Development of a data model and data commons for germ cell tumors

Bo Ci, Donghan M. Yang, Mark Krailo, Caihong Xia, Bo Yao, Danni Luo, Qinbo Zhou, Guanghua Xiao, Lin Xu, Stephen X. Skapek, Matthew J. Murray, James F. Amatruda, Lindsay Klosterkemper, Furqan Shaikh, Cecile Faure-Conter, Brice Fresneau, Samuel L. Volchenboum, Sara Stoneham, Luiz Fernando Lopes, James NicholsonA. Lindsay Frazier, Yang Xie

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Germ cell tumors (GCTs) are considered a rare disease but are the most common solid tumors in adolescents and young adults, accounting for 15% of all malignancies in this age group. The rarity of GCTs in some groups, particularly children, has impeded progress in treatment and biologic understanding. The most effective GCT research will result from the interrogation of data sets from historical and prospective trials across institutions. However, inconsistent use of terminology among groups, different sample-labeling rules, and lack of data standards have hampered researchers' efforts in data sharing and across-study validation. To overcome the low interoperability of data and facilitate future clinical trials, we worked with the Malignant Germ Cell International Consortium (MaGIC) and developed a GCT clinical data model as a uniform standard to curate and harmonize GCT data sets. This data model will also be the standard for prospective data collection in future trials. Using the GCT data model, we developed a GCT data commons with data sets from both MaGIC and public domains as an integrated research platform. The commons supports functions, such as data query, management, sharing, visualization, and analysis of the harmonized data, as well as patient cohort discovery. This GCT data commons will facilitate future collaborative research to advance the biologic understanding and treatment of GCTs. Moreover, the framework of the GCT data model and data commons will provide insights for other rare disease research communities into developing similar collaborative research platforms.

Original languageEnglish (US)
Pages (from-to)555-566
Number of pages12
JournalJCO Clinical Cancer Informatics
StatePublished - 2020

ASJC Scopus subject areas

  • Oncology
  • Health Informatics
  • Cancer Research


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