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Open Access Primary research

Analysing breast cancer microarrays from African Americans using shrinkage-based discriminant analysis

Herbert Pang1, Keita Ebisu2, Emi Watanabe3, Laura Y Sue4 and Tiejun Tong56*

  • * Corresponding author: Tiejun Tong

Author Affiliations

1 Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710, USA

2 School of Forestry & Environmental Studies, Yale University, New Haven, CT 06511, USA

3 Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06510, USA

4 Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20892, USA

5 Department of Applied Mathematics, University of Colorado, Boulder, CO 80309, USA

6 Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong

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Human Genomics 2010, 5:5-16  doi:10.1186/1479-7364-5-1-5

Published: 1 October 2010

Abstract

Breast cancer tumours among African Americans are usually more aggressive than those found in Caucasian populations. African-American patients with breast cancer also have higher mortality rates than Caucasian women. A better understanding of the disease aetiology of these breast cancers can help to improve and develop new methods for cancer prevention, diagnosis and treatment. The main goal of this project was to identify genes that help differentiate between oestrogen receptor-positive and -negative samples among a small group of African-American patients with breast cancer. Breast cancer microarrays from one of the largest genomic consortiums were analysed using 13 African-American and 201 Caucasian samples with oestrogen receptor status. We used a shrinkage-based classification method to identify genes that were informative in discriminating between oestrogen receptor-positive and -negative samples. Subset analysis and permutation were performed to obtain a set of genes unique to the African-American population. We identified a set of 156 probe sets, which gave a misclassification rate of 0.16 in distinguishing between oestrogen receptor-positive and -negative patients. The biological relevance of our findings was explored through literature-mining techniques and pathway mapping. An independent dataset was used to validate our findings and we found that the top ten genes mapped onto this dataset gave a misclassification rate of 0.15. The described method allows us best to utilise the information available from small sample size microarray data in the context of ethnic minorities.

Keywords:
African Americans; breast cancer; discriminant analysis; oestrogen receptor; health disparities; microarrays