Dataset Information

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).

SUBMITTER: Dalgleish JL

PROVIDER: S-EPMC6887803 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

CNVScope: Visually Exploring Copy Number Aberrations in Cancer Genomes.

Dalgleish James Lt JL Wang Yonghong Y Zhu Jack J Meltzer Paul S PS

Cancer informatics 20191202

<h4>Motivation</h4>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.<h4>Results</h4>The CNVScope visualization, based on a publicly availab ...[more]

PMID: 31832011

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Project description:DNA copy number aberrations (CNAs) are of biological and medical interest because they help identify regulatory mechanisms underlying tumor initiation and evolution. Identification of tumor-driving CNAs (driver CNAs) however remains a challenging task, because they are frequently hidden by CNAs that are the product of random events that take place during tumor evolution. Experimental detection of CNAs is commonly accomplished through array comparative genomic hybridization (aCGH) assays followed by supervised and/or unsupervised statistical methods that combine the segmented profiles of all patients to identify driver CNAs. Here, we extend a previously-presented supervised algorithm for the identification of CNAs that is based on a topological representation of the data. Our method associates a two-dimensional (2D) point cloud with each aCGH profile and generates a sequence of simplicial complexes, mathematical objects that generalize the concept of a graph. This representation of the data permits segmenting the data at different resolutions and identifying CNAs by interrogating the topological properties of these simplicial complexes. We tested our approach on a published dataset with the goal of identifying specific breast cancer CNAs associated with specific molecular subtypes. Identification of CNAs associated with each subtype was performed by analyzing each subtype separately from the others and by taking the rest of the subtypes as the control. Our results found a new amplification in 11q at the location of the progesterone receptor in the Luminal A subtype. Aberrations in the Luminal B subtype were found only upon removal of the basal-like subtype from the control set. Under those conditions, all regions found in the original publication, except for 17q, were confirmed; all aberrations, except those in chromosome arms 8q and 12q were confirmed in the basal-like subtype. These two chromosome arms, however, were detected only upon removal of three patients with exceedingly large copy number values. More importantly, we detected 10 and 21 additional regions in the Luminal B and basal-like subtypes, respectively. Most of the additional regions were either validated on an independent dataset and/or using GISTIC. Furthermore, we found three new CNAs in the basal-like subtype: a combination of gains and losses in 1p, a gain in 2p and a loss in 14q. Based on these results, we suggest that topological approaches that incorporate multiresolution analyses and that interrogate topological properties of the data can help in the identification of copy number changes in cancer.

Project description:IntroductionBreast cancer is the most commonly diagnosed cancer in women worldwide and consequently has been extensively investigated in terms of histopathology, immunochemistry and familial history. Advances in genome-wide approaches have contributed to molecular classification with respect to genomic changes and their subsequent effects on gene expression. Cell lines have provided a renewable resource that is readily used as model systems for breast cancer cell biology. A thorough characterization of their genomes to identify regions of segmental DNA loss (potential tumor-suppressor-containing loci) and gain (potential oncogenic loci) would greatly facilitate the interpretation of biological data derived from such cells. In this study we characterized the genomes of seven of the most commonly used breast cancer model cell lines at unprecedented resolution using a newly developed whole-genome tiling path genomic DNA array.MethodsBreast cancer model cell lines MCF-7, BT-474, MDA-MB-231, T47D, SK-BR-3, UACC-893 and ZR-75-30 were investigated for genomic alterations with the submegabase-resolution tiling array (SMRT) array comparative genomic hybridization (CGH) platform. SMRT array CGH provides tiling coverage of the human genome permitting break-point detection at about 80 kilobases resolution. Two novel discrete alterations identified by array CGH were verified by fluorescence in situ hybridization.ResultsWhole-genome tiling path array CGH analysis identified novel high-level alterations and fine-mapped previously reported regions yielding candidate genes. In brief, 75 high-level gains and 48 losses were observed and their respective boundaries were documented. Complex alterations involving multiple levels of change were observed on chromosome arms 1p, 8q, 9p, 11q, 15q, 17q and 20q. Furthermore, alignment of whole-genome profiles enabled simultaneous assessment of copy number status of multiple components of the same biological pathway. Investigation of about 60 loci containing genes associated with the epidermal growth factor family (epidermal growth factor receptor, HER2, HER3 and HER4) revealed that all seven cell lines harbor copy number changes to multiple genes in these pathways.ConclusionThe intrinsic genetic differences between these cell lines will influence their biologic and pharmacologic response as an experimental model. Knowledge of segmental changes in these genomes deduced from our study will facilitate the interpretation of biological data derived from such cells.

Dataset Information

CNVScope: Visually Exploring Copy Number Aberrations in Cancer Genomes.

Motivation

Results

Availability

Publications

CNVScope: Visually Exploring Copy Number Aberrations in Cancer Genomes.

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