Introduction:
Genomic alterations are a driving force in the multistep process of head and neck cancer (HNC) and result from the interaction of environmental exposures and endogenous cellular processes. Each of these processes leaves a characteristic pattern of mutations on the tumor genome providing the unique opportunity to decipher specific signatures of mutational processes operative during HNC pathogenesis and to address their prognostic value.
Methods:
Whole exome sequencing data were generated with primary tumor samples from HNC patients (n = 83) and somatic mutational signatures were identified by a systematic computational approach. Distinct patient subgroups were identified by principle component analysis (PCA) considering most common mutational signatures, and differences in survival were calculated by univariate and multivariate analysis. Data were confirmed with public available data from the TCGA-HNC cohort.
Results:
Computational analysis of whole exome sequencing data revealed five common mutational signatures in our cohort, which showed distinct relationships with most prominent etiological risk factors (tobacco, alcohol and HPV). These mutational signatures were also highly abundant in the dataset from TCGA-HNC. PCA analysis considering the relative distribution of all five mutational signatures unraveled four patient subgroups with statistically significant differences in clinical and pathological features as well as survival in both cohorts.
Conclusion:
This study provides a proof-of-concept that computational analysis of somatic mutational signatures is not only a powerful tool to decipher environmental and intrinsic processes in the pathogenesis of HNC, but could also pave the way to establish reliable prognostic patterns.
