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