaches, is statistically more significantly associated with MFS than any of the single omic-selected subgroups. R1 and non-R1 samples display similar DNA copy number landscapes, but more frequent chromosomal aberrations are observed in the R1 cluster (especially loss at 13q14.2-3). R1 tumors are characterized by alterations of pathways involved in cell-cell adhesion, extracellular matrix (ECM), epithelial-to-mesenchymal transition (EMT), immune response, and apoptosis.
Conclusions: Integration of data across several omic profiles leads to better selection of patients at higher risk, identification of relevant molecular pathways of metastasis, and potential to discover biomarkers and drug targets.
©2013 AACR.