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Sci Rep. 2019 Jun 19;9(1):8770. doi: 10.1038/s41598-019-45165-4.

MetaGxData: Clinically Annotated Breast, Ovarian and Pancreatic Cancer Datasets and their Use in Generating a Multi-Cancer Gene Signature.

Author information

1
Centre for Computational Biology, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, United Kingdom. d.gendoo@bham.ac.uk.
2
Princess Margaret Cancer Center, University Health Network, Toronto, M5G 2C1, Canada.
3
Department of Biomedical Engineering, McMaster University, Toronto, L8S 4L8, Canada.
4
Department of Medical Biophysics, University of Toronto, Toronto, M5S 3H7, Canada.
5
Institut Universitaire de Cardiologie et de Pneumologie de Québec, Université Laval, Québec City, G1V 4G5, Canada.
6
Graduate School of Public Health and Health Policy, Institute of Implementation Science in Population Health, City University of New York School, New York, 11101, USA. levi.waldron@hunter.cuny.edu.
7
Princess Margaret Cancer Center, University Health Network, Toronto, M5G 2C1, Canada. benjamin.haibe.kains@utoronto.ca.
8
Department of Medical Biophysics, University of Toronto, Toronto, M5S 3H7, Canada. benjamin.haibe.kains@utoronto.ca.
9
Department of Computer Science, University of Toronto, Toronto, M5T 3A1, Canada. benjamin.haibe.kains@utoronto.ca.
10
Ontario Institute of Cancer Research, Toronto, M5G 0A3, Canada. benjamin.haibe.kains@utoronto.ca.
11
Vector Institute, Toronto, M5G 1M1, Canada. benjamin.haibe.kains@utoronto.ca.

Abstract

A wealth of transcriptomic and clinical data on solid tumours are under-utilized due to unharmonized data storage and format. We have developed the MetaGxData package compendium, which includes manually-curated and standardized clinical, pathological, survival, and treatment metadata across breast, ovarian, and pancreatic cancer data. MetaGxData is the largest compendium of curated transcriptomic data for these cancer types to date, spanning 86 datasets and encompassing 15,249 samples. Open access to standardized metadata across cancer types promotes use of their transcriptomic and clinical data in a variety of cross-tumour analyses, including identification of common biomarkers, and assessing the validity of prognostic signatures. Here, we demonstrate that MetaGxData is a flexible framework that facilitates meta-analyses by using it to identify common prognostic genes in ovarian and breast cancer. Furthermore, we use the data compendium to create the first gene signature that is prognostic in a meta-analysis across 3 cancer types. These findings demonstrate the potential of MetaGxData to serve as an important resource in oncology research, and provide a foundation for future development of cancer-specific compendia.

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