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. 2010:2010:386870.
doi: 10.1155/2010/386870. Epub 2010 Jul 8.

Computational method for estimating DNA copy numbers in normal samples, cancer cell lines, and solid tumors using array comparative genomic hybridization

Victor Abkevich  1 , Diana Iliev, Kirsten M Timms, Thanh Tran, Mark Skolnick, Jerry S Lanchbury, Alexander Gutin
Affiliations

Computational method for estimating DNA copy numbers in normal samples, cancer cell lines, and solid tumors using array comparative genomic hybridization

Victor Abkevich et al. J Biomed Biotechnol. 2010.

Abstract

Genomic copy number variations are a typical feature of cancer. These variations may influence cancer outcomes as well as effectiveness of treatment. There are many computational methods developed to detect regions with deletions and amplifications without estimating actual copy numbers (CN) in these regions. We have developed a computational method capable of detecting regions with deletions and amplifications as well as estimating actual copy numbers in these regions. The method is based on determining how signal intensity from different probes is related to CN, taking into account changes in the total genome size, and incorporating into analysis contamination of the solid tumors with benign tissue. Hidden Markov Model is used to obtain the most likely CN solution. The method has been implemented for Affymetrix 500K GeneChip arrays and Agilent 244K oligonucleotide arrays. The results of CN analysis for normal cell lines, cancer cell lines, and tumor samples are presented. The method is capable of detecting copy number alterations in tumor samples with up to 80% contamination with benign tissue. Analysis of 178 cancer cell lines reveals multiple regions of common homozygous deletions and strong amplifications encompassing known tumor suppressor genes and oncogenes as well as novel cancer related genes.

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Figures

Figure 1
Figure 1
Comparison between median signal intensities calculated using parameters generated in this paper (y-axis) and CN estimates obtained in a different way (x-axis). For details see Table 1. Blue line represents correlation between median signal intensities and CN values used in this study.
Figure 2
Figure 2
Results of CN analysis for noncancerous cell lines from nuclear family: NA12056 from the father (a), NA12057 from the mother (b), and NA10851 from the son (c).
Figure 3
Figure 3
(a) Signal intensity of SNPs (y axis) for ovarian cancer cell line OVCAR8 before adjustment on genome size. (b) Fraction of SNPs (x axis) with a certain signal intensity (y axis) for ovarian cancer cell line OVCAR8 (see (a)). (c) CN solution for ovarian cancer cell line OVCAR8 after adjustment on genome size (k = 1.167).
Figure 4
Figure 4
Results of CN analysis of colon tumor sample (collected from male subject) significantly contaminated with benign tissue (~48%). (a) Signal intensity of SNPs (y axis) for this sample after adjustment on genome size but before adjustment on contamination with benign tissue. (b) Correct CN solution for this sample after adjustment on both genome size and contamination with benign tissue. (c) Incorrect CN solution for this sample after adjustment on genome size but without adjustment on contamination with benign tissue.
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