%0 Generic
%A Bongers, Brandon
%A Gorostiola Gonzalez, Marina
%A Wang, Xuesong
%A W. T. van Vlijmen, Herman
%A Jespers, Willem
%A Gutiérrez-de-Terán, Hugo
%A Ye, Kai
%A IJzerman, Adriaan P.
%A Heitman, Laura H.
%A van Westen, Gerard
%D 2021
%T Data underlying the article: Pan-cancer in silico analysis of somatic mutations in G-protein coupled receptors: The effect of evolutionary conservation and natural variance
%U https://data.4tu.nl/articles/dataset/Data_underlying_the_article_Pan-cancer_in_silico_analysis_of_somatic_mutations_in_G-protein_coupled_receptors_The_effect_of_evolutionary_conservation_and_natural_variance/15022410/1
%R 10.4121/15022410.v1
%K GDC
%K 1000 Genomes
%K Cancer
%K Mutations
%K Natural variance
%K Pareto optimization
%K Multi-objective
%K GPCR
%X This repository contains the datasets and source code supporting the conclusions of the manuscript "Pan-cancer in silico analysis of somatic mutations in G-protein coupled receptors: The effect of evolutionary conservation and natural variance". G protein-coupled receptors (GPCRs) form the most frequently
exploited drug target family, moreover they are often found mutated in cancer. Here
we used an aggregated dataset of mutations found in cancer patient samples derived
from the Genomic Data Commons and compared it to the natural human variance as
exemplified by data from the 1000 Genomes project. We investigated the location of these
mutations across the protein domains and conserved
residues in GPCRs such as the “DRY” motif. We subsequently created a ranking of
high scoring GPCRs, using a multi-objective approach (Pareto Front Ranking). In conclusion, this study identifies a
list of GPCRs that are prioritized for experimental follow up characterization to
elucidate their role in cancer. The computational approach here described can
be adapted to investigate the roles in cancer of any protein family.<p></p>
%I 4TU.ResearchData