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

TitleAnalysing breast cancer microarrays from African Americans using shrinkage-based discriminant analysis.
Publication TypeJournal Article
Year of Publication2010
AuthorsPang, Herbert, Keita Ebisu, Emi Watanabe, Laura Y. Sue, and Tiejun Tong
JournalHum Genomics
Volume5
Issue1
Pagination5-16
Date Published2010 Oct
ISSN1479-7364
KeywordsAfrican Americans, Algorithms, Biomarkers, Tumor, Breast Neoplasms, Data Mining, Discriminant Analysis, Female, Genomics, Humans, Protein Array Analysis, Receptors, Estrogen, Whites
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.

DOI10.1186/1479-7364-5-1-5
Alternate JournalHum Genomics
Original PublicationAnalysing breast cancer microarrays from African Americans using shrinkage-based discriminant analysis.
PubMed ID21106486
PubMed Central IDPMC3042882
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
P01CA142538 / CA / NCI NIH HHS / United States
Project: