Robust somatic copy number estimation using coarse-to-fine segmentation

Cancers routinely exhibit chromosomal instability that results in copy number variants (CNVs), namely changes in the abundance of genomic material. Unfortunately, the detection of these variants in cancer genomes is difficult. We present Ploidetect, a software package that effectively identifies CNVs within whole-genome sequenced tumors. Ploidetect utilizes a coarse-to-fine segmentation approach which yields highly contiguous segments while allowing for focal CNVs to be detected with high sensitivity. We benchmark Ploidetect against popular CNV tools using synthetic data, cell line data, and real-world metastatic tumor data, and find an improvement in sensitivity and accuracy while maintaining low levels of oversegmentation. We show that the improved CNV sensitivity enables Ploidetect to recover recurrent homozygous deletions and genes associated with chromosomal instability in a multi-cancer cohort of 687 patients. Using highly contiguous CNV calls afforded by Ploidetect, we also demonstrate the use of segment N50 as a novel metric for the measurement of chromosomal instability within tumor biopsies. We propose that increasingly accurate determination of CNVs is critical for their productive study in cancer, and our work demonstrates advances made possible by progress in this regard.