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Using DNA Methylation Patterns to Infer Tumor Ancestry

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  • You Jin Hong
  • Paul Marjoram
  • Darryl Shibata
  • Kimberly D Siegmund

Abstract

Background: Exactly how human tumors grow is uncertain because serial observations are impractical. One approach to reconstruct the histories of individual human cancers is to analyze the current genomic variation between its cells. The greater the variations, on average, the greater the time since the last clonal evolution cycle (“a molecular clock hypothesis”). Here we analyze passenger DNA methylation patterns from opposite sides of 12 primary human colorectal cancers (CRCs) to evaluate whether the variation (pairwise distances between epialleles) is consistent with a single clonal expansion after transformation. Methodology/Principal Findings: Data from 12 primary CRCs are compared to epigenomic data simulated under a single clonal expansion for a variety of possible growth scenarios. We find that for many different growth rates, a single clonal expansion can explain the population variation in 11 out of 12 CRCs. In eight CRCs, the cells from different glands are all equally distantly related, and cells sampled from the same tumor half appear no more closely related than cells sampled from opposite tumor halves. In these tumors, growth appears consistent with a single “symmetric” clonal expansion. In three CRCs, the variation in epigenetic distances was different between sides, but this asymmetry could be explained by a single clonal expansion with one region of a tumor having undergone more cell division than the other. The variation in one CRC was complex and inconsistent with a simple single clonal expansion. Conclusions: Rather than a series of clonal expansion after transformation, these results suggest that the epigenetic variation of present-day cancer cells in primary CRCs can almost always be explained by a single clonal expansion.

Suggested Citation

  • You Jin Hong & Paul Marjoram & Darryl Shibata & Kimberly D Siegmund, 2010. "Using DNA Methylation Patterns to Infer Tumor Ancestry," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-9, August.
  • Handle: RePEc:plo:pone00:0012002
    DOI: 10.1371/journal.pone.0012002
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    References listed on IDEAS

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    1. Lacey Michelle R & Ehrlich Melanie, 2009. "Modeling Dependence in Methylation Patterns with Application to Ovarian Carcinomas," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-29, September.
    2. Siegmund Kimberly D. & Marjoram Paul & Shibata Darryl, 2008. "Modeling DNA Methylation in a Population of Cancer Cells," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-23, June.
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    Cited by:

    1. Watal M Iwasaki & Hideki Innan, 2017. "Simulation framework for generating intratumor heterogeneity patterns in a cancer cell population," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-28, September.

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