Analysis of mutations in precision oncology using the automated, accurate, and user-friendly web tool PredictONCO
Next-generation sequencing technology has created many new opportunities for clinical diagnostics, but it faces the challenge of functional annotation of identified mutations. Various algorithms have been developed to predict the impact of missense variants that influence oncogenic drivers. However,...
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Elsevier
2024-12-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037024003982 |
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| author | Rayyan Tariq Khan Petra Pokorna Jan Stourac Simeon Borko Adam Dobias Joan Planas-Iglesias Stanislav Mazurenko Ihor Arefiev Gaspar Pinto Veronika Szotkowska Jaroslav Sterba Jiri Damborsky Ondrej Slaby David Bednar |
| author_facet | Rayyan Tariq Khan Petra Pokorna Jan Stourac Simeon Borko Adam Dobias Joan Planas-Iglesias Stanislav Mazurenko Ihor Arefiev Gaspar Pinto Veronika Szotkowska Jaroslav Sterba Jiri Damborsky Ondrej Slaby David Bednar |
| author_sort | Rayyan Tariq Khan |
| collection | DOAJ |
| description | Next-generation sequencing technology has created many new opportunities for clinical diagnostics, but it faces the challenge of functional annotation of identified mutations. Various algorithms have been developed to predict the impact of missense variants that influence oncogenic drivers. However, computational pipelines that handle biological data must integrate multiple software tools, which can add complexity and hinder non-specialist users from accessing the pipeline. Here, we have developed an online user-friendly web server tool PredictONCO that is fully automated and has a low barrier to access. The tool models the structure of the mutant protein in the first step. Next, it calculates the protein stability change, pocket level information, evolutionary conservation, and changes in ionisation of catalytic amino acid residues, and uses them as the features in the machine-learning predictor. The XGBoost-based predictor was validated on an independent subset of held-out data, demonstrating areas under the receiver operating characteristic curve (ROC) of 0.97 and 0.94, and the average precision from the precision-recall curve of 0.99 and 0.94 for structure-based and sequence-based predictions, respectively. Finally, PredictONCO calculates the docking results of small molecules approved by regulatory authorities. We demonstrate the applicability of the tool by presenting its usage for variants in two cancer-associated proteins, cellular tumour antigen p53 and fibroblast growth factor receptor FGFR1. Our free web tool will assist with the interpretation of data from next-generation sequencing and navigate treatment strategies in clinical oncology: https://loschmidt.chemi.muni.cz/predictonco/. |
| format | Article |
| id | doaj-art-3d4ec3367bee4e13870e203cd22157b5 |
| institution | OA Journals |
| issn | 2001-0370 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-3d4ec3367bee4e13870e203cd22157b52025-08-20T01:58:08ZengElsevierComputational and Structural Biotechnology Journal2001-03702024-12-012473473810.1016/j.csbj.2024.11.026Analysis of mutations in precision oncology using the automated, accurate, and user-friendly web tool PredictONCORayyan Tariq Khan0Petra Pokorna1Jan Stourac2Simeon Borko3Adam Dobias4Joan Planas-Iglesias5Stanislav Mazurenko6Ihor Arefiev7Gaspar Pinto8Veronika Szotkowska9Jaroslav Sterba10Jiri Damborsky11Ondrej Slaby12David Bednar13Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic; International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech RepublicDepartment of Biology, Faculty of Medicine and Central European Institute of Technology, Masaryk University, Brno, Czech Republic; Center for Precision Medicine, University Hospital Brno, Brno, Czech RepublicLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic; International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech RepublicLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic; International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech Republic; IT4Innovations Centre of Excellence, Faculty of Information Technology, Brno University of Technology, Brno, Czech RepublicLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech RepublicLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic; International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech RepublicLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic; International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech RepublicLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech RepublicLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic; International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech RepublicLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech RepublicCenter for Precision Medicine, University Hospital Brno, Brno, Czech Republic; Department of Paediatric Oncology, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech RepublicLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic; International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech RepublicDepartment of Biology, Faculty of Medicine and Central European Institute of Technology, Masaryk University, Brno, Czech Republic; Center for Precision Medicine, University Hospital Brno, Brno, Czech Republic; Corresponding author at: Department of Biology, Faculty of Medicine and Central European Institute of Technology, Masaryk University, Brno, Czech Republic.Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic; International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech Republic; Corresponding author at: Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic.Next-generation sequencing technology has created many new opportunities for clinical diagnostics, but it faces the challenge of functional annotation of identified mutations. Various algorithms have been developed to predict the impact of missense variants that influence oncogenic drivers. However, computational pipelines that handle biological data must integrate multiple software tools, which can add complexity and hinder non-specialist users from accessing the pipeline. Here, we have developed an online user-friendly web server tool PredictONCO that is fully automated and has a low barrier to access. The tool models the structure of the mutant protein in the first step. Next, it calculates the protein stability change, pocket level information, evolutionary conservation, and changes in ionisation of catalytic amino acid residues, and uses them as the features in the machine-learning predictor. The XGBoost-based predictor was validated on an independent subset of held-out data, demonstrating areas under the receiver operating characteristic curve (ROC) of 0.97 and 0.94, and the average precision from the precision-recall curve of 0.99 and 0.94 for structure-based and sequence-based predictions, respectively. Finally, PredictONCO calculates the docking results of small molecules approved by regulatory authorities. We demonstrate the applicability of the tool by presenting its usage for variants in two cancer-associated proteins, cellular tumour antigen p53 and fibroblast growth factor receptor FGFR1. Our free web tool will assist with the interpretation of data from next-generation sequencing and navigate treatment strategies in clinical oncology: https://loschmidt.chemi.muni.cz/predictonco/.http://www.sciencedirect.com/science/article/pii/S2001037024003982Precision oncologyWebserverMutationPredictionTreatmentNext-generation sequencing |
| spellingShingle | Rayyan Tariq Khan Petra Pokorna Jan Stourac Simeon Borko Adam Dobias Joan Planas-Iglesias Stanislav Mazurenko Ihor Arefiev Gaspar Pinto Veronika Szotkowska Jaroslav Sterba Jiri Damborsky Ondrej Slaby David Bednar Analysis of mutations in precision oncology using the automated, accurate, and user-friendly web tool PredictONCO Computational and Structural Biotechnology Journal Precision oncology Webserver Mutation Prediction Treatment Next-generation sequencing |
| title | Analysis of mutations in precision oncology using the automated, accurate, and user-friendly web tool PredictONCO |
| title_full | Analysis of mutations in precision oncology using the automated, accurate, and user-friendly web tool PredictONCO |
| title_fullStr | Analysis of mutations in precision oncology using the automated, accurate, and user-friendly web tool PredictONCO |
| title_full_unstemmed | Analysis of mutations in precision oncology using the automated, accurate, and user-friendly web tool PredictONCO |
| title_short | Analysis of mutations in precision oncology using the automated, accurate, and user-friendly web tool PredictONCO |
| title_sort | analysis of mutations in precision oncology using the automated accurate and user friendly web tool predictonco |
| topic | Precision oncology Webserver Mutation Prediction Treatment Next-generation sequencing |
| url | http://www.sciencedirect.com/science/article/pii/S2001037024003982 |
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