MetaGxData: Clinically annotated breast, ovarian and pancreatic cancer datasets and their use in generating a multi-cancer gene signature

Research output: Contribution to journalArticle

Authors

  • Michael Zon
  • Vandana Sandhu
  • Venkata S K Manem
  • Natchar Ratanasirigulchai
  • Gregory M Chen
  • Levi Waldron
  • Benjamin Haibe-Kains

Colleges, School and Institutes

External organisations

  • Centre for Computational Biology, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, United Kingdom. d.gendoo@bham.ac.uk.
  • Department of Biomedical Engineering, McMaster University, Toronto, L8S 4L8, Canada.
  • Princess Margaret Cancer Center, University Health Network, Toronto, M5G 2C1, Canada.
  • Institut Universitaire de Cardiologie et de Pneumologie de Québec, Université Laval, Québec City, G1V 4G5, Canada.
  • 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.
  • 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.

Details

Original languageEnglish
Article number8770
JournalScientific Reports
Volume9
Issue number1
Publication statusPublished - 19 Jun 2019