Keywords
Breast cancer, gene-expression profiling, race, immunohistochemistry status, disease subtypes, triple negative breast cancer, health disparities
Abstract
Scholarly requirements have led to a massive increase of transcriptomic data in the public domain, with millions of samples available for secondary research. We identified gene-expression datasets representing 10,214 breast-cancer patients in public databases. We focused on datasets that included patient metadata on race and/or immunohistochemistry (IHC) profiling of the ER, PR, and HER-2 proteins. This review provides a summary of these datasets and describes findings from 32 research articles associated with the datasets. These studies have helped to elucidate relationships between IHC, race, and/or treatment options, as well as relationships between IHC status and the breast-cancer intrinsic subtypes. We have also identified broad themes across the analysis methodologies used in these studies, including breast cancer subtyping, deriving predictive biomarkers, identifying differentially expressed genes, and optimizing data processing. Finally, we discuss limitations of prior work and recommend future directions for reusing these datasets in secondary analyses.
Original Publication Citation
Ifeanyichukwu O. Nwosu & Stephen R. Piccolo (2021) A systematic review of datasets that can help elucidate relationships among gene expression, race, and immunohistochemistry-defined subtypes in breast cancer, Cancer Biology & Therapy, 22:7-9, 417-429, DOI: 10.1080/15384047.2021.1953902
BYU ScholarsArchive Citation
Nwosu, Ifeanyichukwu O. and Piccolo, Stephen, "A Systematic Review of Datasets that Can Help Elucidate Relationships Among Gene Expression, Race, and Immunohistochemistry-Defined Subtypes in Breast Cancer." (2021). Faculty Publications. 7346.
https://scholarsarchive.byu.edu/facpub/7346
Document Type
Peer-Reviewed Article
Publication Date
2021-08-19
Publisher
Taylor & Francis
Language
English
College
Life Sciences
Department
Biology
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