gms | German Medical Science

64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

08. - 11.09.2019, Dortmund

Enrichment of Patient Cohorts in Precision Medicine by Public Data

Meeting Abstract

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  • Katrin Glocker - Deutsches Krebsforschungszentrum, Heidelberg, Germany
  • Alexander Knurr - Deutsches Krebsforschungszentrum, Heidelberg, Germany
  • Julia Dieter - Deutsches Krebsforschungszentrum, Heidelberg, Germany
  • Frank Ückert - Deutsches Krebsforschungszentrum, Heidelberg, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Dortmund, 08.-11.09.2019. Düsseldorf: German Medical Science GMS Publishing House; 2019. DocAbstr. 245

doi: 10.3205/19gmds023, urn:nbn:de:0183-19gmds0232

Veröffentlicht: 6. September 2019

© 2019 Glocker et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe



Introduction: The ideal of implementing precision medicine for every oncological patient according to special genetic and clinical characteristics leads to a drastic shrinkage of patient cohorts with similar characteristics, which can even be as small as single patients at one site [1]. As comparing groups is an important tool in clinical care to support therapy decisions, researchers and physicians worldwide need to collaborate. However, these collaborations get complicated by various ways and formats of documenting both clinical and genetic data, as there is no unified standard. Consortia like the International Cancer Genome Consortium (ICGC) tackle this problem by proposing a standard and inviting researchers around the world to share data on various oncological diseases [2].

Methods: To prove the concept, data from a project of ICGC focusing on malignant lymphoma (MALY) and data from the MASTER (Molecularly Aided Stratification for Tumor Eradication) program at the National Center for Tumor Diseases (NCT) in Heidelberg were mapped to an in-house developed clinical data model (CDM). Based on this, both data sources were integrated into the local data warehouse, enabling joint data analysis.

Results: The data integration into the local data warehouse was successfully conducted and data were available for analysis. While there are only two patients with follicular lymphoma included in the MASTER project, it was possible to enlarge the patient cohort by the complementation with 107 ICGC patients with the same diagnosis. The verification of findings made for the two patients in the bigger cohort can improve the significance of hypotheses. While clinical data provided by ICGC is restricted on few data elements the amount of genetic data can bring a huge benefit. The genetic profile of the two patients included in the MASTER project can be compared with the big ICGC cohort.

Discussion: With data available from public sources, own data can be put into a broader context, as new hypotheses can be developed and verified. Correlations of genetic or clinical data found in few patients at one site can turn out to be coincidental. However, these correlations can also prove to be a starting point to a new hypothesis for new treatments or a better understanding of a disease. As the MASTER project does not focus on a specific cancer type, the integration of data from other projects would increase the benefit for the physicians. Furthermore, there are more consortia and projects sharing genomic data in a clinical context with scientific partners in a similar way to ICGC. Data from sources such as The Cancer Genome Atlas (TCGA) Program or the Genomics Evidence Neoplasia Information Exchange (GENIE) can further extend the number of patients in one system [3], [4]. With more data accumulated, hypotheses can be strengthened and theories verified. Thus, we want to support clinical researchers by offering a fast way of finding information that benefits patients with rare diseases and supports precision medicine.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.


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