Abstract
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.
Highlights
In Rogers et al.[18] we proposed CScape, a method for predicting the disease-driver status of single nucleotide variants in the coding and non-coding regions of the human cancer genome
We found a reasonable alignment with Martincorena et al.[6] using test data derived from the International Cancer Genome consortium[23] and labelled as primary tumours
Comparing means for cancer types in common between both analyses, we find that our lowest and third lowest ranked cancers by single nucleotide variants (SNVs)-driver count are thyroid and renal cancer and these are lowest ranked by Martincorena et al.[6]
Summary
In recent years a number of methods have been developed for predicting the pathogenic impact of variants in both the coding and non-coding regions of the human genome. In Shihab et al.[7] and Rogers et al.[8] we developed such predictors based on pathogenic disease-driver single nucleotide variants (SNVs) from the Human Gene Mutation Database (HGMD9) and assumed neutral variants from the 1,000 Genomes Project Consortium (1000 G10). In Rogers et al.[18] we proposed CScape, a method for predicting the disease-driver status of single nucleotide variants in the coding and non-coding regions of the human cancer genome (the method is more fully described in Supplementary Section 1). We refer to these types of disease-driver positives as SNV-drivers
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