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Bioinformatics Advance Access published online on October 14, 2008
Bioinformatics, 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 3 and Debashis Ghosh 2,{dagger}

1Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, U.S.A.
2Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A.
3Department of Statistics and Huck Institute of Life Sciences, Penn State University, University Park, Pennsylvania, U.S.A.

*To whom correspondence should be addressed. Dr. Ronglai Shen, E-mail: shenr{at}mskcc.org

{dagger}To whom correspondence should be addressed. Debashis Ghosh


  Abstract

Motivation: Tissue Microarrays (TMAs) quantify tissue-specific protein expression of cancer biomarkers via high-density immunohistochemical 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 MCMC 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 mean staining 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

Associate Editor: Prof. David Rocke

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

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