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Bioinformatics Advance Access published online on October 22, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp604
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© The Author (2009). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Identification of Non-Hodgkin's Lymphoma Prognosis Signatures Using the CTGDR Method

Shuangge Ma 1,*, Yawei Zhang 1, Jian Huang 2, Xuesong Han 1, Theodore Holford 1, Qing Lan 3, Nathaniel Rothman 3, Peter Boyle 4 and Tongzhang Zheng 1

1School of Public Health, Yale University, New Haven, CT 06510
2Department of Statistics and Actuarial Science, University of Iowa, Iowa City, IA 52242
3Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD 20892
4International Prevention Research Institute, Lyon, France

*To whom correspondence should be addressed. Dr. Shuangge Ma, E-mail: shuangge.ma{at}yale.edu


  Abstract

Motivation: Although NHL (Non-Hodgkin's lymphoma) is the fifth leading cause of cancer incidence and mortality in the US, it remains poorly understood and is largely incurable. Biomedical studies have shown that genomic variations, measured with SNPs (single nucleotide polymorphisms) in genes, may have independent predictive power for disease free survival in NHL patients beyond clinical measurements.

Results: We apply the CTGDR (Clustering Threshold Gradient Directed Regularization) method to genetic association studies using SNPs, analyze data from an association study of NHL, and identify prognosis signatures for diffuse large B cell lymphoma (DLBCL) and follicular lymphoma (FL), the two most common subtypes of NHL. With the CTGDR method, we are able to account for the joint effects of multiple genes/SNPs, whereas most existing studies are single-marker based. In addition, we are able to account for the "gene and SNPwithin-gene" hierarchical structure and identify not only predictive genes but also predictive SNPs within identified genes. In contrast, existing studies are limited to either gene or SNP identification, but not both. We propose using resampling methods to evaluate the predictive power and reproducibility of identified genes and SNPs. Simulation study and data analysis suggest satisfactory performance of the CTGDR method.

Contact: shuangge.ma{at}yale.edu

Associate Editor: Prof. John Quackenbush

Received on May 1, 2009; revised on September 28, 2009; accepted on October 16, 2009

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