Precision medicine attempts to individualize cancer therapy by matching 
tumor-specific genetic changes with effective targeted therapies. A crucial 
first step in this process is the reliable identification of cancer-relevant 
variants, which is considerably complicated by the impurity and heterogeneity of 
clinical tumor samples. We compared the impact of admixture of non-cancerous 
cells and low somatic allele frequencies on the sensitivity and precision of 19 
state-of-the-art SNV callers. We studied both whole exome and targeted gene 
panel data and up to 13 distinct parameter configurations for each tool. We 
found vast differences among callers. Based on our comprehensive analyses we 
recommend joint tumor-normal calling with MuTect, EBCall or Strelka for whole 
exome somatic variant calling, and HaplotypeCaller or FreeBayes for whole exome 
germline calling. For targeted gene panel data on a single tumor sample, 
LoFreqStar performed best. We further found that tumor impurity and admixture 
had a negative impact on precision, and in particular, sensitivity in whole 
exome experiments. At admixture levels of 60% to 90% sometimes seen in 
pathological biopsies, sensitivity dropped significantly, even when variants 
were originally present in the tumor at 100% allele frequency. Sensitivity to 
low-frequency SNVs improved with targeted panel data, but whole exome data 
allowed more efficient identification of germline variants. Effective somatic 
variant calling requires high-quality pathological samples with minimal 
admixture, a consciously selected sequencing strategy, and the appropriate 
variant calling tool with settings optimized for the chosen type of data.