Genomic characterization of additional cancer-driver genes using a weighted iterative regression accurately modelling background mutation rate

Genomic aberrations in somatic cells are major drivers of cancers and cancers are of high genetic heterogeneity and most driver genes are only of moderate or small effect size. Existing bioinformatics methods poorly model background mutations and are underpowered to identify driver genes in typical-size samples. Here we propose a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER), to detect cancer-driver genes showing an excess of somatic mutations. This approach has a three-tier framework to improve power in small or moderate samples by accurately modelling background mutations. Compared to alternative methods, this approach detected more significant and cancer-consensus genes in all tested cancers. This technical advance enables the detection of driver genes in TCGA datasets as small as 30 subjects, rescuing genes missed by alternative tools. By introducing an advanced statistical model for accurately estimating the background mutation rate even in small-to-moderate samples, the proposed method is more powerful approach for detecting cancer driver genes than current methods, helps provide a comprehensive landscape of driver genes in cancers.