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Breast cancer gene expression datasets do not reflect the disease at the population level

Authors
  • Xie, Yanping1, 2
  • Davis Lynn, Brittny C.3
  • Moir, Nicholas1
  • Cameron, David A.4
  • Figueroa, Jonine D.1, 2
  • Sims, Andrew H.1
  • 1 Applied Bioinformatics of Cancer, University of Edinburgh Cancer Research Centre, MRC Institute of Genetics and Molecular Medicine, Edinburgh, UK , Edinburgh (United Kingdom)
  • 2 Usher Institute, University of Edinburgh, Edinburgh, UK , Edinburgh (United Kingdom)
  • 3 Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA , Bethesda (United States)
  • 4 NHS Research Scotland Cancer Lead and Cancer Research UK Edinburgh Centre, MRC Institute of Genetics & Molecular Medicine, The University of Edinburgh, Edinburgh, UK , Edinburgh (United Kingdom)
Type
Published Article
Journal
npj Breast Cancer
Publisher
Nature Publishing Group UK
Publication Date
Aug 25, 2020
Volume
6
Issue
1
Identifiers
DOI: 10.1038/s41523-020-00180-x
Source
Springer Nature
License
Green

Abstract

Publicly available tumor gene expression datasets are widely reanalyzed, but it is unclear how representative they are of clinical populations. Estimations of molecular subtype classification and prognostic gene signatures were calculated for 16,130 patients from 70 breast cancer datasets. Collated patient demographics and clinical characteristics were sparse for many studies. Considerable variations were observed in dataset size, patient/tumor characteristics, and molecular composition. Results were compared with Surveillance, Epidemiology, and End Results Program (SEER) figures. The proportion of basal subtype tumors ranged from 4 to 59%. Date of diagnosis ranged from 1977 to 2013, originating from 20 countries across five continents although European ancestry dominated. Publicly available breast cancer gene expression datasets are a great resource, but caution is required as they tend to be enriched for high grade, ER-negative tumors from European-ancestry patients. These results emphasize the need to derive more representative and annotated molecular datasets from diverse populations.

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