Abstracts from the Human Genome Meeting 2018

BackgroundWith huge amount of genome-wide mutational data generated by cancer genomic sequencing studies, distinguishing cancer drivers from the vast majority of passengers is important.Existing cancer driver prediction methods capture specific mutational aspects in discriminating potential cancer drivers.We explore the possibility of alterative way in doing the task. MethodsWe noted mutational parameters (functional mutation ratio, mutation frequency and sample mutation recurrence) vary differently among mutant genes of different sizes.This led us to develop our novel algorithm (Mutant Gene Ranker -MuGeR), incorporating the comparison of multiple mutational parameters of target gene against the corresponding background derived from a specific subset of genes using a sliding window approach, to estimate the likelihood of target genes for being potential cancer drivers.We applied our MuGeR algorithm on the The Cancer Genome Atlas (TCGA) datasets. ResultsEmpirical data on the TCGA datasets and comparison with the prioritization results of 4 other existing tools (MuSiC, MuSig, TUSON explorer and DOTS-Finder) suggested satisfactory performance of our MuGeR algorithm.More importantly, we demonstrated the existence of specific pattern for mutational parameters across cancers.Conclusions Empirical data verified the usefulness of our MuGeR algorithm in identifying potential cancer drivers.Moreover, our in-depth appraisal of TCGA liver hepatocellular carcinoma datasets further highlighted the frequent mutational dysregulation of ubiquitin-related proteasomal degradation in driving hepatocarcinogenesis.