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.

Základní údaje

Originální název

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

Autoři

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 a David BEDNÁŘ

Vydání

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

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/24:00135293

Organizace

Přírodovědecká fakulta – Masarykova univerzita – Repozitář

EID Scopus

Klíčová slova anglicky

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

Návaznosti

EF17_043/0009632, projekt VaV. LX22NPO5102, projekt VaV. MUNI/A/1395/2022, interní kód Repo. NU20-03-00240, projekt VaV. TN02000109, projekt VaV. 857560, interní kód Repo. CZECRIN IV, velká výzkumná infrastruktura.
Změněno: 11. 6. 2025 00:50, RNDr. Daniel Jakubík

Anotace

V originále

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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