MuSE: Somatic Evolution Estimation for Mutation Calling in Sequencing Data of Matched Tumor-Normal Samples

• Activity Number: 101
• Type: Invited
• Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
• Sponsor: WNAR
• Author(s): Yu Fan and Liu Xi and David Wheeler and Wenyi Wang*+
• Companies: MD Anderson Cancer Center and Baylor College of Medicine and Baylor College of Medicine and MD Anderson Cancer Center
• Keywords: Cancer ; Next-generation sequencing ; Somatic mutation ; Bayesian phylogenetics ; Evolutionary distance ; Dynamic cutoff

Abstract

Cancer is a disease process involving somatic mutational events. The use of next-generation sequencing on matched tumor-normal sample pairs is critical for discovery of somatic variation. However, accurate detection of somatic mutations remains a challenge. Here, we present a Bayesian phylogenetic method, MuSE, for describing the evolution from the reference allele to the tumor and the normal allelic composition at a single nucleotide position. Our proposed method incorporates the probability of sequencing errors and computes the unknown allele frequencies, multiple alternative alleles and the rates of nucleotide transition/transversion. By calculating the evolutionary distances and computing the dynamic cutoffs for d, we classify variants into: somatic, germ-line and reversal to the homozygous reference. We include filters that consider the sequence context surrounding the point mutations to further reduce the false positive rate. We validated the performance of MuSE using both a virtual-tumor benchmarking approach, and applied it to analyzing 66 pairs of chromophobe renal cell carcinoma (KICH) exome sequencing data that are part of the Cancer Genome Atlas (TCGA) project.