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The Knowledge Connector decision support system for multiomics-based precision oncology

Precision cancer medicine aims to improve patient outcomes by providing individually tailored recommendations for clinical management based on the evaluation of biological disease profiles in multidisciplinary molecular tumor boards (MTBs). The quality of MTB decisions depends on the comprehensive,...

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Bibliographische Detailangaben
Hauptverfasser: Hübschmann, Daniel (VerfasserIn) , Kreutzfeldt, Simon (VerfasserIn) , Roth, Benjamin (VerfasserIn) , Glocker, Katrin (VerfasserIn) , Schoop, Janine (VerfasserIn) , Oeser, Lena (VerfasserIn) , Hausmann, Steffen (VerfasserIn) , Koch, Christian (VerfasserIn) , Uhrig, Sebastian (VerfasserIn) , Hüllein, Jennifer (VerfasserIn) , Hutter, Barbara (VerfasserIn) , Fröhlich, Martina (VerfasserIn) , Heilig, Christoph E. (VerfasserIn) , Teleanu, Maria-Veronica (VerfasserIn) , Lipka, Daniel (VerfasserIn) , Kerle, Irina A. (VerfasserIn) , Baude-Müller, Annika (VerfasserIn) , Beck, Katja (VerfasserIn) , Heining, Christoph (VerfasserIn) , Glimm, Hanno (VerfasserIn) , Ückert, Frank (VerfasserIn) , Knurr, Alexander (VerfasserIn) , Fröhling, Stefan (VerfasserIn) , Horak, Peter (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 2026
In: Nature Communications
Year: 2026, Jahrgang: 17, Pages: 1-12
ISSN:2041-1723
DOI:10.1038/s41467-026-68333-3
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41467-026-68333-3
Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41467-026-68333-3
Volltext
Verfasserangaben:Daniel Hübschmann, Simon Kreutzfeldt, Benjamin Roth, Katrin Glocker, Janine Schoop, Lena Oeser, Steffen Hausmann, Christian Koch, Sebastian Uhrig, Jennifer Hüllein, Barbara Hutter, Martina Fröhlich, Christoph E. Heilig, Maria-Veronica Teleanu, Daniel B. Lipka, Irina A. Kerle, Annika Baude-Müller, Katja Beck, Christoph Heining, Hanno Glimm, Frank Ückert, Alexander Knurr, Stefan Fröhling & Peter Horak
Beschreibung
Zusammenfassung:Precision cancer medicine aims to improve patient outcomes by providing individually tailored recommendations for clinical management based on the evaluation of biological disease profiles in multidisciplinary molecular tumor boards (MTBs). The quality of MTB decisions depends on the comprehensive, reliable, and reproducible interpretation of increasingly complex molecular data. We developed and implemented, as part of a multicenter precision oncology program, the Knowledge Connector (KC), a decision support system that integrates individual patients’ molecular and clinical data with world knowledge to generate and document MTB recommendations. The KC supports data curation, database integration, and discussion based on multiomics data and provides an interface for creating a cross-institutional knowledge base. Furthermore, it extracts relevant biomarker-drug associations and increases the efficacy of data interpretation in a clinically relevant manner by reducing reliance on external sources and optimizing inter-curator concordance. Our results demonstrate that the KC is a versatile tool that supports medical decision-making in MTBs, thus enabling the scalability of precision cancer medicine.
Beschreibung:Online veröffentlicht: 19. Januar 2026
Gesehen am 25.03.2026
Beschreibung:Online Resource
ISSN:2041-1723
DOI:10.1038/s41467-026-68333-3

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