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Biostatistics Advance Access published online on January 9, 2006
Biostatistics, doi:10.1093/biostatistics/kxj015
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Received September 27, 2005
Revised December 5, 2005
Accepted January 4, 2006

Article

A Pseudolikelihood Approach for Simultaneous Analysis of Array Comparative Genomic Hybridizations (aCGH)

David A. Engler 1, Gayatry Mohapatra 2, David N. Louis 3, and Rebecca A. Betensky 1 *

1 Department of Biostatistics, Harvard University, 655 Huntington Avenue, Boston, MA 02115
2 Massachusetts General Hospital, Department of Pathology, CNY-7015, 149 13th Street, Charlestown, MA 02129
3 Molecular Neuro-Oncology Laboratory and Molecular Pathology Unit, Departments of Pathology, Cancer Center and Neurosurgical Service, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114

* To whom correspondence should be addressed.
Rebecca A. Betensky, E-mail: betensky{at}hsph.harvard.edu


  Abstract

DNA sequence copy number has been shown to be associated with cancer development and progression. Array-based Comparative Genomic Hybridization (aCGH) is a recent development that seeks to identify the copy number ratio at large numbers of markers across the genome. Due to experimental and biological variations across chromosomes and across hybridizations, current methods are limited to analyses of single chromosomes. We propose a more powerful approach that borrows strength across chromosomes and across hybridizations. We assume a Gaussian mixture model, with a hidden Markov dependence structure, and with random effects to allow for intertumoral variation, as well as intratumoral clonal variation. For ease of computation, we base estimation on a pseudolikelihood function. The method produces quantitative assessments of the likelihood of genetic alterations at each clone, along with a graphical display for simple visual interpretation. We assess the characteristics of the method through simulation studies and through analysis of a brain tumor aCGH data set. We show that the pseudolikelihood approach is superior to existing methods both in detecting small regions of copy number alteration and in accurately classifying regions of change when intratumoral clonal variation is present. Software for this approach is available at http://www.biostat.harvard.edu/~betensky/papers.html.


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