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


Authors: Shen, R.; Taylor, J. M. G.; Ghosh, D.
Article Title: Reconstructing tumor-wise protein expression in tissue microarray studies using a Bayesian cell mixture model
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. © The Author 2008. Published by Oxford University Press. All rights reserved.
Keywords: cancer survival; controlled study; human tissue; protein expression; protein array analysis; major clinical study; neoplasms; biological marker; biological markers; bayes theorem; markov chains; neoplasm proteins; proteomics; tumor marker; prostate cancer; simulation; gene expression regulation; probability; cancer cell; needle biopsy; staining; tissue array analysis; tissue microarray; mathematical computing; monte carlo method
Journal Title: Bioinformatics
Volume: 24
Issue: 24
ISSN: 1367-4803
Publisher: Oxford University Press  
Date Published: 2008-12-01
Start Page: 2880
End Page: 2886
Language: English
DOI: 10.1093/bioinformatics/btn536
PUBMED: 18922808
PROVIDER: scopus
PMCID: PMC4505790
DOI/URL:
Notes: --- - "Cited By (since 1996): 2" - "Export Date: 17 November 2011" - "CODEN: BOINF" - "Source: Scopus"
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  1. Ronglai Shen
    205 Shen