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@ARTICLE{Borchert:168775,
      author       = {F. Borchert and A. Mock and A. Tomczak and J. Hügel and S.
                      Alkarkoukly$^*$ and A. Knurr$^*$ and A.-L. Volckmar and A.
                      Stenzinger and P. Schirmacher$^*$ and J. Debus$^*$ and D.
                      Jäger$^*$ and T. Longerich and S. Fröhling$^*$ and R. Eils
                      and N. Bougatf and U. Sax$^*$ and M.-P. Schapranow},
      title        = {{K}nowledge bases and software support for variant
                      interpretation in precision oncology.},
      journal      = {Briefings in bioinformatics},
      volume       = {22},
      number       = {6},
      issn         = {1477-4054},
      address      = {Oxford [u.a.]},
      publisher    = {Oxford University Press},
      reportid     = {DKFZ-2021-01056},
      pages        = {bbab134},
      year         = {2021},
      note         = {2021 Nov 5;22(6):bbab134 / 319H},
      abstract     = {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.},
      keywords     = {HiGHmed (Other) / cancer therapy (Other) / data integration
                      (Other) / molecular tumor board (Other) / personalized
                      medicine (Other)},
      cin          = {M130 / E050 / D120 / HD01 / B340},
      ddc          = {004},
      cid          = {I:(DE-He78)M130-20160331 / I:(DE-He78)E050-20160331 /
                      I:(DE-He78)D120-20160331 / I:(DE-He78)HD01-20160331 /
                      I:(DE-He78)B340-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33971666},
      doi          = {10.1093/bib/bbab134},
      url          = {https://inrepo02.dkfz.de/record/168775},
}