Identifying Significantly Mutated Genes in Cancer

Significantly mutated genes were identified using MutSig2CV which combines p-values from tests for high mutational frequency relative to the background mutation rate (pCV), clustering of mutations within the gene (pCL), and enrichment of mutations within evolutionarily conserved sites (pFN)^{10 (link)}. For 660 lung adenocarcinomas, we had 100% power to detect genes mutated in 10% of patients and 73% power for genes mutated in 5% of patients assuming a mutation rate of 8.7/Mb^{10 (link)}. For 484 lung squamous cell carcinomas, we had 100% power to detect genes mutated in 10% of patients and 41% power for genes mutated in 5% of patients assuming a mutation rate of 9.7/Mb^{10 (link)}. In order to reduce the number of hypotheses tested in the MutSig2CV analysis, we excluded genes that exhibited low expression across tumors with relatively high purity. The median log₂ FPKM value for each gene was obtained for 185 ADCs or 238 SqCCs which had a purity estimate from ABSOLUTE of >50% and available RNA-seq data (Supplementary Fig. 1). For each tumor type, a mixture model of two normal distributions was fit in R using the mclust package v4.2. Genes with 95% probability of belonging to the cluster with higher expression were considered in the multiple hypothesis correction of the MutSig2CV combined p-values. One gene, TRERF1, was excluded from the final results as closer inspection of its mutations revealed a recurrent frameshift deletion that was likely a false positive as all of these mutations had low allelic fractions (<1.5%) and had no supporting reads in matching RNA-seq data. A one-sided Fisher’s exact test was used to determine if the proportion of loss-of-function mutations (including nonsense, frameshift, and de novo start out-of-frame mutations) to other mutations for a given gene was significantly higher compared to the proportion of loss-of-function mutations to other mutations across all other genes.