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Ancestral inference in tumors: how much can we know?
Zhao, Junsong; Siegmund, Kimberly D; Shibata, Darryl; Marjoram, Paul.
  • Zhao J; Department of Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA. Electronic address: junsongz@usc.edu.
  • Siegmund KD; Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA. Electronic address: kims@usc.edu.
  • Shibata D; Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA. Electronic address: dshibata@usc.edu.
  • Marjoram P; Department of Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA; Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA. Electronic address: pmarjora@usc.edu.
J Theor Biol ; 359: 136-45, 2014 Oct 21.
Article en En | MEDLINE | ID: mdl-24907673
ABSTRACT
A tumor is thought to start from a single cell and genome. Yet genomes in the final tumor are typically heterogeneous. The mystery of this intratumoral heterogeneity (ITH) has not yet been uncovered, but much of this ITH may be secondary to replication errors. Methylation of cytosine bases often exhibits ITH and therefore may encode the ancestry of the tumor. In this study, we measure the passenger methylation patterns of a specific CpG region in 9 colorectal tumors by bisulfite sequencing and apply a tumor development model. Based on our model, we are able to retrieve information regarding the ancestry of each tumor using approximate Bayesian computation. With a large simulation study we explore the conditions under which we can estimate the model parameters, and the initial state of the first transformed cell. Finally we apply our analysis to clinical data to gain insight into the dynamics of tumor formation.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Evolución Molecular / Biología Computacional / Neoplasias Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Año: 2014 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Evolución Molecular / Biología Computacional / Neoplasias Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Año: 2014 Tipo del documento: Article