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B0180
Title: Bayesian inference for intra-tumor heterogeneity in mutations and copy number variation Authors:  Juhee Lee - University of California Santa Cruz (United States) [presenting]
Peter Mueller - UT Austin (United States)
Yuan Ji - University of Chicago (United States)
Subhajit Sengupta - Northshore University HealthSystem (United States)
Kamalakar Gulukota - Northshore University HealthSystem (United States)
Abstract: Tissue samples from the same tumor are heterogeneous. They consist of different subclones that can be characterized by differences in DNA nucleotide sequences and copy numbers on multiple loci. Inference on tumor heterogeneity involves the identification of the subclonal copy number and single nucleotide mutations at a selected set of loci. We estimate such tumor heterogeneity on the basis of a Bayesian feature allocation model. We jointly model subclonal copy numbers and the corresponding allele sequences for the same loci. The proposed method utilizes three random matrices, L, Z and w to represent subclonal copy numbers (L), the number of subclonal variant alleles (Z) and the cellular fractions (w) of subclones in one or more tumor samples, respectively. The unknown number of subclones implies a random number of columns for these matrices and having more than one subclone indicates tumor heterogeneity. We estimate the subclonal structures through inference on these three matrices, using next-generation sequencing data. Using simulation studies and a real data analysis, we demonstrate how posterior inference on the subclonal structure is enhanced with the joint modeling of both structure and sequencing variants on subclonal genomes. R package is available at http://cran.rproject.org/web/packages/ BayClone2/index.html.