RT Journal Article
SR Electronic
T1 Discovery of cancer driver genes based on nucleotide context
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 485292
DO 10.1101/485292
A1 Felix Dietlein
A1 Donate Weghorn
A1 Amaro Taylor-Weiner
A1 André Richters
A1 Brendan Reardon
A1 David Liu
A1 Eric S. Lander
A1 Eliezer M. Van Allen
A1 Shamil R. Sunyaev
YR 2018
UL http://biorxiv.org/content/early/2018/12/04/485292.abstract
AB Many cancer genomes contain large numbers of somatic mutations, but few of these mutations drive tumor development. Current approaches to identify cancer driver genes are largely based on mutational recurrence, i.e. they search for genes with an increased number of nonsynonymous mutations relative to the local background mutation rate. Multiple studies have noted that the sensitivity of recurrence-based methods is limited in tumors with high background mutation rates, because passenger mutations dilute their statistical power. Here, we observe that passenger mutations tend to occur in characteristic nucleotide sequence contexts, while driver mutations follow a different distribution pattern determined by the location of functionally relevant genomic positions along the protein-coding sequence. To discover new cancer genes, we searched for genes with an excess of mutations in unusual nucleotide contexts that deviate from the characteristic context around passenger mutations. By applying this statistical framework to whole-exome sequencing data from 12,004 tumors, we discovered a long tail of novel candidate cancer genes with mutation frequencies as low as 1% and functional supporting evidence. Our results show that considering both the number and the nucleotide context around mutations helps identify novel cancer driver genes, particularly in tumors with high background mutation rates.