Detail publikace

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.

Originální název

A computational workflow for analysis of missense mutations in precision oncology

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

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.

Vydáno

24. 7. 2024

Nakladatel

BMC

Místo

LONDON

ISSN

1758-2946

Periodikum

Journal of Cheminformatics

Ročník

16

Číslo

1

Stát

Spojené království Velké Británie a Severního Irska

Strany od

1

Strany do

10

Strany počet

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"
}

Odpovědnost: Ing. Marek Strakoš