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 tissues.
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 accuracy.
This study will help the researchers to explore mutations with the best pathogenicity