Knowledge bases and software support for variant interpretation in precision oncology

Borchert, Florian; Möck, Andreas; Tomczak, Aurelie; Hügel, Jonas; Alkarkoukly, Samer; Knurr, Alexander; Volckmar, Anna‐Lena; Schirmacher, Peter; Debus, J.; Jäger, Dirk; Longerich, Thomas; Fröhling, Stefan; Eils, Roland; Bougatf, Nina; Sax, Ulrich; Schapranow, Matthieu-P.

doi:10.1093/bib/bbab246

Cited by 8 publications

(5 citation statements)

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“…This annotation may be facilitated by the use of additional public or commercial datasets, which are derived from tumor-specific population databases (e.g., COSMIC, Cancer Hotspots, cBioPortal), in-silico predictors (e.g., FATHMM, CADD) and also literature-based, manually curated databases (e.g., OnkoKB, CIVIC, JAX-CKB). 31 Automated annotation using these databases is hampered by historical inconsistencies in gene names and transcripts, inconsistent application of existing standards as well as non-HGVS annotation used by some bioinformatic tools. 32,33 In addition, the databases have to be maintained and versioned in order to be used for automated search and reporting.…”

Section: Step 2: Assessing Biological and Functional Impact Of Identi...mentioning

confidence: 99%

“…To this end, curators obtain information about a variant's effect on gene and protein function and its role within a molecular signaling pathway and the specific disease. This annotation may be facilitated by the use of additional public or commercial datasets, which are derived from tumor‐specific population databases (e.g., COSMIC, Cancer Hotspots, cBioPortal), in‐silico predictors (e.g., FATHMM, CADD) and also literature‐based, manually curated databases (e.g., OnkoKB, CIVIC, JAX‐CKB) 31 . Automated annotation using these databases is hampered by historical inconsistencies in gene names and transcripts, inconsistent application of existing standards as well as non‐HGVS annotation used by some bioinformatic tools 32,33 .…”

Section: Step 2: Assessing Biological and Functional Impact Of Identi...mentioning

confidence: 99%

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Assigning evidence to actionability: An introduction to variant interpretation in precision cancer medicine

Horak

Leichsenring

Goldschmid

et al. 2021

Genes Chromosomes & Cancer

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Modern concepts in precision cancer medicine are based on increasingly complex genomic analyses and require standardized criteria for the functional evaluation and reporting of detected genomic alterations in order to assess their clinical relevance. In this article, we propose and address the necessary steps in systematic variant evaluation consisting of bioinformatic analysis, functional annotation and clinical interpretation, focusing on the latter two aspects. We discuss the role and clinical application of current variant classification systems and point out their scope and limitations. Finally, we highlight the significance of the molecular tumor board as a platform for clinical decision-making based on genomic analyses.

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Section: Step 2: Assessing Biological and Functional Impact Of Identi...mentioning

confidence: 99%

Section: Step 2: Assessing Biological and Functional Impact Of Identi...mentioning

confidence: 99%

Assigning evidence to actionability: An introduction to variant interpretation in precision cancer medicine

Horak

Leichsenring

Goldschmid

et al. 2021

Genes Chromosomes & Cancer

Self Cite

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show abstract

“…It includes single-cell sequencing, whole exome and whole genome sequencing and other functional genomic assays data. Although open-source, germline datasets are not publicly accessible [ 85 ]. The cBioPortal has access to data from over 5000 tumor samples from 20 cancer studies [ 84 ].…”

Section: Breast Cancermentioning

confidence: 99%

A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer

Ahmad,

Ali,

Al-Jasmi

et al. 2023

Briefings in Bioinformatics

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Studies continue to uncover contributing risk factors for breast cancer (BC) development including genetic variants. Advances in machine learning and big data generated from genetic sequencing can now be used for predicting BC pathogenicity. However, it is unclear which tool developed for pathogenicity prediction is most suited for predicting the impact and pathogenicity of variant effects. A significant challenge is to determine the most suitable data source for each tool since different tools can yield different prediction results with different data inputs. To this end, this work reviews genetic variant databases and tools used specifically for the prediction of BC pathogenicity. We provide a description of existing genetic variants databases and, where appropriate, the diseases for which they have been established. Through example, we illustrate how they can be used for prediction of BC pathogenicity and discuss their associated advantages and disadvantages. We conclude that the tools that are specialized by training on multiple diverse datasets from different databases for the same disease have enhanced accuracy and specificity and are thereby more helpful to the clinicians in predicting and diagnosing BC as early as possible.

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“…Recent publications [58,59] provide comprehensive lists of variant annotation knowledge bases and bioinformatics tools for variant interpretation, biomarker identification, drug prioritisation and response prediction. All these resources were conceived as supporting tools to inform clinicians of the available treatment options for their patients.…”

Section: Genomics‐based Drug Selectionmentioning

confidence: 99%

Bioinformatics roadmap for therapy selection in cancer genomics

et al. 2022

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Knowledge bases and software support for variant interpretation in precision oncology

Cited by 8 publications

References 0 publications

Assigning evidence to actionability: An introduction to variant interpretation in precision cancer medicine

Assigning evidence to actionability: An introduction to variant interpretation in precision cancer medicine

A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer

Bioinformatics roadmap for therapy selection in cancer genomics

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