Novel somatic mutations of the cdh1 gene associated with gastric cancer: Prediction of pathogenicity using comprehensive in silico methods

Background: Mutations in the CDH1 and the role of E-cadherin proteins are well established in gastric cancer. Several in silico tools are available to predict the pathogenicity of the mutations present in the genes with varying efficiency and sensitivity to detect the pathogenicity of the mutations. Objective: Our objective was to identify somatic pathogenic variants in CDH1 involved in Gastric Cancer (GC) by Sanger sequencing as well as using in silico tools and to find out the best efficient tool for pathogenicity prediction of somatic missense variants. Methods: Sanger sequencing of CDH1 was done for 80 GC tumor and adjacent normal tis-sues. Synthetic data sets were downloaded from the COSMIC database for comparison of the known mutations with the discovered mutations from the present study. Different algorithms were used to predict the pathogenicity of the discovery and synthetic mutation datasets using various in-silico tools. Statistical analysis was done to check the efficiency of the tools to predict pathogenic variants by using MEDCALC and GraphPad. Results: Six missense somatic variants were found in exons 3, 4, 7, 9, 12 and 15. Out of the 6 variants, 5 variants (chr16:68835618C>A, chr16:68845613A>C, chr16:68847271T>G, chr16:68856001T>G, chr16:68863585G>C) were novel and not reported in disease variant databases. PROVEAN, Polyphen 2 and PANTHER predicted the pathogenicity of the variants more efficiently in both the discovery and synthetic datasets. The overall sensitivity of predictions ranged from 60 to 80%, depending on the program used, with specificity from 55 to 100%. Conclusion: This study estimates the specificity and sensitivity of prediction tools in predicting novel missense variants of CDH1 in Gastric Cancer. We report that PROVEAN, Polyphen 2 and PANTHER are efficient predictors with constant higher specificity and ac-curacy. This study will help the researchers to explore mutations with the best pathogenicity prediction tools.