Network-based predictors of progression in head and neck squamous cell carcinoma

The heterogeneity in head and neck squamous cell carcinoma (HNSCC) has made reliable stratification extremely challenging. Behavioral risk factors such as smoking and alcohol consumption contribute to this heterogeneity. To help elucidate potential mechanisms of progression in HNSCC, we focused on elucidating patterns of gene interactions associated with tumor progression. We performed de-novo gene co-expression network inference utilizing 229 patient samples from The Cancer Genome Atlas (TCGA) previously annotated by Bornstein et al. (2016). Differential network analysis allowed us to contrast progressor and non-progressor cohorts. Beyond standard differential expression (DE) analysis, this approach evaluates changes in gene expression variance (differential variability DV) and changes in covariance, which we denote as differential wiring (DW). The set of affected genes was overlaid onto the co-expression network, identifying 12 modules significantly enriched in DE, DV, and/or DW genes. Additionally, we identified modules correlated with behavioral measures such as alcohol consumption and smoking status. In the module enriched for differentially wired genes, we identified network hubs including IL10RA, DOK2, APBB1IP, UBASH3A, SASH3, CELF2, TRAF3IP3, GIMAP6, MYO1F, NCKAP1L, WAS, FERMT3, SLA, SELPLG, TNFRSF1B, WIPF1, AMICA1, PTPN22; the network centrality and progression specificity of these genes suggest a potential role in tumor evolution mechanisms related to inflammation and microenvironment. The identification of this network-based gene signature could be further developed to guide progression stratification, highlighting how network approaches may help improve clinical research end points and ultimately aid in clinical utility.