Identifying cancer driver genes in tumor genome sequencing studies

Abstract

Motivation: Major tumor sequencing projects have been conducted in the past few years to identify genes that contain ‘driver’ somatic mutations in tumor samples. These genes have been defined as those for which the non-silent mutation rate is significantly greater than a background mutation rate estimated from silent mutations. Several methods have been used for estimating the background mutation rate.Results: We propose a new method for identifying cancer driver genes, which we believe provides improved accuracy. The new method accounts for the functional impact of mutations on proteins, variation in background mutation rate among tumors and the redundancy of the genetic code. We reanalyzed sequence data for 623 candidate genes in 188 non-small cell lung tumors using the new method. We found several important genes like PTEN, which were not deemed significant by the previous method. At the same time, we determined that some genes previously reported as drivers were not significant by the new analysis because mutations in these genes occurred mainly in tumors with large background mutation rates.Availability: The software is available at: http://linus.nci.nih.gov/Data/YounA/software.zipContact: rsimon@mail.nih.govSupplementary information: Supplementary data are available at Bioinformatics online.

Genomic landscape and genetic heterogeneity in gastric adenocarcinoma revealed by whole-genome sequencing

“…Samples with the TpT mutational pattern (highlighted by a blue bar) tended to occur largely in a subset of diffuse-type GCs and showed a much higher mutation rate in intergenic and intronic regions than in coding regions compared with the remaining samples. -T58 diffuse -T57 diffuse -T49 diffuse -T42 intestinal-T21 intestinal-T43 intestinal-T47 intestinal-T16 intestinal-T56 diffuse -T38 diffuse -T10 diffuse -T26 diffuse -T62 diffuse -T17 diffuse -T06 diffuse -T14 diffuse -T29 diffuse -T44 diffuse -T60 diffuse -T20 diffuse -T07 diffuse -T30 diffuse -T45 diffuse -T31 diffuse -T08 diffuse -T46 diffuse -T18 diffuse -T19 diffuse -T15 diffuse -T28 diffuse -T34 diffuse -T48 diffuse -T04 diffuse -T09 intestinal-T12 intestinal-T55 intestinal-T23 intestinal-T22 intestinal-T50 intestinal-T13 intestinal-T25 intestinal-T05 intestinal-T112 intestinal-T32 intestinal-T02 intestinal-T40 intestinal-T27 diffuse -T36 diffuse -T63 A_C A_G A_T C_A C_C C_G C_T G_A G_C G_G G_T T_A T_C T_G A_A A_C A_G A_T C_A C_C C_G C_T G_A G_C G_G G_T T_A T_C T_G We identify significantly mutated genes 16 (Supplementary Data 6), reaffirming known mutations in GC (TP53, ARID1A, TGFBR2 and CDH1) 3,4 , and uncovered novel mutated genes including SYNE1 (N ¼ 10, 20%) and TMPRSS2 (N ¼ 3, 6%). Mutations of SYNE1 have been frequently reported in oesophageal adenocarcinoma and glioblastoma 13,17 .…”

“…We used the method previously published 16 to identify significantly mutated genes, that is, genes for which the non-silent mutation rate is significantly higher than the background mutation rate. In brief, this method incorporates information such as the functional impact of mutations on the protein products, variation in the background mutation rate among tumours and redundancy of the genetic code to provide significance.…”

Discovery of biclonal origin and a novel oncogene SLC12A5 in colon cancer by single-cell sequencing

“…We used the method described by Youn A and Simon R [25] to compute the significance of observed mutations on each gene. The statistical model takes both mutation prevalence and functional impact into consideration.…”

Cancer stem cells and addicted cancer cells

“…How frequently a mutation occurs is determined by comparing its frequency to the background mutation rate [1]. This rate is estimated based on silent mutations that do not change amino acid encoding and which are therefore considered to be passenger mutations [25]. Different types of mutations have a different impact on protein function.…”

“…A driver gene is expected to have more types of mutations that disrupt its protein function, such as frameshift indels, nonsense mutations and mutations in splice sites. According to the expected impact of their mutations on protein functions, genes with more 'driver-like' mutations are distinguished from genes with less 'driver-like' mutations by assigning different scores for following calculations [25]. Whether the mutation is associated with clonal expansion and whether the gene with the driver-like mutations is involved in known cellular processes that are relevant to oncogenes is also investigated [1].…”