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A Comparison of DNA Copy Number Profiling Platforms

¹ Translational Medicine, GlaxoSmithKline, King of Prussia, Pennsylvania; ² Center for Applied Cancer Science, the Belfer Institute for Innovative Cancer Science and ³ Department of Medical Oncology, Dana-Farber Cancer Institute; ⁴ Department of Dermatology, Harvard Medical School, Boston, Massachusetts; and ⁵ Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, Pennsylvania

Requests for reprints: Lynda Chin, Department of Medical Oncology, Dana-Farber Cancer Institute, 44 Binney Street, Boston, MA 02115-6084. Phone: 617-632-6091; Fax: 617-632-6069; E-mail: lynda_chin{at}dfci.harvard.edu.

The accurate mapping of recurring DNA copy number aberrations (CNAs), a hallmark feature of the cancer genome, has facilitated the discovery of tumor suppressor genes and oncogenes. Microarray-based assays designed to detect these chromosomal copy number alterations on a genome-wide and high-resolution scale have emerged as a cornerstone technology in the genomic era. The diversity of commercially available platforms prompted a systematic comparison of five copy number profiling assays for their ability to detect 2-fold copy number gain and loss (4n or 1n, respectively) as well as focal high-amplitude CNAs. Here, using a collection of established human melanoma cell lines, we defined the reproducibility, absolute signals, signal to noise, and false-positive and false-negative rates for each of the five assays against ground truth defined by spectral karyotyping, in addition to comparing the concordance of CNA detection by two high-resolution Agilent and Affymetrix microarray platforms. Our analyses concluded that the Agilent's 60-mer oligonucleotide microarray with probe design optimized for genomic hybridization offers the highest sensitivity and specificity (area under receiver operator characteristic curve >0.99), whereas Affymetrix's single nucleotide polymorphism microarray seems to offer better detection of CNAs in gene-poor regions. Availability of these comparison results should guide study design decisions and facilitate further computational development. [Cancer Res 2007;67(21):10173–80]