For cancers, such as common solid tumours, variants in the genome give a selective growth advantage to certain cells. It has recently been argued that the mean count of coding single nucleotide variants acting as disease-drivers in common solid tumours is frequently small in size, but signiﬁcantly variable by cancer type (hypermutation is excluded from this study). In this paper we investigate this proposal through the use of integrative machine-learning-based classiﬁers we have proposed recently for predicting the disease-driver status of single nucleotide variants (SNVs) in the human cancer genome. We ﬁnd that predicted driver counts are compatible with this proposal, have similar variabilities by cancer type and, to a certain extent, the drivers are identiﬁable by these machine learning methods. We further discuss predicted driver counts stratiﬁed by stage of disease and driver counts in non-coding regions of the cancer genome, in addition to driver-genes.
- Bristol Population Health Science Institute