Bivariate segmentation of SNP-array data for allele-specific copy number analysis in tumour samples

Concordance of copy number alterations using a common analytic pipeline for genome-wide analysis of Illumina and Affymetrix genotyping data: a report from the Children's Oncology Group

Copy number alterations (CNAs) are a hallmark of pediatric cancer genomes. An increasing number of research groups use multiple platforms and software packages to detect and analyze CNAs. However, different platforms have experimental and analysis-specific biases that may yield different results. We sought to estimate the concordance of CNAs in children with de novo acute myeloid leukemia between two experimental platforms: Affymetrix SNP 6.0 array and Illumina OmniQuad 2.5 BeadChip. Forty-five paired tumor-remission samples were genotyped on both platforms, and CNAs were estimated from total signal intensity and allelic contrast values using the allele-specific copy number analysis of tumors (ASCAT) algorithm. The two platforms were comparable in detection of CNAs, each missing only two segments from a total of 42 CNAs (4.6%). Overall, there was an interplatform agreement of 96% for allele-specific tumor profiles. However, poor quality samples with low signal/noise ratios showed a high rate of false-positive segments independent of the genotyping platform. These results demonstrate that a common analytic pipeline can be utilized for SNP array data from these two platforms. The customized programming template for the preprocessing, data integration, and analysis is publicly available at https://github.com/AplenCHOP/affyLumCNA.

Performance evaluation of DNA copy number segmentation methods

A number of bioinformatic or biostatistical methods are available for analyzing DNA copy number profiles measured from microarray or sequencing technologies. In the absence of rich enough gold standard data sets, the performance of these methods is generally assessed using unrealistic simulation studies, or based on small real data analyses. To make an objective and reproducible performance assessment, we have designed and implemented a framework to generate realistic DNA copy number profiles of cancer samples with known truth. These profiles are generated by resampling publicly available SNP microarray data from genomic regions with known copy-number state. The original data have been extracted from dilutions series of tumor cell lines with matched blood samples at several concentrations. Therefore, the signal-to-noise ratio of the generated profiles can be controlled through the (known) percentage of tumor cells in the sample. This article describes this framework and its application to a comparison study between methods for segmenting DNA copy number profiles from SNP microarrays. This study indicates that no single method is uniformly better than all others. It also helps identifying pros and cons of the compared methods as a function of biologically informative parameters, such as the fraction of tumor cells in the sample and the proportion of heterozygous markers. This comparison study may be reproduced using the open source and cross-platform R package jointseg, which implements the proposed data generation and evaluation framework: http://r-forge.r-project.org/R/?group_id=1562.