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

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,...

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Bibliographic Details
Published in:Scientific reports 2019-06, Vol.9 (1), p.8770-14, Article 8770
Main Authors: Gendoo, Deena M. A., Zon, Michael, Sandhu, Vandana, Manem, Venkata S. K., Ratanasirigulchai, Natchar, Chen, Gregory M., Waldron, Levi, Haibe-Kains, Benjamin
Format: Article
Language:English
Subjects:
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631/114/2401
692/53/2422
Biomarkers, Tumor - genetics
Biomarkers, Tumor - metabolism
Breast cancer
Breast Neoplasms - genetics
Breast Neoplasms - metabolism
Data storage
Databases, Nucleic Acid
Female
Humanities and Social Sciences
Humans
Male
Metadata
multidisciplinary
Oncology
Ovarian cancer
Ovarian Neoplasms - genetics
Ovarian Neoplasms - metabolism
Pancreatic cancer
Pancreatic Neoplasms - genetics
Pancreatic Neoplasms - metabolism
Science
Science (multidisciplinary)
Solid tumors
Transcriptome
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-45165-4