Aneuploidy Spectrum Analysis as a Primer for Copy Number Profiling of Cancer Cells

Abstract Motivation Hyperploidy and segmental aneuploidy are hallmarks of cancer cells due to chromosome segregation errors and genomic instability. In such situations, accurate aneuploidy profiling of cancer data is critical for calibration of copy number (CN)-detection tools. Additionally, cancer cell populations suffer from different levels of clonal heterogeneity and aneuploidy alterations over time. The degree of heterogeneity adversely affects the segregation of the depth of coverage (DOC) signal into integral CN states. This, in turn, strongly influences the reliability of this data for ploidy profiling and copy number variation (CNV) analysis. Results We developed AStra framework for aneuploidy profiling of cancer data and assessing their suitability for copy number analysis without any prior knowledge of the input sequencing data. AStra estimates the best-fit aneuploidy profile as the spectrum with most genomic segments around integral CN states. We employ this spectrum to extract the CN-associated features such as the homogeneity score (HS), whole-genome ploidy level, and CN correction factor. The HS measures the percentage of genomic regions around CN states. It is used as a reliability assessment of sequencing data for downstream aneuploidy profiling and CNV analysis. We evaluated the accuracy of AStra using 31 low-coverage datasets from 20 cancer cell lines. AStra successfully identified the aneuploidy spectrum of complex cell lines with HS greater than 75%. Benchmarking against nQuire tool showed that AStra is superior in detecting the ploidy level using both low- and high-coverage data. Furthermore, AStra accurately estimated the ploidy of 26/27 strains of MCF7 (hyperploid) cell line which exhibit varied levels of aneuploidy spectrum and heterogeneity. Remarkably, we found that HS is strongly correlated with the doubling time of these strains. Availability and implementation AStra is an open source software implemented in Python and is available at https://github.com/AISKhalil/AStra