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, Jürgen; Jäger, Dirk; Longerich, Thomas; Fröhling, Stefan; Eils, Roland; Bougatf, Nina; Sax, Ulrich; Schapranow, Matthieu-P.

doi:10.1093/bib/bbab134

Cited by 15 publications

(9 citation statements)

References 146 publications

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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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Section: Genomics‐based Drug Selectionmentioning

confidence: 99%

Bioinformatics roadmap for therapy selection in cancer genomics

et al. 2022

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“…The CIViC database supports the development of computational tools for the functional prediction and interpretation of the clinical significance of cancer variants. Together with OncoKB [3] and My Cancer Genome [22], it is one of the most commonly used knowledge bases [2] for this purpose.…”

Section: Databasementioning

confidence: 99%

Transformers and the representation of biomedical background knowledge

Wysocki¹,

Zhou²,

O'Regan³

et al. 2022

Preprint

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BioBERT and BioMegatron are Transformers models adapted for the biomedical domain based on publicly available biomedical corpora. As such, they have the potential to encode large-scale biological knowledge. We investigate the encoding and representation of biological knowledge in these models, and its potential utility to support inference in cancer precision medicine -namely, the interpretation of the clinical significance of genomic alterations. We compare the performance of different transformer baselines; we use probing to determine the consistency of encodings for distinct entities; and we use clustering methods to compare and contrast the internal properties of the embeddings for genes, variants, drugs and diseases. We show that these models do indeed encode biological knowledge, although some of this is lost in fine-tuning for specific tasks. Finally, we analyse how the models behave with regard to biases and imbalances in the dataset.

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“…The challenges of clinical interpretation of genetic variants based on manual investigation of extensive literature may also be served by automated solutions (1,28,29). Because they differ in content and usability, specialists are forced to use multiple knowledge bases (29)(30)(31). Pishvaian et al (32) developed a cloud-based virtual MTB, which integrated different data modalities such as the patient medical history, pathology results and -omics results to identify the most appropriate treatment option.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-driven support to decision-making in molecular tumour boards for lymphoma: A design science approach

et al. 2022

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BackgroundThe increasing amount of molecular data and knowledge about genomic alterations from next-generation sequencing processes together allow for a greater understanding of individual patients, thereby advancing precision medicine. Molecular tumour boards feature multidisciplinary teams of clinical experts who meet to discuss complex individual cancer cases. Preparing the meetings is a manual and time-consuming process.PurposeTo design a clinical decision support system to improve the multimodal data interpretation in molecular tumour board meetings for lymphoma patients at Karolinska University Hospital, Stockholm, Sweden. We investigated user needs and system requirements, explored the employment of artificial intelligence, and evaluated the proposed design with primary stakeholders.MethodsDesign science methodology was used to form and evaluate the proposed artefact. Requirements elicitation was done through a scoping review followed by five semi-structured interviews. We used UML Use Case diagrams to model user interaction and UML Activity diagrams to inform the proposed flow of control in the system. Additionally, we modelled the current and future workflow for MTB meetings and its proposed machine learning pipeline. Interactive sessions with end-users validated the initial requirements based on a fictive patient scenario which helped further refine the system.ResultsThe analysis showed that an interactive secure Web-based information system supporting the preparation of the meeting, multidisciplinary discussions, and clinical decision-making could address the identified requirements. Integrating artificial intelligence via continual learning and multimodal data fusion were identified as crucial elements that could provide accurate diagnosis and treatment recommendations.ImpactOur work is of methodological importance in that using artificial intelligence for molecular tumour boards is novel. We provide a consolidated proof-of-concept system that could support the end-to-end clinical decision-making process and positively and immediately impact patients.ConclusionAugmenting a digital decision support system for molecular tumour boards with retrospective patient material is promising. This generates realistic and constructive material for human learning, and also digital data for continual learning by data-driven artificial intelligence approaches. The latter makes the future system adaptable to human bias, improving adequacy and decision quality over time and over tasks, while building and maintaining a digital log.

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Knowledge bases and software support for variant interpretation in precision oncology

Cited by 15 publications

References 146 publications

Bioinformatics roadmap for therapy selection in cancer genomics

Bioinformatics roadmap for therapy selection in cancer genomics

Transformers and the representation of biomedical background knowledge

Data-driven support to decision-making in molecular tumour boards for lymphoma: A design science approach

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