Building a Knowledge Base for Variant Annotation Using Therapy Recommendations in cBioPortal
Molecular tumor boards present special challenges when it comes to information collection for case preparation. It is one of the most time-consuming tasks participating pathologists and oncologists face, limiting the number of cases that can be discussed in these specialized tumor boards and in turn can profit from a potential highly personalized therapy. Digital support is a necessity to enable medical professionals to efficiently make use of the vast amount of data available for each patient and their genomic and clinical profile. This includes historically recommended therapies for patients with molecularly similar tumors. To combat this issue, we developed an extension for the MTB-cBioPortal in collaboration with clinicians, enabling users to access previously documented therapy recommendations combined with corresponding follow-up data based on HL7 FHIR profiles and modules established in the Medical Informatics Initiative (MII). The information is made available through an additional annotation in the MTB-cBioPortal patient view. In doing so we intend to improve the efficiency of the case preparation process for molecular tumor boards and lay the groundwork for a potential multicentric exchange of therapy recommendations and follow-up data.
Authors
- Philipp Unberath (ORCID: https://orcid.org/0000-0002-1269-9360)
- Alexander Scheiter (ORCID: https://orcid.org/0000-0001-6734-261X)
- Dominik Boehm (ORCID: https://orcid.org/0009-0001-2887-7109)
- Niklas Reimer (ORCID: https://orcid.org/0000-0002-0491-3929)
- Jan Christoph (ORCID: https://orcid.org/0000-0003-4369-3591)
Institutions
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Universitätsklinikum Erlangen (DE)
- University Hospital Schleswig-Holstein (DE)
- University of Regensburg (DE)
- Martin Luther University Halle-Wittenberg (DE)
- University of Lübeck (DE)
Publication Details
- Journal
- Studies in health technology and informatics
- Published
- 2025-05-15
- DOI
- https://doi.org/10.3233/shti250281
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Friedrich-Alexander-Universität Erlangen-Nürnberg