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Bioinformatics Advance Access originally published online on October 14, 2008
Bioinformatics 2008 24(24):2880-2886; doi:10.1093/bioinformatics/btn536
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Reconstructing tumor-wise protein expression in tissue microarray studies using a Bayesian cell mixture model

Ronglai Shen 1,*, Jeremy M. G. Taylor 2 and Debashis Ghosh 3

1Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, 2Department of Statistics and Huck Institute of Life Sciences, Penn State University, University Park, PA and 3Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Tissue microarrays (TMAs) quantify tissue-specific protein expression of cancer biomarkers via high-density immuno-histochemical staining assays. Standard analysis approach estimates a sample mean expression in the tumor, ignoring the complex tissue-specific staining patterns observed on tissue arrays.

Methods: In this article, a cell mixture model (CMM) is proposed to reconstruct tumor expression patterns in TMA experiments. The concept is to assemble the whole-tumor expression pattern by aggregating over the subpopulation of tissue specimens sampled by needle biopsies. The expression pattern in each individual tissue element is assumed to be a zero-augmented Gamma distribution to assimilate the non-staining areas and the staining areas. A hierarchical Bayes model is imposed to borrow strength across tissue specimens and across tumors. A joint model is presented to link the CMM expression model with a survival model for censored failure time observations. The implementation involves imputation steps within each Markov chain Monte Carlo iteration and Monte Carlo integration technique.

Results: The model-based approach provides estimates for various tumor expression characteristics including the percentage of staining, mean intensity of staining and a composite meanstaining to associate with patient survival outcome.

Availability: R package to fit CMM model is available at http://www.mskcc.org/mskcc/html/85130.cfm

Contact: shenr{at}mskcc.org

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: David Rocke

Received on May 8, 2008; revised on September 19, 2008; accepted on October 11, 2008

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