Joint Inference of Genome Structure and Content in Heterogeneous Tumour Samples

TL;DR

This paper introduces a novel method for disentangling mixed signals in cancer genome sequencing to accurately infer the structure and content of multiple tumor clones within heterogeneous samples.

Contribution

It presents a new approach using genome graphs and haplotype blocks to jointly infer tumor clone structures and contents from mixed sequencing data.

Findings

Accurately predicts copy number variations in simulated data.

Effectively infers gene adjacency and genome structure.

Demonstrates robustness in heterogeneous tumor samples.

Abstract

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.