Publication detail

A computational workflow for analysis of missense mutations in precision oncology

KHAN, R. POKORNÁ, P. ŠTOURAČ, J. BORKO, S. AREFIEV, I. PLANAS-IGLESIAS, J. DOBIÁŠ, A. PINTO, G. SZOTKOWSKÁ, V. ŠTĚRBA, J. SLABÝ, O. DAMBORSKÝ, J. MAZURENKO, S. BEDNÁŘ, D.

Original Title

A computational workflow for analysis of missense mutations in precision oncology

Type

journal article in Web of Science

Language

English

Original 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.

Keywords

Bioinformatics; Cancer; Function; High-performance computing; Machine learning; Molecular modelling; Oncology; Personalised medicine; Single nucleotide polymorphism; Stability; Treatment

Authors

KHAN, R.; POKORNÁ, P.; ŠTOURAČ, J.; BORKO, S.; AREFIEV, I.; PLANAS-IGLESIAS, J.; DOBIÁŠ, A.; PINTO, G.; SZOTKOWSKÁ, V.; ŠTĚRBA, J.; SLABÝ, O.; DAMBORSKÝ, J.; MAZURENKO, S.; BEDNÁŘ, D.

Released

24. 7. 2024

Publisher

BMC

Location

LONDON

ISBN

1758-2946

Periodical

Journal of Cheminformatics

Year of study

16

Number

1

State

United Kingdom of Great Britain and Northern Ireland

Pages from

1

Pages to

10

Pages count

10

URL

https://jcheminf.biomedcentral.com/articles/10.1186/s13321-024-00876-3

BibTex

@article{BUT197550,
  author="Rayyan {Khan} and Petra {Pokorná} and Jan {Štourač} and Simeon {Borko} and Ihor {Arefiev} and Joan {Planas-Iglesias} and Adam {Dobiáš} and Gaspar P. {Pinto} and Veronika {Szotkowská} and Jaroslav {Štěrba} and Ondřej {Slabý} and Jiří {Damborský} and Stanislav {Mazurenko} and David {Bednář}",
  title="A computational workflow for analysis of missense mutations in precision oncology",
  journal="Journal of Cheminformatics",
  year="2024",
  volume="16",
  number="1",
  pages="10",
  doi="10.1186/s13321-024-00876-3",
  issn="1758-2946",
  url="https://jcheminf.biomedcentral.com/articles/10.1186/s13321-024-00876-3"
}

Responsibility: Ing. Marek Strakoš