CNVScope: Visually Exploring Copy Number Aberrations in Cancer Genomes

Abstract

Motivation: DNA copy number (CN) data are a fast-growing source of information used in basic and translational cancer research. Most CN segmentation data are presented without regard to the relationship between chromosomal regions. We offer both a toolkit to help scientists without programming experience visually explore the CN interactome and a package that constructs CN interactomes from publicly available data sets.

Results: The CNVScope visualization, based on a publicly available neuroblastoma CN data set, clearly displays a distinct CN interaction in the region of the MYCN, a canonical frequent amplicon target in this cancer. Exploration of the data rapidly identified cis and trans events, including a strong anticorrelation between 11q loss and17q gain with the region of 11q loss bounded by the cell cycle regulator CCND1.

Availability: The shiny application is readily available for use at http://cnvscope.nci.nih.gov/, and the package can be downloaded from CRAN (https://cran.r-project.org/package=CNVScope), where help pages and vignettes are located. A newer version is available on the GitHub site (https://github.com/jamesdalg/CNVScope/), which features an animated tutorial. The CNVScope package can be locally installed using instructions on the GitHub site for Windows and Macintosh systems. This CN analysis package also runs on a linux high-performance computing cluster, with options for multinode and multiprocessor analysis of CN variant data. The shiny application can be started using a single command (which will automatically install the public data package).

Keywords: CNA; CNV; R; cancer; copy number; copy number variation; shiny; visualization.

Figure 1.

Workflow from GDC TARGET neuroblastoma CN data to finalized interchromosomal matrices used in the shiny application. Files are converted from GDC tab-delimited files with varying bin sizes into an input matrix of even 1 Mb bins and sample identifiers, and then into relationship metrics from linear regression (the negative log P-value). Postprocessing then sets the infinites to a high number to allow visualization and adds negative signs in regions where the strong relationship is an inverse linear relationship. Finally, the large, postprocessed matrix is converted to many small matrices with the ensembl-75 genes mapped to each genomic bin pair. TARGET indicates Therapeutically Applicable Research to Generate Effective Treatments; CN, copy number; CNV, copy number variant.

Figure 2.

(A) A whole genome interaction view of neuroblastoma copy number (CN) associations (chr1-X). Boxed regions highlight chr2 (enlarged in Figure 3), chr11, and the negatively signed off-diagonal association of 11q and 17q. (B) The enlarged chr11-chr17 map illustrates the strong anticorrelated regions of 11q-17q. The lowest correlation point is highlighted (r =−0.482, Benjamini-Hochberg adjusted P value is .000117). The number of tests used for the adjustment is the number of bin pairs in the whole genome. (C) Representative regression plot of CN values on 11q and 17q illustrates an anticorrelation trend as well as the detailed sample-level data on 3 tooltips. (D) The data from the same coordinates as (C) are represented as a histogram showing clear separation of CN value distributions on 11q and 17q. These plots support the interpretation that there is a significant association of 11q loss with 17q gain.

Figure 3.

(A) Intrachromosomal association plot for chromosome 2. The box highlights a distinct feature on the diagonal indicating narrowing of the region of local co-association, and white lines emanating from that region show a reduction in association from the MYCN locus across all loci on the chromosome. (B) Enlarged view of the MYCN amplification domain with a break in linear regression signal near MYCN most likely due to amplification of MYCN in relatively small, often extrachromosomal, amplicons. The MYCN locus is highlighted.

Figure 4.

CCND1, a focally amplified cell cycle regulator, is located within a distinct association domain in 11q13. In this plot of chr11-chr11, the sharp reduction of chr11-chr11 association is likely the result of structural variants leading to CCND1 copy number (CN) gain. False discovery rate P-value filtering was applied to this Pearson correlation plot of CN associations on chr11. CCND1 is located precisely at chr11:69455855-69469242.