J 2024

PredictONCO: a web tool supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning

ŠTOURAČ, Jan; Simeon BORKO; Rayyan Tariq KHAN; Petra POKORNÁ; Adam DOBIÁŠ et al.

Basic information

Original name

PredictONCO: a web tool supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning

Authors

ŠTOURAČ, Jan; Simeon BORKO; Rayyan Tariq KHAN; Petra POKORNÁ; Adam DOBIÁŠ; Joan PLANAS IGLESIAS; Stanislav MAZURENKO; José Gaspar RANGEL PAMPLONA PIZARRO PINTO; Veronika SZOTKOWSKÁ; Jaroslav ŠTĚRBA; Ondřej SLABÝ; Jiří DAMBORSKÝ and David BEDNÁŘ

Edition

Briefings in Bioinformatics, OXFORD, Oxford University Press, 2024, 1467-5463

Other information

Language

English

Type of outcome

Article in a journal

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

is not subject to a state or trade secret

References:

Marked to be transferred to RIV

Yes

RIV identification code

RIV/00216224:14310/24:00135293

Organization

Přírodovědecká fakulta – Repository – Repository

EID Scopus

Keywords in English

cancer; oncology; personalized medicine; single-nucleotide polymorphism; targeted therapy

Links

EF17_043/0009632, research and development project. LX22NPO5102, research and development project. MUNI/A/1395/2022, interní kód Repo. NU20-03-00240, research and development project. TN02000109, research and development project. 857560, interní kód Repo. CZECRIN IV, large research infrastructures.
Changed: 11/6/2025 00:50, RNDr. Daniel Jakubík

Abstract

In the original language

PredictONCO 1.0 is a unique web server that analyzes effects of mutations on proteins frequently altered in various cancer types. The server can assess the impact of mutations on the protein sequential and structural properties and apply a virtual screening to identify potential inhibitors that could be used as a highly individualized therapeutic approach, possibly based on the drug repurposing. PredictONCO integrates predictive algorithms and state-of-the-art computational tools combined with information from established databases. The user interface was carefully designed for the target specialists in precision oncology, molecular pathology, clinical genetics and clinical sciences. The tool summarizes the effect of the mutation on protein stability and function and currently covers 44 common oncological targets. The binding affinities of Food and Drug Administration/ European Medicines Agency -approved drugs with the wild-type and mutant proteins are calculated to facilitate treatment decisions. The reliability of predictions was confirmed against 108 clinically validated mutations. The server provides a fast and compact output, ideal for the often time-sensitive decision-making process in oncology. Three use cases of missense mutations, (i) K22A in cyclin-dependent kinase 4 identified in melanoma, (ii) E1197K mutation in anaplastic lymphoma kinase 4 identified in lung carcinoma and (iii) V765A mutation in epidermal growth factor receptor in a patient with congenital mismatch repair deficiency highlight how the tool can increase levels of confidence regarding the pathogenicity of the variants and identify the most effective inhibitors. The server is available at https://loschmidt.chemi.muni.cz/predictonco.

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