Abstract 4957: Comparison of commercial and custom microarrays: which is the best choice for copy-number analysis in tumor samples (2010)

Abstract Microarray technologies are powerful assays, which can identify copy-number abnormalities (CNA) underlying the pathogenesis of cancer. Several commercial arrays are available but it is unclear which is the best platform. We did a side-by-side comparison between two leading arrays; the Affymetrix SNP6 and Agilent 1M, to compare data compression, noise, gene and exon coverage, and CNA detection. Five samples were run on both platforms and analyzed as recommended by the manufacturer. We first performed an in silico comparison to define the number of probes per serial 5k interval, number of probes per gene and exon, and the coefficient of variation of different smoothing windows. Comparing the CV of the two arrays showed a similar level of variation between a 3 and 5 probe averaging on the 1M and SNP6, respectively. Using these smoothing levels the 1M interrogates 81.2% of the genes in the genome compared to 69.3% on the SNP6. At the exon level the 1M has at least one probe in 64.8% compared to 41.2% on the SNP6. We observed a similar level of data compression (∼10%) on the two arrays but the SNP6 had nearly twice the noise (SD 0.38 versus 0.21). Finally, there was a marked increment in the number of CNA identified by 1M (mean of 78.5) than SNP6.0 (59.5). In this comparison the Agilent 1M out performed the Affymetrix SNP6. Even with half the number of probes, the 1M was superior in CNA detection, gene/exon coverage and showed less signal variation. A comparison with previous platforms showed that these two arrays did not identify new gene amplifications but did detect new deletions, however, neither platform detected several known deletions detected by other methods. Therefore, we designed a custom, exon-centered, 2×400K array (Agilent) with the goal of identifying additional deletions. The design outline was: a) all the probes from the Agilent CGH 44K array; b) 3 probes per exon across the human exome; c) high resolution tiling of 1450 cancer implicated genes; and d) tiling the 4kb flanking all MIRs. To improve performance, we only included probes with performance scores higher than 0.8 and 0.85 in queries b) and c), respectively. Lastly, 15000 probes from the 1M array that map to exons missed by our design parameters were also included. Our custom array has half the probes (415K) of the 1M (960K) and 4.5x less than the SNP6 (1.85M) but shows the best gene and exon coverage. When the minimum number of probes needed for calculating a CNA is considered, 86% of genes are interrogated compared to 81.2% (1M) and 69.3% (SNP6). The difference is even more marked at the exon level with 84% covered by at least one probe and at least 63.2% interrogated by two probes compared to 5.3% (1M) and 18.0% (SNP6). The promising in silico comparison of this exon-centered array, its more stringent design parameters in combination with significantly reducing the cost per run should make this a very promising array design for CNA analysis of tumor samples in the future Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4957.