Literature DB >> 33971666

Knowledge bases and software support for variant interpretation in precision oncology.

Florian Borchert1, Andreas Mock2,3, Aurelie Tomczak4,5, Jonas Hügel6,7, Samer Alkarkoukly8, Alexander Knurr9, Anna-Lena Volckmar4, Albrecht Stenzinger4, Peter Schirmacher4,5, Jürgen Debus10,11,12,13,14, Dirk Jäger3,15, Thomas Longerich4,5, Stefan Fröhling2,16, Roland Eils17,18, Nina Bougatf10,11,12,13,14, Ulrich Sax6,7, Matthieu-P Schapranow1.   

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.
© The Author(s) 2021. Published by Oxford University Press.

Entities:  

Keywords:  HiGHmed; cancer therapy; data integration; molecular tumor board; personalized medicine

Mesh:

Year:  2021        PMID: 33971666      PMCID: PMC8574624          DOI: 10.1093/bib/bbab134

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  136 in total

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Authors:  Mark Starr; Iain Chalmers; Mike Clarke; Andrew D Oxman
Journal:  Int J Technol Assess Health Care       Date:  2009-06-18       Impact factor: 2.188

2.  Proteomics. Tissue-based map of the human proteome.

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Journal:  Science       Date:  2015-01-23       Impact factor: 47.728

3.  UMD (Universal mutation database): a generic software to build and analyze locus-specific databases.

Authors:  C Béroud; G Collod-Béroud; C Boileau; T Soussi; C Junien
Journal:  Hum Mutat       Date:  2000       Impact factor: 4.878

4.  Molecular tumor board: the University of California-San Diego Moores Cancer Center experience.

Authors:  Maria Schwaederle; Barbara A Parker; Richard B Schwab; Paul T Fanta; Sarah G Boles; Gregory A Daniels; Lyudmila A Bazhenova; Rupa Subramanian; Alice C Coutinho; Haydee Ojeda-Fournier; Brian Datnow; Nicholas J Webster; Scott M Lippman; Razelle Kurzrock
Journal:  Oncologist       Date:  2014-05-05

Review 5.  Data resources for the identification and interpretation of actionable mutations by clinicians.

Authors:  A Prawira; T J Pugh; T L Stockley; L L Siu
Journal:  Ann Oncol       Date:  2017-05-01       Impact factor: 32.976

6.  DrugBank 5.0: a major update to the DrugBank database for 2018.

Authors:  David S Wishart; Yannick D Feunang; An C Guo; Elvis J Lo; Ana Marcu; Jason R Grant; Tanvir Sajed; Daniel Johnson; Carin Li; Zinat Sayeeda; Nazanin Assempour; Ithayavani Iynkkaran; Yifeng Liu; Adam Maciejewski; Nicola Gale; Alex Wilson; Lucy Chin; Ryan Cummings; Diana Le; Allison Pon; Craig Knox; Michael Wilson
Journal:  Nucleic Acids Res       Date:  2018-01-04       Impact factor: 16.971

7.  Diagnosis of fusion genes using targeted RNA sequencing.

Authors:  Erin E Heyer; Ira W Deveson; Danson Wooi; Christina I Selinger; Ruth J Lyons; Vanessa M Hayes; Sandra A O'Toole; Mandy L Ballinger; Devinder Gill; David M Thomas; Tim R Mercer; James Blackburn
Journal:  Nat Commun       Date:  2019-03-27       Impact factor: 14.919

8.  A virtual molecular tumor board to improve efficiency and scalability of delivering precision oncology to physicians and their patients.

Authors:  Michael J Pishvaian; Edik M Blais; R Joseph Bender; Shruti Rao; Simina M Boca; Vincent Chung; Andrew E Hendifar; Sam Mikhail; Davendra P S Sohal; Paula R Pohlmann; Kathleen N Moore; Kai He; Bradley J Monk; Robert L Coleman; Thomas J Herzog; David D Halverson; Patricia DeArbeloa; Emanuel F Petricoin; Subha Madhavan
Journal:  JAMIA Open       Date:  2019-10-07

Review 9.  Next-generation sequencing to guide cancer therapy.

Authors:  Jeffrey Gagan; Eliezer M Van Allen
Journal:  Genome Med       Date:  2015-07-29       Impact factor: 11.117

10.  LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC.

Authors:  Alexis Allot; Yifan Peng; Chih-Hsuan Wei; Kyubum Lee; Lon Phan; Zhiyong Lu
Journal:  Nucleic Acids Res       Date:  2018-07-02       Impact factor: 16.971

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  1 in total

1.  Controversial Trials First: Identifying Disagreement Between Clinical Guidelines and New Evidence.

Authors:  Florian Borchert; Laura Meister; Thomas Langer; Markus Follmann; Bert Arnrich; Matthieu-P Schapranow
Journal:  AMIA Annu Symp Proc       Date:  2022-02-21
  1 in total

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