An algorithm for assessing the pathogenicity of genetic mutations in tumor based on a retrospective study of pathogenic and neutral genetic variants

Introduction. Cancer is accounting for 16.8% of all deaths and 22.8% of noncommunicable disease-related deaths, approximately. The diagnostic, prognostic, and therapeutic aspects of patient management majorly depend on mutations that drive the oncogenic process. However, evaluating the clinical significance of the variant is a major challenge, as many of them become variants of unknown significance (VUS).

Aim: of the current study is to create a new algorithm for classification of missense variants.

Material and Methods. Data from the NCBI Assembly, Uniprot, GnomAD, and OncoKB databases was processed with Python 3 to assess oncogenicity, population frequency of missense variants, as well as their occurrence in orthologous sequences. We selected 314 known benign polymorphisms and 332 reported pathogenic mutations of BRCA1, BRCA2, DICER1, PIK3CA, and TP53 genes from the ClinVar database for training and testing datasets.

Results. We have developed the algorithm that provides three criteria based on oncogenicity and population frequency of a variant, as well as its occurrence in orthologous sequences for assessing its potential pathogenicity. A variant was classified as neutral if the following was true: a) a variant doesn’t meet the criterion for oncogenicity; b) a variant meets at least one of the remaining criteria. All other variants were deemed to be pathogenic. The new algorithm demonstrates high sensitivity (94.95% (88.61%, 98.34%)) and specificity (96.52% (91.33%, 99.04%)) in classifying benign and pathogenic variants. The algorithm requires a position of a variant to be represented in population databases and to correspond to an appropriately aligned region in a multiple sequence alignment of orthologs, along with two adjacent positions.

Conclusion. The algorithm might be used to evaluate the variants of other oncogenic genes, possibly making the classification of genetic variants more precise, intensifying molecular diagnostics.

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