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For a genomically unstable cancer, a single tumour biopsy will often containa mixture of competing tumour clones. These tumour clones frequently differwith respect to their genomic content (copy number of each gene) and structure(order of genes on each chromosome). Modern bulk genome sequencing mixes thesignals of tumour clones and contaminating normal cells, complicating inferenceof genomic content and structure. We propose a method to unmix tumour andcontaminating normal signals and jointly predict genomic structure and contentof each tumour clone. We use genome graphs to represent tumour clones, andmodel the likelihood of the observed reads given clones and mixing proportions.Our use of haplotype blocks allows us to accurately measure allele specificread counts, and infer allele specific copy number for each clone. The proposedmethod is a heuristic local search based on applying incremental, locallyoptimal modifications of the genome graphs. Using simulated data, we show thatour method predicts copy counts and gene adjacencies with reasonable accuracy.

Joint Inference of Genome Structure and Content in Heterogeneous Tumour Samples