Characterizing and predicting ccRCC-causing missense mutations in Von Hippel-Lindau disease.

Autor:	Serghini A; School of Chemistry and Molecular Biosciences, Chemistry Building 68, Cooper Road, The University of Queensland, St Lucia, QLD 4072, Queensland, Australia., Portelli S; School of Chemistry and Molecular Biosciences, Chemistry Building 68, Cooper Road, The University of Queensland, St Lucia, QLD 4072, Queensland, Australia., Troadec G; School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3010, Australia., Song C; School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3010, Australia., Pan Q; School of Chemistry and Molecular Biosciences, Chemistry Building 68, Cooper Road, The University of Queensland, St Lucia, QLD 4072, Queensland, Australia.; Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC 3004, Australia., Pires DEV; School of Computing and Information Systems, University of Melbourne, Melbourne, VIC 3010, Australia.; Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC 3004, Australia., Ascher DB; School of Chemistry and Molecular Biosciences, Chemistry Building 68, Cooper Road, The University of Queensland, St Lucia, QLD 4072, Queensland, Australia.; Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC 3004, Australia.
Jazyk:	angličtina
Zdroj:	Human molecular genetics [Hum Mol Genet] 2024 Jan 20; Vol. 33 (3), pp. 224-232.
DOI:	10.1093/hmg/ddad181
Abstrakt:	Background: Mutations within the Von Hippel-Lindau (VHL) tumor suppressor gene are known to cause VHL disease, which is characterized by the formation of cysts and tumors in multiple organs of the body, particularly clear cell renal cell carcinoma (ccRCC). A major challenge in clinical practice is determining tumor risk from a given mutation in the VHL gene. Previous efforts have been hindered by limited available clinical data and technological constraints. Methods: To overcome this, we initially manually curated the largest set of clinically validated VHL mutations to date, enabling a robust assessment of existing predictive tools on an independent test set. Additionally, we comprehensively characterized the effects of mutations within VHL using in silico biophysical tools describing changes in protein stability, dynamics and affinity to binding partners to provide insights into the structure-phenotype relationship. These descriptive properties were used as molecular features for the construction of a machine learning model, designed to predict the risk of ccRCC development as a result of a VHL missense mutation. Results: Analysis of our model showed an accuracy of 0.81 in the identification of ccRCC-causing missense mutations, and a Matthew's Correlation Coefficient of 0.44 on a non-redundant blind test, a significant improvement in comparison to the previous available approaches. Conclusion: This work highlights the power of using protein 3D structure to fully explore the range of molecular and functional consequences of genomic variants. We believe this optimized model will better enable its clinical implementation and assist guiding patient risk stratification and management. (© The Author(s) 2023. Published by Oxford University Press.)
Databáze:	MEDLINE
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