A computational workflow for analysis of missense mutations in precision oncology
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Date
2024-07-24
Authors
Khan, Rayyan
Pokorná, Petra
Štourač, Jan
Borko, Simeon
Arefiev, Ihor
Planas-Iglesias, Joan
Dobiáš, Adam
Pinto, Gaspar P.
Szotkowská, Veronika
Štěrba, Jaroslav
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
BMC
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Abstract
Every year, more than 19 million cancer cases are diagnosed, and this number continues to increase annually. Since standard treatment options have varying success rates for different types of cancer, understanding the biology of an individual's tumour becomes crucial, especially for cases that are difficult to treat. Personalised high-throughput profiling, using next-generation sequencing, allows for a comprehensive examination of biopsy specimens. Furthermore, the widespread use of this technology has generated a wealth of information on cancer-specific gene alterations. However, there exists a significant gap between identified alterations and their proven impact on protein function. Here, we present a bioinformatics pipeline that enables fast analysis of a missense mutation's effect on stability and function in known oncogenic proteins. This pipeline is coupled with a predictor that summarises the outputs of different tools used throughout the pipeline, providing a single probability score, achieving a balanced accuracy above 86%. The pipeline incorporates a virtual screening method to suggest potential FDA/EMA-approved drugs to be considered for treatment. We showcase three case studies to demonstrate the timely utility of this pipeline. To facilitate access and analysis of cancer-related mutations, we have packaged the pipeline as a web server, which is freely available at https://loschmidt.chemi.muni.cz/predictonco/.Scientific contributionThis work presents a novel bioinformatics pipeline that integrates multiple computational tools to predict the effects of missense mutations on proteins of oncological interest. The pipeline uniquely combines fast protein modelling, stability prediction, and evolutionary analysis with virtual drug screening, while offering actionable insights for precision oncology. This comprehensive approach surpasses existing tools by automating the interpretation of mutations and suggesting potential treatments, thereby striving to bridge the gap between sequencing data and clinical application.
Description
Citation
Journal of Cheminformatics. 2024, vol. 16, issue 1, p. 1-10.
https://jcheminf.biomedcentral.com/articles/10.1186/s13321-024-00876-3
https://jcheminf.biomedcentral.com/articles/10.1186/s13321-024-00876-3
Document type
Peer-reviewed
Document version
Published version
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Language of document
en