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Digital Karyotyping

An Update of its Applications in Cancer

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Abstract

DNA copy number alterations, including entire chromosomal changes and small interstitial DNA amplifications and deletions, characterize the development of cancer. These changes usually affect the expression of target genes and subsequently the function of the target proteins. Since the completion of the human genome project, the capacity to comprehensively analyze the human cancer genome has expanded significantly. Techniques such as digital karyotyping have been developed to allow for the detection of DNA copy number alterations in cancer at the whole-genome scale. When compared with conventional methods such as spectral karyotyping, representational difference analysis, comparative genomic hybridization (CGH), or the more recent array CGH; digital karyotyping provides an evaluation of copy number of genetic material at higher resolution. Digital karyotyping has therefore promised to enhance our understanding of the cancer genome. This article provides an overview of digital karyotyping including the principle of the technology and its applications in identifying potential oncogenes and tumor suppressor genes.

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Fig. 1
Table I
Table II

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Acknowledgements

This study was supported by the US Department of Defense grant (W81XWH-05-1-0004).

The authors have no conflicts of interest that are directly relevant to the content of this review.

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Authors and Affiliations

  1. Department of Gynecology, Johns Hopkins University School of Medicine, 1550, Orleans Street, Room: 306, Baltimore, MD, 21231, USA

    Ritu Salani &  Tian-Li Wang

  2. Department of Oncology, Johns Hopkins University School of Medicine, 1550, Orleans Street, Room: 306, Baltimore, MD, 21231, USA

    Ritu Salani, Chao-Ling Chang, Leslie Cope &  Tian-Li Wang

Authors
  1. Ritu Salani
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  2. Chao-Ling Chang
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  3. Leslie Cope
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  4. Tian-Li Wang
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Corresponding author

Correspondence to Tian-Li Wang.

Appendix

Appendix

1. Method for Table I and Table II

In order to identify DNA copy number alterations, we slide a window along each chromosome, looking for places where the sum of tag counts within a window is higher or lower than expected by chance alone. If we assume that the tag counts have a Poisson distribution, then the sum has a Poisson distribution as well, and we can perform hypothesis tests based on that distribution. If the Poisson assumption is correct, these tests are equivalent to the permutation tests usually used for analysis of digital karyotyping data. As shown in table I, we compared the results produced by Poisson test with the results from Monte Carlo simulation in the original digital karyotyping paper.[1] They appear consistent when requiring the PPV to be >95%. We include a p-value adjustment for the multiple tests that arise when scanning the genome and the exact value was determined by extensive simulation.

If there is exactly one mutation on a chromosome, the PPV can be approximated as power/(α + power), where a represents a type I error. We set power to be 0.95 and a to be 0.05, so the PPV will be approximately 0.95 in each cell of the table. More mutations will lead to a higher PPV, so this is in some sense, the most stringent criteria. The calculation was based on a total of 842 202 virtual tags deduced from the entire human genome, and for convenience, an ‘average’ chromosome of 40 000 tags were used for all the calculations, although results may vary slightly between chromosomes of different lengths.

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Salani, R., Chang, CL., Cope, L. et al. Digital Karyotyping. Mol Diag Ther 10, 231–237 (2006). https://doi.org/10.1007/BF03256461

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