Large-scale pathogenicity prediction analysis of cancer-associated kinase mutations reveals variability in sensitivity and specificity of computational methods.

ABSTRACT:

Background

Mutations in kinases are the most frequent genetic alterations in cancer; however, experimental evidence establishing their cancerous nature is available only for a small fraction of these mutants.

Aims

Predicition analysis of kinome mutations is the primary aim of this study. Further objective is to compare the performance of various softwares in pathogenicity prediction of kinase mutations.

Materials and methods

We employed a set of computational tools to predict the pathogenicity of over forty-two thousand mutations and deposited the kinase-wise data in Mendeley database (Estimated Pathogenicity of Kinase Mutants [EPKiMu]).

Results

Mutations are more likely to be drivers when being present in the kinase domain (vs. non-kinase domain) and belonging

SUBMITTER: Akula S 

PROVIDER: S-EPMC10501281 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature