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Knowledge bases and software support for variant interpretation in precision oncology

  • Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools.Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.show moreshow less

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Author details:Florian BorchertGND, Andreas MockORCiDGND, Aurelie TomczakGND, Jonas HügelORCiD, Samer AlkarkouklyORCiD, Alexander KnurrGND, Anna-Lena VolckmarORCiDGND, Albrecht StenzingerORCiDGND, Peter SchirmacherORCiDGND, Jürgen DebusORCiDGND, Dirk Jäger, Thomas LongerichORCiDGND, Stefan FröhlingORCiDGND, Roland EilsORCiDGND, Nina BougatfORCiDGND, Ulrich SaxORCiDGND, Matthieu-Patrick SchapranowORCiDGND
DOI:https://doi.org/10.1093/bib/bbab134
ISSN:1467-5463
ISSN:1477-4054
Pubmed ID:https://pubmed.ncbi.nlm.nih.gov/33971666
Title of parent work (English):Briefings in bioinformatics
Publisher:Oxford Univ. Press
Place of publishing:Oxford
Publication type:Article
Language:English
Date of first publication:2021/05/10
Publication year:2021
Release date:2024/01/18
Tag:HiGHmed; cancer therapy; data integration; molecular tumor board; personalized medicine
Volume:22
Issue:6
Article number:bbab134
Number of pages:17
Funding institution:German Federal Ministry of Research and Education [01ZZ1802]; Physician-Scientist Program of the University of Heidelberg, Faculty of Medicine, DKTK (German Cancer Consortium) School of Oncology; Cancer Core Europe TRYTRAC program; MTB-Report project (VolkswagenStiftung) [ZN3424]
Organizational units:An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH
DDC classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Peer review:Referiert
Publishing method:Open Access / Hybrid Open-Access
License (German):License LogoCC-BY - Namensnennung 4.0 International
External remark:Correction: https://doi.org/10.1093/bib/bbab246
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