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.zip.

Fig. 1.

Histogram of the number of mutations per sample. The data are from Ding et al. (2008) who sequenced 623 genes in 188 tumor samples.

Fig. 2.

Map of the 30 selected genes versus tumor samples. Tumor samples with/without mutations in genes are labeled yellow/blue. The rows (genes) are ordered according to the P-value obtained by our method. The columns (samples) are ordered according to the total number of genes with non-silent mutations (among all 623 genes) in the corresponding sample. The red/blue/yellow banner across the left side of the map shows the difference between selected genes by the two methods: our method and the method of Ding et al. (2008). The genes covered by the red bar are the additional genes found by the method of Ding et al. (2008) and those covered by the yellow bar are the additional genes found by our method. The genes covered by the blue bar are those which both methods find significant.