Gene characteristics predicting missense, nonsense and frameshift mutations in tumor samples

Ivan P Gorlov,Michael D Cole,David Wallace-Bradley,Hildreth R Frost,Marek Kimmel,Samuel D Strohbehn,Claudio W Pikielny,Christopher I Amos,Stephanie C Her,Olga Y Gorlova

doi:10.1186/s12859-018-2455-0

Abstract

BackgroundBecause driver mutations provide selective advantage to the mutant clone, they tend to occur at a higher frequency in tumor samples compared to selectively neutral (passenger) mutations. However, mutation frequency alone is insufficient to identify cancer genes because mutability is influenced by many gene characteristics, such as size, nucleotide composition, etc. The goal of this study was to identify gene characteristics associated with the frequency of somatic mutations in the gene in tumor samples.ResultsWe used data on somatic mutations detected by genome wide screens from the Catalog of Somatic Mutations in Cancer (COSMIC). Gene size, nucleotide composition, expression level of the gene, relative replication time in the cell cycle, level of evolutionary conservation and other gene characteristics (totaling 11) were used as predictors of the number of somatic mutations. We applied stepwise multiple linear regression to predict the number of mutations per gene. Because missense, nonsense, and frameshift mutations are associated with different sets of gene characteristics, they were modeled separately. Gene characteristics explain 88% of the variation in the number of missense, 40% of nonsense, and 23% of frameshift mutations. Comparisons of the observed and expected numbers of mutations identified genes with a higher than expected number of mutations– positive outliers. Many of these are known driver genes. A number of novel candidate driver genes was also identified.ConclusionsBy comparing the observed and predicted number of mutations in a gene, we have identified known cancer-associated genes as well as 111 novel cancer associated genes. We also showed that adding the number of silent mutations per gene reported by genome/exome wide screens across all cancer type (COSMIC data) as a predictor substantially exceeds predicting accuracy of the most popular cancer gene predicting tool - MutsigCV.

Highlights

Because driver mutations provide selective advantage to the mutant clone, they tend to occur at a higher frequency in tumor samples compared to selectively neutral mutations
It has been shown that genes with a higher expression level tend to have a higher frequency of somatic mutations. [11, 12] Another known gene characteristic associated with mutability is relative replication time within cell cycle: later replicating genes tend to have a higher number of somatic mutations. [11, 12] Chromatin accessibility has been shown to be positively associated with the density of somatic mutations
Major findings We found that gene characteristics can explain considerable proportion of inter genic variation in the number of somatic mutations: 88% for missense, 40% for nonsense, and 23% for frameshift mutations

Summary

Introduction

Because driver mutations provide selective advantage to the mutant clone, they tend to occur at a higher frequency in tumor samples compared to selectively neutral (passenger) mutations. [13] Differences in mutation rate of different nucleotide substitutions, e.g. high frequency of transitions in CpG sites [14] suggest that nucleotide composition of the gene may be associated with mutability. [11, 12] Another known gene characteristic associated with mutability is relative replication time within cell cycle: later replicating genes tend to have a higher number of somatic mutations. Those and other gene characteristics are inter-correlated. The authors noted that about half of the identified driver mutations “occur in yet-to-be-discovered cancer genes”

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